Search

Introduction. This paper examines regulations which govern procedures for reporting incidents other than accidents or serious incidents related to unmanned aircraft system (UAS) operations. The regulations are discussed in the context of available data and the paper included an analysis of them from both a European and national perspective. The goal of the paper is to provide a series of recommendations with regard to the procedures for reporting and analyzing UAS incidents in order to improve the safe integration of unmanned and manned aviation. This article also explores the legal consequences that arise from the midair collision between a UAS and a manned aircraft. Material and methods: The method of study comprises a content analysis of existing legislations. The current doctrine was confronted with existing regulations, documents and materials. Results: The results of the study show that there is a practical problem of objectively identifying operators of a UAS as well as in defining what exactly constitutes an “incident”. It can be reasonably concluded that reporting and analyzing UAS-related incidents allows for the assessment and development of strategies for integrating manned and unmanned aviation. It is worth mentioning that drones and UAS technology requires refinement, especially in technological terms. It is reasonable to take action aimed at raising awareness amongst UAS users of the need to report incidents, as well as engaging UAS users in the investigative process which follows such occurrences.

Coal moisture content monitoring plays an important role in carbon reduction and clean energy decisions of coal transportation-storage aspects. Traditional coal moisture content detection mechanisms rely heavily on detection equipment, which can be expensive or difficult to deploy under field conditions. To achieve fast prediction of coal moisture content, a novel neural network model based on attention mechanism and bidirectional ResNet-LSTM structure (ABRM) is proposed in this paper. The prediction of coal moisture content is achieved by training the model to learn the relationship between changes of coal moisture content and meteorological conditions. The experimental results show that the proposed method has superior performance in terms of moisture content prediction accuracy compared with other state-of-the-art methods, and that ABRM model approaches appear to have the greatest potential for predicting coal moisture content shifts in the face of meteorological elements.

In this paper, we present a Decomposition Coordination (DC) method applied to solve the problem of safe trajectory planning for autonomous Unmanned Aerial Vehicle (UAV) in a dynamic environment. The purpose of this study is to make the UAV more reactive in the environment and ensure the safety and optimality of the computed trajectory. In this implementation, we begin by selecting a dynamic model of a fixed-arms quadrotor UAV. Then, we define our multi-objective optimization problem, which we convert afterward into a scalar optimization problem (SOP). The SOP is subdivided after that into smaller sub-problems, which will be treated in parallel and in a reasonable time. The DC principle employed in our method allows us to treat non-linearity at the local level. The coordination between the two levels is achieved after that through the Lagrange multipliers. Making use of the DC method, we can compute the optimal trajectory from the UAV’s current position to a final target practically in real-time. In this approach, we suppose that the environment is totally supervised by a Ground Control Unit (GCU). To ensure the safety of the trajectory, we consider a wireless communication network over which the UAV may communicate with the GCU and get the necessary information about environmental changes, allowing for successful collision avoidance during the flight until the intended goal is safely attained. The analysis of the DC algorithm’s stability and convergence, as well as the simulation results, are provided to demonstrate the advantages of our method and validate its potential.

The events of recent weeks have reminded us how difficult it is to fight the element of fire. Knowing where the fire has started allows emergency services to react faster and more efficiently. That is why unmanned aerial vehicles are being used more and more often. A drone with a thermal imaging camera locates fire outbreaks from the air. The drone will not only reach places that are difficult to reach, but also record an image from a wide area and—importantly—allow you to assess the extent of damage and fire extinguishing activities in real time. The unmanned aerial vehicle with a thermal imaging camera installed, despite the clouds of smoke and poor visibility, registers where the fire is located and in which direction the fire is spreading. The obtained data, along with the exact coordinates of the places that are on fire, are forwarded to the fire brigade. On this basis, firefighters can react quickly and move to a specific area to extinguish a fire. The analysis of the temperature of the indicated area also translated into the safety of the services participating in the fire extinguishing operation. This refers to issues in the area of public safety and privacy. UAV, under the international, regional and national regime of aviation law, are considered as aircraft. Since there is no framework existing in connection with the unauthorized flight in operations area of fire services in the context of the possibility of the U-space using, this paper will focus on this analysis. The paper’s conclusion will address the proposed recommendations for the UAV and aviation industries to consider as potential solution- implementation of the U-space concept. This idea, apart from the regulations of aviation law and other branches of law seems to be relevant for sanctioning the use of the airspace in fire services application.

A common issue with many commercial rehabilitative exoskeletons and orthoses are that they can be prohibitively expensive for an average individual to afford without additional financial support. Due to this a user may have limited to the usage of such devices within set rehabilitation sessions as opposed to a continual usage. The purpose of this review is therefore to find which actuator implementations would be most suitable for a simplistic, low-cost powered orthoses capable of assisting those with pathologic gait disorders by collating literature from Web of Science, Scopus, and Grey Literature. In this systematic review paper 127 papers were selected from these databases via the PRISMA guidelines, with the financial costs of 25 actuators discovered with 11 distinct actuator groups identified. The review paper will consider a variety of actuator implementations used in existing lower-limb exoskeletons that are specifically designed for the purpose of rehabilitating or aiding those with conditions inhibiting natural movement abilities, such as electric motors, hydraulics, pneumatics, cable-driven actuators, and compliant actuators. Key attributes such as technical simplicity, financial cost, power efficiency, size limitations, accuracy, and reliability are compared for all actuator groups. Statistical findings show that rotary electric motors (which are the most common actuator type within collated literature) and compliant actuators (such as elastic and springs) would be the most suitable actuators for a low-cost implementation. From these results, a possible actuator design will be proposed making use of both rotary electric motors and compliant actuators.The online version contains supplementary material available at 10.1007/s10846-022-01695-0.

Over the past decade, unmanned aerial vehicles (UAVs) have received a significant attention due to their diverse capabilities for non-combatant and military applications. The primary aim of this study is to unveil a clear categorization overview for more than a decade worth of substantial progress in UAVs. The paper will begin with a general overview of the advancements, followed by an up-to-date explanation of the different mechanical structures and technical elements that have been included. The paper will then explore and examine various vertical take-off and landing (VTOL) configurations, followed by expressing the dynamics, applicable simulation tools and control strategies for a Quadrotor. In conclusion to this review, the dynamic system presented will always face limitations such as internal and/or external disturbances. Hence, this can be minimised by the choice of introducing appropriate control techniques or mechanical enhancements.

The use of autonomous robots in areas that require executing a broad range of different tasks is currently hampered by the high complexity of the software that adapts the robot controller to different situations the robot would face. Current robot software frameworks facilitate implementing controllers for individual tasks with some variability, however, their possibilities for adapting the controllers at runtime are very limited and don’t scale with the requirements of a highly versatile autonomous robot. With the software presented in this paper, the behavior of robots is implemented modularly by composing individual controllers, between which it is possible to switch freely at runtime, since the required transitions are calculated automatically. Thereby the software developer is relieved of the task to manually implement and maintain the transitions between different operational modes of the robot, what largely reduces software complexity for larger amounts of different robot behaviors. The software is realized by a model-based development approach. We will present the metamodels enabling the modeling of the controllers as well as the runtime architecture for the management of the controllers on distributed computation hardware. Furthermore, this paper introduces an algorithm that calculates the transitions between two controllers. A series of technical experiments verifies the choice of the underlying middleware and the performance of online controller reconfiguration. A further experiment demonstrates the applicability of the approach to real robotics applications.

Path planning of autonomous mobile robots in a real-world environment presents several challenges which are usually not raised in other areas. The real world is inherently complex, uncertain and dynamic. Therefore, accurate models of path planning are difficult to obtain and quickly become outdated. Anytime planners are ideal for this type of problem as they can find an initial solution very quickly and then improve it as time allows. This paper proposes a new anytime incremental search algorithm named improved Anytime Dynamic A*(iADA*). The algorithm is based on the currently popular anytime heuristic search algorithm, which is Anytime Dynamic A*(ADA*). The iADA* algorithm improves the calculation of the path lengths and decreases the calculating frequency of the path throughout the search, making it significantly faster. The algorithm is designed to provide an efficient solution to a complex, dynamic search environment when the locally changes affected. Our study shows that the two-dimensional path-planning iADA* experiments were between 2.0 to 3.7 times faster than ADA*, both in partially known and fully unknown dynamic environments. Additionally, in this paper shows the experiment results of the comparison with other four existing algorithms based on computing time and path lengths. iADA* was an average 2.57 times reduced on the computational time for the environment which locally changes effected. For the path length is little increase, but it is not the worst case. According to the experiments, the more the environmental problems and complexity increases, the more iADA* provides a rapid in-search time and total time to obtain the final solution.

A cluster consensus algorithm for nonlinear multi-agent systems under directed graph topology is proposed in this paper. Cluster consensus is the convergence of states/outputs of agents in the same cluster to consistent values which are different from those of other clusters. Cluster consensus has been obtained based on Lyapunov stability and matrix theory in terms of some sufficient conditions. A feedback control law is provided using Linear Matrix Inequality (LMI) to achieve cluster consensus for multi-agent systems. Moreover, cluster consensus for nonlinear multi-agent systems in the presence of time delay has been studied in this paper. Finally, simulation results are presented for different number of clusters to validate theoretical analysis. Examples are provided for first-order and second-order and also general high-order systems. Furthermore, first-order system with time delay is simulated for a single-link flexible joint manipulator.

The intelligent drone is the key device of the future Internet of Drone, and its safe and robust flight in complicated environments still faces challenges. In this paper, we present a sampling-based kinodynamic planning algorithm for quadrotors, which plans a dynamically feasible trajectory in a complicated environment. We have designed a method to constrain the sampling state by using the vector field to construct a cone in the sampling stage of RRT*, so that the generated trajectory is connected as smoothly as possible to other states in the reachable set. The motion primitives are then generated by solving an optimal control problem and an explicit solution of the optimal duration for the motion primitives is given to optimally connect any pair of states. In addition, we have tried a new method to determine the neighbor radius for the non-Euclidean metrics of this paper. Finally, the planned trajectory is applied to the simulated quadrotor, which verifies the dynamic feasibility of the trajectory. Simulation results show that compared with the existing kinodynamic RRT* under the same number of iterations, the proposed algorithm explores more states with a shorter execution time and generates a smoother trajectory.

The paper deals with the legal facets of the Single European Sky strategy and the reform of today’s European air traffic management network for Unmanned Aerial Systems (UAS). The aim of this strategy is to guarantee standards on safety, efficiency and environmental impact of the air traffic, so as to let drones progressively sharing it. The paper scrutinizes the ways in which the European Commission and European Aviation Safety Agency (EASA) intend to attain this end by September 2023. In addition, the paper sheds light on some of the troubles with this roadmap. First, it is all about the clarification and implementation of the regulations and guidelines set up by both EASA and the Commission. Second, problems of coordination persist among different regulatory authorities in multiple legal domains, e.g. the interaction between civil aviation law authorities and data protection authorities. Third, most solutions on a risk-based regulation, markets integration, and the design of law-abiding drones should involve the UAS sector industries. The latter are an essential factor for the adoption of new technological standards and still, the definition of such standards brings us back to the coordination problems mentioned above. Whether or not these problems on rules, standards and coordination for the governance of law-abiding drones will obstruct the agenda for a Single European Sky remains an open question. But, it seems fair to admit such problems will increasingly attract the attention of scholars over the next years.

This paper focuses on the problem of transporting a cable-suspended load by a quadrotor UAV for safer flight and more efficiency. The dynamic model of a quadrotor coupled to the suspended load is derived using the Euler-Lagrange formulation. The optimal trajectory for carrying the maximum payload and minimum oscillation of swinging load will be obtained. The optimal cable length to increase the maximum payload capacity and reduce the maximum oscillation angle of swinging load is obtained. Also, the effect of load mass on the maximum oscillation angle of swinging load is studied. In this paper, the optimization procedure is based on the solution of the optimal control problem from the class of open loop with an indirect method. The application Pontryagin’s Minimum Principle lead to deriving the optimality conditions and subsequently a two-point boundary value problem (TPBVP) which is solved by a numerical method. An appropriate algorithm is presented for calculating the maximum payload to move between two specified points. The main superiority of this method is that it can solve a wide range of optimal maneuvers for arbitrary initial and final configurations relevant to every considered cost function. Generating various optimal paths with different maximum payloads and oscillation angles by modifying the values of the penalty matrices. In order to verify the efficiency of the proposed method and the presented algorithm, a simulation study is performed for a quadrotor with a suspended load in maneuver between two specified points and various object function.

In research on providing motion assistance for elderly care, the robot walking helper is considered to be able to maintain their vitality. For its practicality, one issue of interest is its feasible path planning for guidance. Inspired by the concept of including human factors for path planning previously proposed, in this paper, we develop such a motion guidance system for the robot walking helper. We first selected the human factors most vital for the elderly and also public via an Internet survey, and then developed a corresponding path planning algorithm and control strategy for its realization. Experiments are conducted to demonstrate the effectiveness of the proposed system. Key contributions of the paper lie on (a) one of the few studies that include human factors into path planning of the robot walking helper and (b) a more thorough consideration on comfort with both physical and psychological factors corresponding to elderly and public preference included.

In this paper, an optimal nonlinear control scheme based on the application of ant colony optimization (ACO) is applied to a micro-robot equipped with vibratory actuators. Accordingly, two small vibrating motors are utilized to run the micro-robot and the motion principle of stick-slip is used for locomotion purpose. First, a dynamic model of the micro-robot is derived considering the stiffness of the robot’s legs. Then, the influences of robot mass and length of legs on micro-robot motion are studied using simulation. Next, an optimal linear PID control scheme is applied to the micro-robot system. However, it is found that this control method does not have an acceptable performance when friction is low or the system is under disturbance. Consequently, an ACO-based optimal nonlinear PID control is proposed to cope with the mentioned drawbacks as the main contribution of the paper. Afterwards, the performance of both control techniques is compared through simulation. Finally, the micro-robot is developed and experimentally evaluated. It is found that the experimental results are in a good agreement with some of the simulation outcomes through which the validity of the mathematical scheme as well as the feasibility of design is affirmed.

Poland was one of the first European countries to adopt a national regulatory framework for the operation of drones. During its first years (2013–2016), the national regulator was more focused on VLOS operations, and BVLOS operations were possible only in segregated airspace. Since 2019, a new law has allowed for certain types of BVLOS operations to be conducted outside of segregated airspace, at very low levels. This paper will analyze how national legislation is dealing with this new technology, focusing on the new law on BVLOS operations. As the national regulation will be replaced in June 2020 by common European rules that have been adopted 2019, this paper also aims to highlight the most important provisions of the EU regulation.

This paper proposes a robust model predictive control (MPC) strategy for the trajectory tracking control of a four-mecanum-wheeled omni-directional mobile robot (FM-OMR) under various constraints. The method proposed in this paper can solve various constraints while implementing trajectory tracking of the FM-OMR. Firstly, a kinematics model with constraint relationship of the FM-OMR is established. On the basis of the kinematics model, the kinematics trajectory tracking error model of the FM-OMR is further formulated. Then, it is transformed into a constrained quadratic programming(QP) problem by the method of MPC. In addition, aiming at the speed deficiencies of conventional neural networks in QP solving, a delayed neural network (DNN) is applied to solve the optimal solution of the QP problem, and compared with the Lagrange programming neural network (LPNN) to show the rapidity of the DNN. Finally, two simulation cases considering bounded random disturbance are provided to verify the robustness and effectiveness of the proposed method. Theoretical analysis and simulation results show that the control strategy is effective and feasible.

This paper presents a lightweight 3D vision system called Equal Baseline Camera Array (EBCA). EBCA can work in different light conditions and it can be applied for measuring large range of distances. The system is a useful alternative to other known distance measuring devices such as structured-light 3D scanners, time-of-flight cameras, Light Detection and Ranging (LIDAR) devices and structure from motion techniques. EBCA can be mounted on a robotic arm without putting significant load on its construction. EBCA consists of a central camera and a ring of side cameras. The system uses stereo matching algorithms to acquire disparity maps and depth maps similarly as in case of using stereo cameras. This paper introduces methods of adapting stereo matching algorithms designed for stereo cameras to EBCA. The paper also presents the analysis of local, semi-global and global stereo matching algorithms in the context of the EBCA usage. Experiments show that, on average, results obtained from EBCA contain 37.49% less errors than the results acquired from a single stereo camera used in the same conditions.

Autonomous underwater vehicles (AUVs) have important applications in several areas such as data collection, geological survey and exploration in underwater environment. The hydrodynamic forces acting on AUVs are highly nonlinear and the higher-order hydrodynamic coefficients should be taken into account to represent these nonlinear behaviors. Therefore, developing a suitable dynamic model of the AUV and also an accurate knowledge about the hydrodynamic parameters are important for proper design of the navigation and control system. The main goal of the present paper is to investigate the role of proper dynamic model for an AUV which includes different hydrodynamic stability derivatives coefficients. Design of proper actuator is also significant for the global performance of the system and the expenses of the plan. It is tried in the present paper to investigate the effect of full hydrodynamic coefficients on the design of actuators and control system performance analysis for an AUV which was ignored in the previous works. For this purpose, the effect of these parameters on the open loop characteristics of the AUV, its role in the control system design and the interaction of control system and actuator dynamics are considered in nonlinear time domain and frequency domain. It is shown that in a full hydrodynamic consideration for design of the AUV, the effect of control gains, system bandwidth and damping can be different in comparison with the cases which are not fully considered them. Finally, the design of optimum actuator and the effect of actuator natural frequency on the flight performance in the presence of full hydrodynamic coefficients are studied.

This paper focuses on localization and mapping issues for autonomous mobile robots equipped with low-cost 2D lidar in complex environments. Most existing solutions commonly parameterize the robot pose on SE(3) when the robot moves on the rough ground and uses the scan data that may be insufficient or sparse to build the map. In this paper, we first developed the Gaussian Process (GP) to address insufficient scan data for low-precision 2D lidar by enriching the lidar measurements at interest or specific bearing regions. Meanwhile, A new method, based on the graph optimization framework, to solve the non-SE(2) perturbations is proposed, namely SE2-3D constraint, which directly parameterizes the robot pose as SE(2) without ignoring the non-SE(2) perturbations by associating the extended SE(2) pose with map point via lidar measurements. The experimental results indicate that the raw lidar data processed by our method can generate higher quality maps than the original data under the same working conditions. The simulation results verify that the proposed method has higher performance in terms of accuracy than traditional methods. This paper provides a meaningful solution for the broad application of ground mobile robots equipped with low-cost 2D lidar.

This work deals with a unicycle-like mobile platform pushing one and two trailers connected to it, mimicking an application in precision agriculture. The idea is to have a tractor vehicle pushing or pulling passive trailers with materials to be moved accordingly. The first part corresponds to the development of a suitable model for the whole underactuated system, the articulated chain, considering that a point of interest in the last trailer should follow a specified path. Thus, as the trailers are passive subsystems, the way a trailer is connected to the mobile robot or another trailer allows transferring velocities, even when the two bodies have different heading angles. Therefore, the first contribution of the paper is to model these relations, considering a robot pushing one or two trailers. After getting a suitable model for the whole system, two controllers were developed, one based only on inverse kinematics, and the other adding a compensation of the robot dynamics to the kinematic controller, in this case building a cascade or inner-outer loop control structure. The objective behind the design of these two controllers is to compare their performance when guiding the multi-articulated vehicle to accomplish a path-following task, aiming at selecting the best of these two controllers. Finally, experiments run are discussed, whose results validate the proposed model and control system, besides allowing concluding that the best option is to adopt the kinematic controller plus the dynamic compensation, mainly for higher velocities, which is the main contribution of the paper.

For a moving robot based on visual Simultaneous Localization and Mapping (SLAM), blurred images will degrade the accuracy of localization. Therefore, how to handle blurred images is a main problem in visual SLAM. In order to decrease the influence of blurred images on localization accuracy, this paper proposes an improved visual SLAM, which is based on Haar wavelet transform and has the ability of eliminating blurred images. Besides, a correlation-weighted pose optimization is also developed in this paper. This weighted optimization integrates the correlation between matching features as weighting coefficients into the reprojection errors. In this weighted method, pose optimization algorithm can reduce the influence of the matching features with low correlation, which are more likely to be mismatched. As a result, the accuracy of the estimated pose will be improved. The improved system optimized by our method is evaluated on the TUM RGB-D dataset and real environment. It is also compared with other optimization systems, which were based on blurred image elimination and uncertainty-weighted optimization respectively. The experimental results demonstrate that the system optimized by our method could achieve the highest accuracy and robustness in pose estimation.

Rotation estimation is important for localization, mapping and navigation in robotic applications. A novel rotation estimation method for geometric vision of omni-directional cameras is proposed in this paper. Firstly, we formulate the rotation estimation as a sinusoid fitting problem on the basis of column-wise and row-wise shifts in omni-directional images. Then the method is implemented in two steps: motion vector extraction and sinusoid fitting. We are extracting motion vectors by finding pixel deviations in row- and column-directions as well as image rotation for each sub-image by Fourier-Mellin transform (FMT) or by optical flow. Then we fit these column-wise motion vectors to two sinusoid functions. Due to sharing the optimization variables, column-wise shifts and image rotation of each motion vector can be optimized jointly. Afterwards, the 3D rotation between two frames is obtained from the offsets, amplitudes and phase-shifts of the two sinusoid functions. Finally, we perform experiments for 3D rotation, which show that our algorithm outperforms the geometry-based methods in accuracy, robustness and speed. Compared to our early version, this paper makes several improvements: 1) modeling both pixel shifts and sub-regions’ rotation to the sinusoid fitting problem, instead of only pixel shifts; 2) exploiting optical flow to extract motion vectors in addition to FMT; 3) comparing with a geometry-based method for pure rotation as well as five-point algorithms.

In the paper, a family of novel real-time tuning methods for an unmanned aerial vehicle (UAV) altitude controller in in-flight conditions. The methods allow the controller’s gains to be adapted only on the basis of measurements from a basic sensory equipment and by constructing the optimization cost function in an on-line fashion with virtually no impeding computational complexity; in the case of the altitude controller as in this paper for a hexacopter, altitude measurements were used only. The methods are not dependent on the measurement level, and present the approach in a generally applicable form to tuning arbitrary controllers with low number of parameters. Real-world experimental flights, preceded by simulation tests, have shown which method should behave best in a noisy environment when e.g. wind disturbances act on a UAV while it is in autonomous flight. As the methods can potentially be extended to other control loops or controller types, making this a versatile, rapid-tuning tool. It has been shown that a well-tuned controller using the proposed AL-TUNE scheme outperforms controllers that are tuned just to stabilize the system. AL-TUNE provides a new way of using UAVs in terms of adaptivity to changing their dynamic properties and can be deployed rapidly. This enables new applications and extends the usability of fully autonomous UAVs, unlike other tuning methods, which basically require the availability of a UAV model. The core difference with respect to other research from the field is that other authors either use a model of a UAV to optimize the gains analytically or use machine learning techniques, what increases time consumption, whereas the presented methods offer a rapid way to tune controllers, in a reliable way, with deterministic time requirements.

This paper addresses the issue of cooperative control concerning the output synchronization of the linear time-invariant multi-input multi-output multi-agent system i.e., multi-unmanned aerial vehicle (a swarm of the drone), by designing observer-based consensus protocol with directed communication topology. Considering the leader following consensus tracking problem, an observer-based protocol is designed to obtain desirable output synchronization. The protocol design consists of a local observer for the leader drone and a distributed observer for the follower drones. Observer gains are optimized through a multi-step algorithm based on linear quadratic regulator controller and linear matrix inequalities. Most of the existing literature on cooperative control considers full state information for the controller as well as observer design. However, the protocol for the controller and observer design for consensus tracking of multi-unmanned aerial vehicles presented in this paper considers that only the relative partial states information from the corresponding neighboring drones are utilized. The designed algorithm is robust and guarantees that all the states of follower drones track the states of the leader drone and the consensus is achieved asymptotically even in the presence of external disturbances. Finally, considering the ideal and perturbed system, the efficacy of the analytical results are illustrated by comparative analysis using numerical simulations.

The unmanned robotic excavator system has recently become one solution for rescue at the disaster site. Almost all of the solutions about excavator automation have used the electric valve controller and pressure sensor through the mechanical modifications since they are essential. However, some problems might occur because of unpredictable cost and time consumption in modifying the hydraulic drive and mechanical system. In contrast, this paper proposes a completely novel approach for regulation control of any typical hydraulic excavator by excluding those devices which is related to hydraulic drive system. This method controls the lever displacements physically by using the detachable devices and embedded system for avoiding an any modifications and complex computation. Applying the time delay control (TDC) method, this paper demonstrates the globally stable of regulation control of an excavator in the joint space even though states about the hydraulic systems and body dynamics are not observed. It is expected that this method could help the user to safe respond to requirements in emergencies through the rapid robotization without modifications.

In this paper (A preliminary version of the submitted paper appeared in the Proceedings of the 2020 International Conference on Unmanned Aircraft Systems (ICUAS’20), Athens, Greece*), a robust attitude controller is proposed for a small-scale helicopter based on Multi-Variable Super Twisting Algorithm. An interconnected hybrid model of a helicopter is considered consisting of fuselage and rotor which are modeled as a single rigid body and a disc, respectively. For designing a globally defined controller, we consider that attitude dynamics evolves on non-Euclidean space. We assume that the rotor is subjected to unknown disturbances and exact information of the helicopter parameters are unavailable due to parametric uncertainty. A attitude tracking controller using Super Twisting Algorithm based on Second-Order sliding mode technique is proposed to ensure robustness in the presence of both unknown disturbance and parameter variation. A family of strong Lyapunov functions is used to show that the proposed controller drives the closed loop system to the sliding manifold in finite time. For the suitable choice of gain matrices, the reduced order error dynamics becomes almost globally asymptotically stable. We further show that the reduced order error dynamics is almost semi-globally exponentially stable if its trajectories starts from a specific subset. The rate of convergence can be adjusted as required by tuning the controller gains. Numerical simulations are carried out by comparing the proposed controller with the state-of-the art controllers available in the literature.

In recent years, UASs have become more and more popular. There are many reasons for this; one of the most popular is the enhancement of drones’ functionalities and improvement in battery life, stabilization, navigation, sensor technology, and much more. Many benefits drive growth. However, as the number of drones is expanding and their technological functionalities are evolving, drones’ use brings many concerns and challenges that should not be underestimated. This refers to issues in the area of cybersecurity, privacy, and public safety. UASs, under the international, regional, and national regime of aviation law, are considered aircraft. Since there is no existing cybersecurity framework specific to the UASs, the civil aviation cybersecurity framework should apply to their operations. This paper will focus on the potential cyber threats against UASs, providing examples of cyber-attacks from the past. Further, the aviation cybersecurity framework’s overview will follow to determine the current status of maturity at the international, regional (the European Union), and national (the Republic of Poland) levels. The paper’s conclusion will address the proposed recommendations for the UAS and aviation industries to be considered and potential solutions in terms of applying the aviation cybersecurity framework into the operation of UASs.

Because of complex and strong coupling system, the precision and adaptability of underground robots are greatly restricted. Based on the newly developed intelligent underground heavy-load robot which is still a gap to fill in current coal mine machinery, this paper proposes a new dynamic cooperative optimization control algorithm. Firstly, the complex and strongly coupled Multi-disciplinary Design Optimization system of the robot is decoupled into horizontal/vertical motion space with the idea of hierarchical target transmission, in order to weaken the strong coupling relationship between each hydraulic loop. Then, the spatial posture coefficient is introduced into main/auxiliary feedback control loop in horizontal/vertical motion space, to realize optimal collaborative control of each hydraulic loop under the premise of weak coupling between each control loop, so as to obtain the precise dynamic control signals of each hydraulic loop, and finally realize the optimal control of overall system for the robot. Lastly, the experiment and simulation verify that the DCO control algorithm presented in this paper can obtain better control results: The executive efficiency of the overall system is improved by 14.2%; The control flow is saved by 9.98%, and the executive precision meets the engineering and technical requirements. This paper provides a new efficient method and idea for the control system of intelligent underground heavy-load robots. Furthermore, the algorithm has reference value on development and design of high precise control system for the same kind of complex intelligent engineering machinery products.

Formula Student Driverless (FSD) requires students to design and build a driverless vehicle to race on track, which incurs great demands on the odometry solution. High accuracy odometry plays a significant role in Simultaneous Localization and Mapping (SLAM) and automatic navigation mission. This paper proposes an odometry method based on Camera-Lidar-IMU information fusion and Factor-Graph optimization. It solves the problem of observation of speed and pose transformation in high speed racing scenes with sparse features. Firstly, a YoloV3-tiny object detector is used to identify cone objects captured through camera sensor, which is used to segment the object points from the Lidar pointcloud. Then, the object points are registered by utilizing the inertial measurement unit (IMU) pre-integration result as rough estimation, to obtain increment of pose transformation in horizontal plane. And a Ground Normal Vector Registration method is developed using ground points to solve increment of vertical pose transformation. These two transformation results are coupled to get a real-time odometry. At last, the odometry results and observations are optimized at the back-end with the Factor-Graph algorithm. Experiments show that the method presented in this paper performs well in real environment, and achieves high accuracy and provides a good reference for vehicle SLAM and navigation.

Autism Spectrum Disorder (ASD) is becoming a growing concern worldwide. Parents are often not aware of the different nature of children with ASD and attempt to treat him/her the same way as other children. However, that causes more and more isolation of such children from the social interactions around them, resulting in more secluded and people-phobic behaviors. Nevertheless, similar to other children, children with ASD also like to play with toys. This observation has led to the use of toys in a way that mere playful activities could become sources of learning and skill-building, somewhat serving or assisting in the role of a human teacher. Robots have been observed to be fascinating for all children and compensating for a human companion to a certain extent. In this paper, a short study has been presented involving a humanoid robot programmed for a number of teaching and therapeutic behaviors, such as exercises, singing, explaining, and playing with children. Tests were performed on a small group of 15 children with ASD (ages 7�11) using these activities at a local school for children with special needs for a number of weeks. The objective of the study was to quantify the improvement in a number of behavior and learning parameters when children performed the activities with NAO robot present with the teacher, as opposed to the same type of activities performed by the teacher alone. The performance improvement was quantified in terms of the NAO robot activity as independent variable, and following dependent behavioral variables observed from the responses of children: (a) number of trials, (b) activity response time, (c) response type, and (d) behavior retention. Quantified findings from these tests are reported in this paper against average performance values (based on teachers and psychologists' evaluation). The results of the study have been found to be very encouraging which demonstrates the capability of robotic toys to improve the learning process for children with ASD. The results of this study also encourage the low-cost development and usage of such robotic toy systems for teaching and therapeutic applications that help such children to become better members of society. - 2019, Springer Nature B.V. We would like to thank the KINDI Research Center and Shafallah (Center for Children with Special Needs) that provided the platform to gain practical experience and to develop innovative solutions to improve the future of our country. We would also like to acknowledge the support of the Step by Step School Qatar, the Shafallah Institute Qatar, and the Al-Awsaj Academy Qatar. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Scopus

Mobile robots are frequently faced with mapping and exploring uncertain environments in surveillance, military, and convenience tasks. Often times, human teleoperation is either inconvenient or infeasible for these kinds missions. Furthermore, these tasks can be improved by cooperative multi-agent systems, where coordinating robotic efforts can be complicated and computationally-expensive. This paper presents a stochastic framework for autonomous exploration and patrol with multiple cooperating robots. The first contribution extends the authors’ prior work in single-robot exact occupancy grid mapping and autonomous exploration in a 2D environment to mapping and exploring in a 3D environment. The proposed 3D occupancy grid map is computed efficiently using an inverse sensor model that accounts for the sensor uncertainty, where we propose how several measurement sources may be fused together by considering depth readings individually. This approach is scalable to larger and more complex scenarios for real-time mapping. Furthermore, this paper shows how important aspects of a 3D map representing a structured environment are projected onto a 2D occupancy grid map, where an autonomous exploration algorithm is designed to select robotic motions that maximize map information gain. The mapping and exploration algorithms are demonstrated with an experiment where a quadrotor autonomously maps and explores an initially-uncertain environment. The second contribution is a novel approach to multi-vehicle cooperative patrol of environments based on map uncertainty. We propose a cooperative autonomous exploration algorithm, which applies a bidding-based framework to coordinate robotic efforts for improving occupancy grid map information gain. Since these exploration approaches are based on probabilistic knowledge about the map, the 3D occupancy grid map is systematically degraded over time to encourage the robots to revisit regions as time passes, thereby patrolling the environment. Furthermore, using a Bayesian framework and receding horizons, the algorithm is robust to dynamic obstacles within the mapping space. The efficacy of the proposed multi-vehicle cooperative patrol is illustrated with a simulation involving three robots patrolling a large floor plan with a non-cooperative person walking around the space.

A key issue of load-carrying exoskeleton is to maintain the stability of gait during walking and prevent the wearer from falling over. Some of current strategies were to maintain ZMP (zero-moment point) in the support area, which had been widely used in humanoid robot. However, this method would cause serious interference between body and exoskeleton. because the body often gets energy-saving and rapid walking through unstable conditions during human walking. A control strategy for load-carrying exoskeleton was presented in this paper, which maintained the consistency of body ZMP and exoskeleton ZMP. This control strategy could reduce body-exoskeleton interference while maintaining the stability of walking. In this paper, we firstly introduced the experimental environment and the ZMP detection method, and then designed a computational model of the body-exoskeleton interaction force and evaluation criterion for gait stability. Then, a control strategy based on ZMP was designed. Finally, experiments showed that the control strategy could effectively reduce the body-exoskeleton interference, and maintained the stability of wearer’s walking.

With the rise of service robots, research on cooperation between two-arm robots has become increasingly important. In this paper, two NAO two-armed robots are used as the experimental platform and are combined with projective geometry, vision, robotics and other knowledge to carry out theoretical derivation and experiments on the coordinated movements of dual-arm robots. From the aspect of visual information processing, we analyse and solve the detailed target recognition process. Then, on this basis, we propose a set of complete coordinated motion control schemes. For object recognition, in this paper, we propose a highly adaptable linear stick recognition method. To solve the control flow of coordinated movement, we calculate the inverse kinematics of the unreachable pose of a single NAO manipulator by ignoring the degree of freedom of rotation around an end axis, and propose a trajectory planning method for the vertical constraint relationship between the tool and the workpiece plane in the coordinated manipulator movement. A comparison of the results of a simulation and a real experiment reveals that the trajectories of a workpiece clamped at the ends of the two robots’ mechanical arms are roughly the same; consequently, the coordinated control scheme proposed in this paper is feasible. Moreover, the scheme proposed in this paper is sufficiently accurate to meet service robot applications in daily life. Because the joint active clearance of the NAO robot arm is large and its sensor sensitivity is high, clearance change can be used in the future to replace the force sensor for hybrid control.

In this paper, a powerful optimization framework is proposed to design highly efficient winged unmanned aerial vehicle (UAV) that is powered by electric motors. In the proposed approach, the design of key UAV parameters including both aerodynamic configurations, (e.g. wing span, sweep angle, chord, taper ratio, cruise speed and angle of attack) and the propulsion systems (e.g. propeller, motor and battery) are cast into an unified optimization problem, where the optimization objective is the design goal (e.g. flight range, endurance). Moreover, practical constraints are naturally incorporated into the design procedures as constraints of the optimization problem. These constraints may arise from the preliminary UAV shape and layout determined by industrial design, weight constraints, etc. The backend of the optimization based UAV design framework are highly accurate aerodynamic models and propulsion system models proposed in this paper and verified by actual experiment data. The optimization framework is inherently non-convex and involves both continuous variables (e.g. the aerodynamic configuration parameters) and discrete variables (e.g. propulsion system combinations). To solve this problem, a novel coordinate descent method is proposed. Trial designs show that the proposed method works rather efficiently, converging in a few iterations. And the returned solution is rather stable with different initial conditions. Finally, the entire approach is applied to design a quadrotor tail-sitter VTOL UAV. The designed UAV is validated by both CFD simulations and intensive real-world flight tests.

This paper presents a new Coverage Path Planning (CPP) method for an aerial imaging mission with multiple Unmanned Aerial Vehicles (UAVs). In order to solve a CPP problem with multicopters, a typical mission profile can be defined with five mission segments: takeoff, cruise, hovering, turning, and landing. The traditional arc-based optimization approaches for the CPP problem cannot accurately estimate actual energy consumption to complete a given mission because they cannot account for turning phases in their model, which may cause non-feasible routes. To solve the limitation of the traditional approaches, this paper introduces a new route-based optimization model with column generation that can trace the amount of energy required for all different mission phases. This paper executes numerical simulations to demonstrate the effectiveness of the proposed method for both a single UAV and multiple UAV scenarios for CPP problems.

In this paper, a multiple model-based nonlinear identification approach is introduced for a conventional aircraft in the presence of different types of actuator faults. Occurrence of actuator faults can obviously reduce the validity of a predetermined dynamic model of nonlinear systems. In such cases, use of multi-model structures can be an effective choice. However, determining the optimal validity functions of the local models in a multi-model structure is still a challenging problem. This problem becomes even more challenging in case of unpredictable faults, which are not considered in training the local models. In this paper, two effective techniques are proposed for online determination of the validity functions of local models called the Error-based Gaussian validity functions (E-GVF) and the Online Sequential Extreme Learning Machine (OSELM)-based approach. Accordingly, there is no need to employ a separate Fault Detection and Isolation (FDI) block in the proposed identification approach. Also, due to great capabilities of neural networks for modeling complex nonlinear systems, recurrent neural networks are used as the local models of the proposed multi-model structure. The obtained simulation results indicate the capability of the proposed OSELM-based multi-model structure for nonlinear identification of complex dynamic systems in the presence of both predictable and unpredictable actuator faults.

One of the most impactful and exciting applications of robotic technology, especially autonomous and semi-autonomous systems, is in the field of search and rescue. Robots present an opportunity to go where rescuers cannot, keep responders out of danger, work indefatigably, and augment the capabilities of the humans who put their lives at risk while helping others. This paper examines the use of robotic systems in human rescue applications, with an emphasis on performing search, extraction, evacuation, and medical field treatment procedures. The work begins with a review of the various robotic systems designed to perform one or more of the above operations. The relative merits of each system are discussed along with their shortcomings. The paper also addresses the use of robotic competitions as a means of benchmarking field robotic systems. Based on the review of state of the art systems, a novel concept (Semi- Autonomous Victim Extraction Robot) designed to address the shortcomings of existing systems is described in the conclusion, along with detailed discussion on how it improves upon state of the art systems. The future research thrusts to be explored before realizing a fully integrated robotic rescue system are also detailed.

Autonomous estimation of the state is of key importance in UAVs, as the measurement systems may experience faults and failures. Thus estimation techniques must provide estimates of the most important variables used in the control algorithms for safe, autonomous, unmanned flights. In this paper, a filter with low computational complexity for attitude estimation of a quadrotor UAV is introduced, with a model suitable for Fault-Tolerant Observation. The new filtration method, called the Square Root Unscented Complementary Kalman Filter (SRUCKF), is based on the commonly-known Kalman Filter (KF) in its nonlinear version, namely the Square Root Unscented Kalman Filter (SRUKF). The fundamental equation of the KF is modified so that the complementary feature of the filter is exalted. The new filter introduces characteristics that are analyzed on the basis of its application in quadrotor state estimation. Finally, the results are compared to an ordinary filter of the same type (using the Unscented Transformation). The presented studies indicate that the newly derived filter (SRUCKF) handles strong nonlinearities and gives results similar to those obtained from the SRUKF. Furthermore, it introduces lower computational burden, as the undergoing process uses diagonal matrices in its crucial places. In the paper, the estimation algorithms are tailored to a quadrotor UAV (Crazyflie 2.0), for which a quaternion-based model is proposed. The contribution of the paper lies in a Kalman-based novel state observer and its application in Fault-Tolerant Observation (FTO).

The visual-inertial navigation system using a single camera and IMU requires an accurate initialization without increasing the processing cost and complexity for real-time deployment. The processing cost in the existing solutions can be traced to the gyroscopic bias estimation using 1) Closed-form solutions (Martinelli, Int. J. Comput. Vis. 106(2), 138–152, 2014; Kaiser et al., IEEE Robot. Autom. Lett. 2(1), 18–25, 2017 and 2) Loosely coupled schemes using visual-inertial alignment (Mur-Artal and Tardos IEEE Robot. Autom. Lett. 2(2), 796–803 2017); Qin, IEEE Trans. Robot. 34(4), 1004–1020 2018). The complexity arises because of the non-linear nature of the system to estimate the gyro bias, which is solved either by directly solving the non-linear, non-convex problem or by decoupling the vision and IMU measurements using linear models. The termination conditions are based on condition number or covariance of the estimated variables, which varies from one experiment to another. The present paper seeks to improve the closed-form solution with higher accuracy and less processing cost per frame. The proposed method separates the gyroscope bias estimation from the closed-form solution using tightly coupled but linear models with reduced number of variables in the closed-form solution. This paper also addresses the problem inherent to the closed-form solutions, which requires sufficient motion in the initialization window with a minimum number of common features. Towards this, a novel method of propagating the past information into the present initialization window is presented. This reduces the total processing cost per frame by limiting the initialization window to 10 frames ≈ 1s without compromising the motion inside the window. We also present a common and intuitive termination criteria which is independent from the experiment scenario. This helps to increase the robustness in the initialization by removing erroneous solutions. The proposed method is evaluated with EuRoC Micro Aerial Vehicle (MAV) dataset (Burri et al. 2016) sequences. We compare the proposed method with a recently proposed loosely coupled method, which shows the improved accuracy, processing cost and robustness in the initialization.

Twin-rotor two-input two-output (TITO) aerodynamical system (TRAS) is a highly non-linear system with a cross-coupling effect. The objective of the controller design in TRAS is to track the trajectory of azimuth and pitch angles, reach the desired azimuth and pitch angle positions, and stabilize TRAS in the presence of significant cross-coupling. The coupling effect between the main rotor and tail rotor is a severe issue for the controller design. So, this paper proposes a proportional integral derivative double derivative (PIDD2) controller design for TRAS, first time. Further, the tuning of PIDD2 controller is carried out via a modified grey wolf optimizer (MGWO). Besides, the real-time hardware-in-loop (HIL) implementation of the PIDD2 controller with the TRAS laboratory prototype setup is carried out, first-time. Moreover, It is tested under external disturbances for robustness and effectiveness analyses. In addition, this paper performs both simulations as well as hardware results analyses. These analyses reveal that the experimental results are found to match closely with simulation results. Finally, the efficacy of the proposed control design approach is presented by comparing it with the recently published controldesign schemes.

This paper is motivated by the autonomous shuttle service that operates in the geo-fenced Linden Residential Area of Columbus, Ohio that links residents to the two nearby locations of opportunity of a community center and a transit hub. This paper focuses on path planning and path tracking of an autonomous shuttle which are its most fundamental autonomous driving functions. Path planning is based on improving efficiency of computation and smoothness of path. Velocity planning is based on obeying speed limits, limiting longitudinal acceleration along straight segments and lateral acceleration during curved segments for improved ride comfort of the passengers. Path tracking control focuses on robust implementation that keeps accuracy of path following in the presence of uncertainties and variations in speed. A realistic, 3D virtual simulation environment of the actual geo-fenced urban area used here is built for evaluating and developing the path planning and path tracking functions of this paper. The same environment can also be used for developing and evaluating other autonomous driving functions with the capability of generating complicated traffic scenarios. The path tacking control results are compared with those of the pure pursuit path tracking algorithm of the open source and publicly available Autoware autonomous driving interface for the Robot Operating System.

The domain of robotics is successfully automating majority of terrestrial applications with significant breakthroughs in research involving wheeled robots. Robots equipped with biomimetic capabilities can aid effectively in many scenarios where conventional infrastructure such as roads, flat terrains and wheeled robots are not available. 2DxoPod is a modular robot developed with the aim improve the mobility in robots in challenging scenarios by taking advantage of its features tuned to enhance biomimetic capabilities. A novel joint mechanism present at the center of the robotic design is capable of mimicking movements in vertebrates and providing two degrees of freedom that are orthogonal and coincident to each other. The paper describes characteristics of the 2DxoPod module and it’s advantages with respect to modular robots developed in the field of robotics. Simulations are performed on navigation capabilities of the snake and quadruped robotic coordinated structures assembled using 2DxoPod robotic modules using centralized pattern generator and the results of the same are also provided in the paper.

In recent years, Unmanned Aerial Vehicles (UAVs) are widely utilized in precision agriculture, such as tree plantations. Due to limited intelligence, these UAVs can only operate at high altitudes, leading to the use of expensive and heavy sensors for obtaining important health information of the plants. To fly at low altitudes, these UAVs must possess the capability of obstacle avoidance to prevent crashes. However, most current obstacle avoidance systems with active sensors are not applicable to small aerial vehicles due to the cost, weight, and power consumption constraints. To this end, this paper presents a novel approach to the autonomous navigation of a small UAV in tree plantations only using a single camera. As the monocular vision does not provide depth information, a machine learning model, Faster Region-based Convolutional Neural Network (Faster R-CNN), was trained for the tree trunk detection. A control strategy was implemented to avoid the collision with trees. The detection model uses image heights of detected trees to indicate their distances from the UAV and image widths between trees to find the widest obstacle-free space. The control strategy allows the UAV to navigate until any approaching obstacle is detected and to turn to the safest area before continuing its flight. This paper demonstrates the feasibility and performance of the proposed algorithms by carrying out 11 flight tests in real tree plantation environments at two different locations, one of which is a new place. All the successful results indicate that the proposed method is accurate and robust for autonomous navigation in tree plantations.

The mission of future parcel delivery will be performed by unmanned aerial vehicles (UAVs). However, the localization of global navigation satellite systems (GNSS) in urban areas experiences the notorious multipath effect and non-line-of-sight (NLOS) reception which could potentially generate approximately 50 meters of positioning error. This misleading localization result can be hazardous for UAV applications in GNSS-challenged areas. Due to multipath complexity, there is no general solution to eliminate this effect. A solution to guide UAV operation is to plan an optimal route that smartly avoids the area with a strong multipath effect. To achieve this goal, the impact of the multipath effect in terms of positioning error at different locations must be predicted. This paper proposes to simulate the reflection route by a ray-tracing technique, aided by predicted satellite positions and the widely available 3D building model. Thus, the multipath effect in the pseudorange domain can be simulated using the reflection route and multipath noise envelope according, according to specific correlator designs. By constructing the multipath-biased pseudorange domain, the predicted positioning error can be obtained using a least square positioning method. Finally, the predicted GNSS error distribution of a target area can be further constructed. A new A* path planning algorithm is developed to combine with the GNSS error distribution. This paper designs a new cost function to consider both the distance to the destination and the positioning error at each grid. By comparing the conventional and the proposed path planning algorithms, the planned paths of the proposed methods experienced fewer positioning errors, which can lead to safer routes for UAVs in urban areas.

This paper surveys various applications of artificial evolution in the field of modular robots. Evolutionary robotics aims to design autonomous adaptive robots automatically that can evolve to accomplish a specific task while adapting to environmental changes. A number of studies have demonstrated the feasibility of evolutionary algorithms for generating robotic control and morphology. However, a huge challenge faced was how to manufacture these robots. Therefore, modular robots were employed to simplify robotic evolution and their implementation in real hardware. Consequently, more research work has emerged on using evolutionary computation to design modular robots rather than using traditional hand design approaches in order to avoid cognition bias. These techniques have the potential of developing adaptive robots that can achieve tasks not fully understood by human designers. Furthermore, evolutionary algorithms were studied to generate global modular robotic behaviors including; self-assembly, self-reconfiguration, self-repair, and self-reproduction. These characteristics allow modular robots to explore unstructured and hazardous environments. In order to accomplish the aforementioned evolutionary modular robotic promises, this paper reviews current research on evolutionary robotics and modular robots. The motivation behind this work is to identify the most promising methods that can lead to developing autonomous adaptive robotic systems that require the minimum task related knowledge on the designer side.

This review paper presents the Entropy approach to modeling and performance evaluation of Intelligent Machines (IMs), which are modeled as hierarchical, multi-level structures. It provides a chronological summary of developments related to intelligent control, from its origins to current advances. It discusses fundamentals of the concept of Entropy as a measure of uncertainty and as a control function, which may be used to control, evaluate and improve through adaptation and learning performance of engineering systems. It describes a multi-level, hierarchical, architecture that is used to model such systems, and it defines autonomy and machine intelligence for engineering systems, with the aim to set foundations necessary to tackle related challenges. The modeling philosophy for the systems under consideration follows the mathematically proven principle of Increasing Precision with Decreasing Intelligence (IPDI). Entropy is also used in the context of N-Dimensional Information Theory to model the flow of information throughout such systems and contributes to quantitatively evaluate uncertainty, thus, autonomy and intelligence. It is explained how Entropy qualifies as a unique, single, measure to evaluate autonomy, intelligence and precision of task execution. The main contribution of this review paper is that it brings under one forum research findings from the 1970’s and 1980’s, and that it supports the argument that even today, given the unprecedented existing computational power, advances in Artificial Intelligence, Deep Learning and Control Theory, the same foundational framework may be followed to study large-scale, distributed Cyber Physical Systems (CPSs), including distributed intelligence and multi-agent systems, with direct applications to the SmartGrid, transportation systems and multi-robot teams, to mention but a few applications.

This paper presents a kinematic controller for a differentially driven mobile robot. The controller is based on the navigation function (NF) concept that guarantees goal achievement from almost all initial states. Slow convergence in some cases is a significant disadvantage of this approach, especially when narrow passages exist in the environment and/or specific values of design parameters are set. The main reason of this phenomenon is that the velocity control strongly depends on the slope of the NF. The algorithm proposed in this paper is based on a method introduced in Urakubo (Nonlin. Dyn. 81(3): 1475–1487 2015), that extends NF to nonholonomic mobile platforms and allows stabilizing not only the position of robots but also their orientation. This algorithm is used as a reference in experimental performance comparison. In the new algorithm, the gradient of the NF is used to generate motion direction but the velocity is computed as a function of position and orientation errors. This approach results in much better state converge. Analysis of the convergence shows how the location of the eigenvalues of linearized system affects time of goal achievement. The paper describes saddle point detection and avoidance methodology and presents their experimental verification. It also shows what happens in practice if initial position is located exactly in the saddle point and its detection/avoidance procedures are turned off.

This paper addresses the problem of normal forms and singularities of non-holonomic robotic systems represented by control-affine systems. By means of the concept of the end-point map of the system, and of the system’s Jacobian, the configuration singularities have been defined as the control functions for which the Jacobian is not surjective. The presence of these singularities impairs performance of Jacobian motion planning algorithms. Being the singular optimal controls, the configuration singularities can be examined using the tools from the optimal control theory. The main idea of this paper is to rely the analysis of configuration singularities on normal forms of robotic systems. This idea has been applied to the dynamics of a space manipulator. Normal forms of this manipulator under the feedback equivalence have been obtained, and exploited in the analysis of its configuration singularities.

This paper presents new efficient guidance algorithms allowing Unmanned Aircraft Systems (UAS) to avoid a variety of Global Navigation Satellite System (GNSS) continuity and integrity performance threats detected by an Aircraft Based Augmentation System (ABAS). In particular, the UAS guidance problem is formulated as an optimal control-based Multi-Objective Trajectory Optimization (MOTO) problem subject to suitable dynamic and geometric constraints. Direct transcription methods of the global orthogonal collocation (pseudospectral) family are exploited for the solution of the MOTO problem, generating optimal trajectories for curved GNSS approaches in real-time. Three degrees-of-freedom aircraft dynamics models and suitable GNSS satellite visibility models based on Global Positioning System (GPS) constellation ephemeris data are utilised in the MOTO solution algorithm. The performance of the proposed MOTO algorithm is evaluated in representative simulation case studies adopting the JAVELIN UAS as the reference platform. The paper focusses on descent and initial curved GNSS approach phases in a Terminal Maneuvering Area (TMA) scenario, where multiple manned/unmanned aircraft converge on the same short and curved final GNSS approach leg. The results show that the adoption of MOTO based on pseudospectral methods allows an efficient exploitation of ABAS model-predictive augmentation features in online GNSS guidance tasks, supporting the calculation of suitable arrival trajectories in 7 to 16 s using a normal PC.