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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.

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.

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.

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 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.

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.

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.

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.

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 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.

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.

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.

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 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.

Understanding how multiple drones can coordinate and communicate is essential for advancing multi-agent robotics. Multiple drone applications are growing with technology and have reached areas such as: farming, meteorology, pollution detection, and forest fire monitoring. The focus of this paper is to illustrate how current systems manage mission control and communication strategies for multi-agent drone deployments. The primary scope was to examine papers that provide promising experimental results and analyze popular communication hardware used. Principal results included the classification of two main mission control strategies: centralized and decentralized. In addition the two most popular formation strategies leader-follower and virtual structure were compared. Finally, successful experimental frameworks that were used for practical applications were introduced and classified. Most results were limited in number of agents or were in their initial experimental stages. The literature review revealed a need for a greater focus on overall robustness of multi-agent systems.

The aim of this paper is to provide a global overview of mobile robot control and navigation methodologies developed over the last decades. Mobile robots have been a substantial contributor to the welfare of modern society over the years, including the industrial, service, medical, and socialization sectors. The paper starts with a list of books on autonomous mobile robots and an overview of survey papers that cover a wide range of decision, control and navigation areas. The organization of the material follows the structure of the author’s recent book on mobile robot control. Thus, the following aspects of wheeled mobile robots are considered: kinematic modeling, dynamic modeling, conventional control, affine model-based control, invariant manifold-based control, model reference adaptive control, sliding-mode control, fuzzy and neural control, vision-based control, path and motion planning, localization and mapping, and control and software architectures.

This paper presents CRSPF-SLAM, a critical rays self-adaptive particle filtering occupancy grid based SLAM system that can operate efficiently with different kinds of odometer in real time, in small and large, indoor and outdoor environments for various platforms. Its basic idea is to eliminate the accumulated error of odometer through scan to map matching based on particle filtering. Through some improvements for the original particle filtering method, the lidar system becomes more robust to conduct accurate localization and mapping. Specifically, in our proposed method, particle filter based on Monte-Carlo algorithm is designed to be out-of-step to the odometer; During the scan matching process, the influence of some critical rays selected through a ray-selection algorithm is enhanced and that of the unreliable rays is weaken or removed; The current optimal match value is regarded as the feedback to reset the particle number and the filtering range; Once the optimal pose and scan are obtained, the previous error scan stored in the map will be removed. It is also introduced in the paper that the method can work effectively with dead reckoning, visual odometry and IMU, respectively. And we have tried to use it on different types of platforms — an indoor service robot, a self-driving car and an off-road vehicle. The experiments in a variety of challenging environments, such as bumpy and characterless area, are conducted and analyzed.

In this paper we present the design and implementation of autopilot tracking control law for Micro Aerial Vehicle using the second order sliding mode approach. The inner loop attitude tracking control design is carried out using output feedback based second order sliding mode technique, to ensure finite time convergence of the tracking error dynamics. While addressing tracking control of a time varying reference signal, it is important to investigate the stability characteristics of the internal dynamics to ensure perfect tracking. This paper mainly addresses the output tracking control problem for a MAV and investigate the stability characteristics of the longitudinal zero dynamics during tracking. We have proposed a stability proof based on Lyapunov theory to analyze the stability of the MAV longitudinal zero dynamics during tracking. A nonlinear aircraft model obtained using aerodynamic derivatives of The Blackkite 300 mm wingspan fixed MAV is used for both control design and as well as to verify its performance against the classical control methods. Extensive hardware in-loop simulation results of the proposed control algorithm implemented on the commercially available PX4 based Pixhawk autopilot board are also presented here.

This paper presents the concept of Cognitive Human-Machine Interfaces and Interactions (CHMI2) for Unmanned Aircraft System (UAS) Ground Control Stations (GCS). CHMI2 represents a new approach to aviation human factors engineering that introduces adaptive functionalities in the design of operators' command, control and display functions. A CHMI2 system assesses human cognitive states based on measurement of key psycho-physiological observables. The cognitive states are used to predict and enhance operator performance in the accomplishment of aviation tasks, with the objective of improving the efficiency and effectiveness of the overall human-machine teaming. The CHMI2 system presented in this paper employs a four-layer architecture comprising sensing, extraction, classification and adaptation functionalities. An overview of each layer is provided along with the layer's metrics, algorithms and functions. Two relevant case studies are presented to illustrate the interactions between the different layers, and the conceptual design of the associated display formats is described. The results indicate that specific eye tracking variables provide discrimination between different modes of control. Furthermore, results indicate that the higher levels of automation supported by the CHM 2 are beneficial in Separation Assurance and Collision Avoidance (SA&CA) scenarios involving low-detectability obstacles and stringent time constraints to implement recovery manoeuvres. These preliminary results highlight that the introduction of CHMI2 functionalities in future UAS can significantly reduce reaction time and enhance operational effectiveness of unmanned aircraft response to collision and loss of separation events, as well as improve the overall safety and efficiency of operations.

Teleoperation allows humans to reach environments that would otherwise be too difficult or dangerous. The distance between the human operator and remote robot introduces a number of issues that can negatively impact system performance including degraded and delayed information exchange between the robot and human. Some operation scenarios and environments can tolerate these degraded conditions, while others cannot. However, little work has been done to investigate how factors such as communication delay, automation, and environment characteristics interact to affect teleoperation system performance. This paper presents results from a user study analyzing the effects of teleoperation factors including communication delay, autonomous assistance, and environment layout on user performance. A mobile robot driving task is considered in which subjects drive a robot to a goal location around obstacles as quickly (minimize time) and safely (avoid collisions) as possible. An environment difficulty index (ID) is defined in the paper and is shown to be able to predict the average time it takes for the human to drive the robot to a goal location with different obstacle configurations. The ID is also shown to predict the path chosen by the human better than travel time along that path.

A Steerable in-pipe inspection robot is designed in this paper and its optimal control based on linear quadratic regulator (LQR) approach is performed subject to input minimization. In-pipe inspection robots are necessary mobile robots in order to investigate the pipelines. Most of the in-pipe inspection robots are limited to move with a constant pitch rate. An in pipe inspection robot is proposed in this paper which is based on screw locomotion and its steering angle is also controllable in order to handle the pitch rate of the movement and bypass the probable obstacles. Since the proposed robot is multivariable with more than one controlling input, minimizing its control inputs are extremely useful. The goal of this paper is to extract the dynamic model of the mentioned steerable screw in-pipe inspection robot and controlling it within a predefined trajectory in an optimal way. The proper mechanism is designed and its related kinematics and kinetics are derived. Afterwards the objective function is defined based on minimizing the controlling input and maximizing the accuracy of movement. The nonlinear state space is linearized around its operating point and optimization is implemented using Linear Quadratic Regulator (LQR). The efficiency of the designed robot and controller and the optimality of its controlling procedure are investigated by the aid of MATLAB simulation and comparative analysis. It is proved that the designed robot is able to move with controllable pitch rate and acceptable accuracy while the obstacles can be avoided and the energy consumption is optimized. At the end the validity of modeling and simulation in MATLAB is also verified by modeling the robot in ADAMS and comparing the results.

A cooperative aircraft differential game where an Attacker missile pursues an unmanned aerial vehicle (UAV) herein called the Target is addressed. The Target UAV cooperates with up to two Defender missiles which are launched in order to intercept the Attacker before the latter reaches the Target. This is a scenario with important military applications where each one of the agents is an autonomous air vehicle. Each agent plans and corrects its course of action in order to defeat an opposing force while simultaneously optimizing an operational relevant cost/payoff performance measure. The Target and the Defenders cooperate to form a team against the Attacker. The results in this paper build on the solution of a three agent differential game, where the three players are the Target, the Attacker, and one Defender; in this paper, the benefits of firing a second Defender are considered. Indeed, launching two interceptor missiles is a standard procedure by providing redundant backup. Building on the solution of the one-Defender problem, it is possible to address a seemingly intractable problem, where the Target needs to decide which Defender(s) to cooperate with, in addition to obtaining the optimal headings of every player in the game. Given the initial positions of the players, we solve the problem of determining if a second Defender improves the Target/Defender(s) payoff and provide the optimal strategies for each of the agents involved. Finally, we address the game of kind (for the case of one Defender) which provides the safety regions to determine which side will win based on the initial state. These safety regions provides the Target’s area of vulnerability, and using these results, we describe the reduction to the Target’s vulnerability area brought by an additional Defender.

To achieve fully autonomous operation for Unmanned Aerial Systems (UAS) it is necessary to integrate multiple and heterogeneous technical solutions (e.g., control-based methods, computer vision methods, automated planning, coordination algorithms, etc.). The combination of such methods in an operational system is a technical challenge that requires efficient architectural solutions. In a robotic engineering context, where productivity is important, it is also important to minimize the effort for the development of new systems. As a response to these needs, this paper presents Aerostack, an open-source software framework for the development of aerial robotic systems. This framework facilitates the creation of UAS by providing a set of reusable components specialized in functional tasks of aerial robotics (trajectory planning, self localization, etc.) together with an integration method in a multi-layered cognitive architecture based on five layers: reactive, executive, deliberative, reflective and social. Compared to other software frameworks for UAS, Aerostack can provide higher degrees of autonomy and it is more versatile to be applied to different types of hardware (aerial platforms and sensors) and different types of missions (e.g. multi robot swarm systems). Aerostack has been validated during four years (since February 2013) by its successful use on many research projects, international competitions and public exhibitions. As a representative example of system development, this paper also presents how Aerostack was used to develop a system for a (fictional) fully autonomous indoors search and rescue mission.

In this paper we present a novel concept of attitude control for a multi-rotor unmanned aerial vehicle by actively controlling its center of gravity. This research is a part of our efforts to build a heavy lift multi-rotor platform capable of carrying over 50 kg of payload. To that end, we propose using miniature two-stroke combustion engines to supply the necessary lift and combine them with moving masses used to control the vehicle attitude. In this paper we present a complete nonlinear dynamical model of such a vehicle and use it to derive the transfer functions of roll and pitch dynamics. Furthermore, we formulate a detailed root-locus based stability and sensitivity analysis of the proposed control scheme and discuss its underlining effect on the mechanical parameter design. We present the experimental testbed, consisted of the vehicle mounted on a 2 degrees of freedom gimbal, and derive necessary conditions for testbed parameters in order to match the testbed and free-flight quadrotor dynamics. Finally, we present simulation results from a Gazebo based simulator and experimental results of the testbed. Both results confirm the findings of our mathematical analysis.

The speed, agility, maneuverability, as well as the range and efficiency of insects and birds are far beyond the state of the art mini flying vehicles. Especially within the last couple of decades there have been various research works to create bio-inspired flying vehicles that try to imitate these amazing creatures. In this paper, the design and control of a micro aerial vehicle that has four flapping wings is introduced. The paper introduces firstly, the mechanical design of the vehicle that uses a four-bar mechanism as the wing mechanism in each flapping wings. In order to obtain flapping motion from rotation of the motor, a crank rocker mechanism is used by connecting the shortest link to the fixed link. Flapping range (swing angle) is determined by obtaining dead-center positions, and it is chosen as large as possible to maximize the lift force. With the help of the transmission angle optimization, and keeping the deviation of transmission angle from 90 ∘ as small as possible, the dimensions of the wings are determined. Once the mechanical design is finalized, controller system design was performed. To develop a successful controller, a model of the vehicle was introduced. PID controllers were proposed and Matlab Simulink simulations were developed to show that the proposed PID controllers are able to control the vehicle successfully. Lastly, a prototype vehicle has been designed and manufactured with the help of the results of this paper.

In the paper, a novel hybrid driven underwater vehicle which can realize anti-disturbance twice is proposed. When is disturbed by the water surge and waves in the shallow water area, the attitude of the vehicle can be once adjusted by the attitude control algorithm and the motion drive system. Then the second anti-disturbance adjustment over shooting platform by a three-axis cloud terrace is actualized. The attitude control of the camera platform is realized by two anti-disturbance adjustments that ensures the whole shooting process continuous, smooth and complete. The design of the mechanical structure and the control system are main contents of the paper. Kinematics analysis and dynamics modeling are also included. Simulations and the software design are performed to demonstrate the feasibility of the system.

This paper presents a decentralized control approach of harmonic drive (HD) based modular robot manipulator (MRM) using only position measurements. Unlike known methods that rely on joint torque and velocity sensing, this paper addresses the problem of controlling HD based MRM using only position measurements on the motor and link sides of each module. The dynamic model of HD based MRM is formulated by employing a high-fidelity HD model and the velocity of each robot joint is estimated based on a novel nonlinear velocity estimator. With only local information on each module, a decentralized integral sliding mode controller (ISMC) is designed based on variable gain super-twisting algorithm (VGSTA) to compensate the model uncertainty and to reduce the chattering effect of the controller. The asymptotic stability of the closed-loop system is proved using the Lyapunov theory. Finally, experiments are performed for a 3-DOF MRM to verify the advantage of the proposed method.

Nonlinear aircraft controlled by its linearized model based controller will be asymptotically stable if its trajectories originating from various initial conditions are all contained in the stability region (or the region of attraction). In such a stable aircraft, suppose a sensor or an actuator fault occurs. Depending upon the fault size, the stability region is modified. As a result, the trajectories for a potential failure to contain in the modified stability region could lead to flight control degradation. In this paper, first, a stability margin is determined to separate an acceptable fault size from an unacceptable fault. Secondly, with an acceptable fault size, the aircraft will remain stable in distorted stability regions. In this paper, admissible control inputs which generate the perturbed trajectories in the distorted stability regions is presented for safe unmanned aerial vehicle flight control evaluations. A three degree of freedom aircraft in pitch plane is considered to illustrate the stability margins and control inputs that are safe to operate the damaged aircraft.

This paper proposes an efficient and distributed deployment strategy to optimally distribute teams of robots in environments that can be represented by topological maps. Among the several applications of our solution are sensing and coverage of large corridor-based buildings, such as hospitals and schools, and the optimal placement of service vehicles in the streets of a big city. The representation of the environment as a topological map transforms the original two or three-dimensional problem into a one-dimensional, simplified problem, thus reducing the computational cost of the solution. Moreover, each robot can reach its final position by simply following a sequence of intuitive, human-like commands, without the need for global metric localization, which also simplifies robot control. Besides presenting convergence proofs for our method, the paper also presents simulated and real world experiments that illustrate and validate our approach.

This paper presents an optical-aided navigation method for automatic flights where satellite navigation might be disturbed. The proposed solution follows common approaches where satellite position updates are replaced with measurements from environment sensors such as a camera, lidar or radar as required. The alternative positioning is determined by a localization and mapping (SLAM) algorithm that handles 2D feature inputs from monocular camera images as well as 3D inputs from camera images that are augmented by range measurements. The method requires neither known landmarks nor a globally flat terrain. Beside the visual SLAM algorithm, the paper describes how to generate 3D feature inputs from lidar and radar sources and how to benefit from both monocular triangulation and 3D features. Regarding state estimation, the approach decouples visual SLAM from the filter updates. This allows software and hardware separation, i.e. visual SLAM computations on powerful hardware while the main filter can be installed on real-time hardware with possible lower capabilities. The localization quality in case of satellite dropouts is tested with data sets from manned and unmanned flights with different sensors while keeping all parameters constant. The tests show the applicability of this method in flat and hilly terrain and with different path lengths from few hundred meters to many kilometers. The relative navigation achieves an accumulation error of 1–6 % of distance traveled depending on the flight scenario. In addition to the flights, the paper discusses flight profile limitations when optical navigation methods are used.

This paper deals with navigation for a group of vehicles while avoiding collisions and ensuring global network connectivity in unknown environments using a new decentralized navigation function. It is pointed out that the traditional navigation function is not effective in the situation where vehicles work in a large environment. It is shown that in this situation velocity of the vehicle would be extremely small, which is not realistic in practical applications. This paper proposes a new decentralized navigation function with a novel goal function based on which a decentralized control law that is along the negative gradient of the decentralized navigation function is derived. Finally the proposed decentralized control law is applied in a multi-vehicle navigation scenario. Based on the properties of the proposed navigation function and dual Lyapunov theorem, a sufficient condition is derived for vehicles to converge to regions surrounding their corresponding goal positions in a collision-free and connectivity-keeping manner. Simulation results demonstrate the efficacy of the proposed method. Accepted version

Globally exponentially stabilizing a class of underactuated mechanical systems (UMS) with nonaffine nonlinear dynamics is investigated in this paper. The considered UMS has a nonaffine nonlinear subsystem that can be globally asymptotically stabilized by saturated feedbacks, but the saturated feedback cannot be analytically expressed in closed-form. This obstacle limits the real-time applications of most controllers presented in literatures. In this paper, a hybrid feedback strategy is presented to globally exponentially stabilize the UMS with nonaffine and strict-feedback canonical forms. The hybrid feedback strategy is characterized by the composition of partial states feedback and partial virtual outputs feedback based on a higher-order finite-time stabilizing observer. The presented hybrid feedback controller can be synthesized by applying Lyapunov stability theory. Some numerical simulations associated with two underactuated nonlinear systems, the Acrobot system and the Inertia-Wheel-Pendulum (IWP) system, are employed to demonstrate the effectiveness of the proposed controller. The presented control strategy can be applied in real time, thus providing a new feasible dynamic model other than the differential flatness systems for synthesizing the mechanical systems of general underactuated legged robots.

The paper proposes an algorithm for multi-robot coordination and navigation in order to intercept a target at a long distance. For this purpose, a limit cycle based algorithm using a neural oscillator with phase differences is proposed. The state of target is unknown, under the assumption that it is stationary or in motion with constant unknown speed along a straight line. Using the proposed algorithm, a group of robots is intended to move towards the target in such a way that the robots surround it. While moving to the target, self-collision between the robots is avoided. Moreover, a collision avoidance with static obstacles as well as dynamic target is realized. The robots reach the target at a desired distance, keeping uniformly distributed angles around the target. The algorithm is further extended so that a static interception point for the target can be estimated in place of pursuing a dynamic target, which is referred to as a virtual target in this paper. In other words, the robots move towards the virtual target instead of the actual target. The robots ultimately encircle the actual target when they arrive at the virtual target. The effectiveness of the proposed method is verified through simulation results.

This paper proposes a novel path planning method for improving the feasibility of a forced landing. When an aircraft completely loses its thrust, the only measure it can take is to make a forced landing at an adjacent airport as soon as possible. In such a situation, the flight path to the landing point must be safe and viable. This paper details a method which enables safer and easier landing by transferring the benefits of excess altitude to the final approach length. Moreover, by planning the descent angle of final approach to be in the middle of a non-spoiler and a full-spoiler glide angle, this method enables a change in descent angle to correct any tracking errors, without using thrust. To verify the effectiveness of the proposed method, six degrees-of-freedom nonlinear simulations were performed and the results are compared with comparable methods. From the simulation results, it was confirmed that the proposed method could plan a safe path in a sufficiently short time and the aircraft could reach the landing point safely.

Parallel kinematic mechanisms (PKM) have received particular attention due to their higher stiffness, increased payload capacity, and agility, when compared to their serial counterparts. Despite these significant advantages, however, most PKM designs, typically, yield limited workspace, problematic singularities, and configuration-dependent stiffness. In response, mechanism redundancy has emerged as an effective tool to address these and other problems. In this paper, we present an in-depth discussion of past research on PKM redundancy. The methodical review of the pertinent literature, first, introduces the concept of redundancy based on the number of actuators and the number of degrees of freedom required to perform a task and, then, discusses the two main types of redundancy according to the mobility of the mechanism, i.e., kinematic and actuation redundancy. Subsequently, research on the design aspects of redundant PKMs, including the various criteria used for design optimization is detailed. Primary advantages of PKM redundancy include workspace enlargement, singularity elimination/ avoidance, and improved joint-torque distribution. In this paper, these advantages are discussed in contrast to the main challenges redundant mechanisms present, such as in motion planning and control, and calibration. Other issues of PKM redundancy, briefly, presented herein for completeness are fault-tolerance, reconfigurability, cable-driven and hyper-redundant PKMs.

This paper considers a pursuit-evasion game for non-holonomic systems where a group of pursuers attempts to capture an evader in a bounded connected domain. The problem is challenging because all vehicles have the same maneuvering capability in terms of speed and turn radius constraint. The paper initially discusses a simple approach for holonomic systems that is based on the minimization of the safe-reachable area (the area containing the set of points to where an evader can travel without being caught). This idea is then extended to develop a pursuit-evasion strategy for non-holonomic systems. However, solving such a problem is computationally intractable. Therefore, we propose a computationally efficient algorithm to obtain approximate solutions. This paper also proposes an alternative approach to obtain a simple yet effective solution to the cooperative pursuit problem that is based on missile guidance laws. As there is no analytical proof of capture, we empirically evaluate the performance of the algorithms and perform a comparative study using solutions obtained from umpteen simulations. A total of four different cooperative pursuit strategies and three different evader strategies are taken into account for the comparative study. In the process, an evader strategy which is superior to that based on the optimization of safe-reachable area is also identified.

This paper investigates solutions for the fundamental yet challenging problem of autonomous landing of multirotor Unammaned Aerial Vehicles UAVs. In addition to landing on static targets, tracking and landing on a moving platform is addressed, as a solution to facilitate the deployment of the UAV. The paper presents the design of a new landing pad and its relative pose estimation. The fusion of inertial measurement with the estimated pose is considered to ensure a high sampling rate, and to increase the manoeuvrability of the vehicle. Two filters are designed to conduct the fusion, an Extended Kalman Filter (EKF) and an Extended Hź (EHź). The extensive simulation and practical tests permitted identification of the challenges of the landing task. Adequate solutions to these challenges are proposed to lessen their impact on landing precision.

In this paper, we address pursuit-evasion games of high speed evader involving multiple pursuers and a single evader with holonomic constraints in an open domain. The existing work on this problem discussed the required formation and capture strategy for a group of pursuers. However, the formulation has mathematical errors and has raised concerns over the validity of the developed capture strategy. This paper uses the idea of Apollonius circle to develop an escape strategy for the high speed evader, resolving the shortfalls in the existing work. The strategy is built on a concept of perfectly encircled formation and the conditions required to construct the same are presented. The escape strategy contains two steps. Firstly, the evader employs a strategy that forces a gap in the formation against all the admissible strategies of a group of pursuers. In the second step, it uses this gap to escape. The strategy considers both direct and indirect gaps in the formations. The indirect gap is encountered when a group of three or four pursuers is employed to capture. The efficacy of the escape strategy is established using simulation results.

In this paper, a path planning system is proposed for optimal rendezvous of multiple underwater gliders in three-dimensional (3D) space. Inspired by the Dubins Paths consisting of straight lines and circular arcs, this paper presents the first attempt to extend the 3D Dubins curve to accommodate the characteristic glider motions include upwards and downwards straight glides in a sawtooth pattern and gliding in a vertical spiral. This modified 3D Dubins scheme is combined with genetic algorithm (GA), together with a rendezvous position selection scheme to find rendezvous trajectories for multiple gliders with minimal energy consumption over all participating vehicles. The properties and capabilities of the proposed path planning methodology are illustrated for several rendezvous mission scenarios. First, a simple application was performed for a single glider to rendezvous with a fix dock. Simulation results show the proposed planner is able to obtain more optimized trajectories when compared with the typical Dubins trajectory with nominal velocity. Additional representative simulations were run to analyse the performance of this path planner for multiple gliders rendezvous. The results demonstrate that the proposed path planner identifies the optimal rendezvous location and generates the corresponding rendezvous trajectories for multiple gliders that ensures they reach their destination with optimized energy consumption.

This paper presents HAMSTER, the HeAlthy, Mobility and Security based data communication archiTEctuRe. HAMSTER is designed for Unmanned Vehicles and addresses mainly three types of communications: machine-to-machine, machine-to-infrastructure and internal machine communications. It is divided into three main versions: Flying HAMSTER (for aerial systems), Running HAMSTER (for terrestrial systems) and Swimming HAMSTER (for aquatic systems). Every version of such architecture is also equipped with Sphere and Nimble. Sphere deals with Safety & Security aspects regarding communication, components "health" and modules authentication. Nimble is aimed at increasing the overall mobility in such scenarios, strongly actuating with inherent communications of each application field. This paper details every aspect of HAMSTER and presents, as a plus at the end, two case studies: the first one consists of an evaluation of five communications schemes for internal communications in airplanes; the second one is a cryptographic evaluation of two Elliptic Curve Cryptography algorithms.

A planning approach for a platform composed of multiple unmanned aerial systems is presented in this paper. The research activities are focused on the interoperability, task allocation and task planning problems within the system. In order to tackle with the interoperability problem between the vehicles of the platform and other external systems, C-BML has been chosen as the standard language to formalize the description of the missions. Regarding the planning problems involved, several planners have been applied to solve them: a task allocation planner to create an initial assignment of tasks to vehicles, a symbolic planner for high-level reasoning and tools for geometric reasoning. The main contribution of the paper is the use of consolidated task planning techniques to automatically generate low-level plans from a mission described in C-BML, filling the gap between interoperability and automatic plan generation for missions where multiple heterogeneous aerial platforms are involved. The approach has been tested in missions involving multiple surveillance and 3D map generation tasks and the paper includes experimental results.

The objective of this paper is to develop a vision-based navigation technique for micro aerial vehicles, quadrotor type, to operate in GPS-denied environment. The navigation method has been developed while using appearance-based Visual-Teach-and-Repeat (VT&R) technique. In a teaching phase, a quadrotor is manually navigated along a desired route to collect a set of reference images. In a repeating phase, the quadrotor is able to autonomously follow the desired route using these reference images. Self-localization is developed to determine the current segment of the desired route by a number of Speeded-Up Robust Features (SURF), matched between the current image and the reference images. In this paper, three methods of self-localization are proposed and compared. After performing self-localization, the quadrotor computes appearance-based motion control commands (desired yaw and height) for the next movement in order to keep track of the desired route. This computation is developed on Funnel Lane theory, which was originally proposed in Chen and Birchfield (IEEE Trans. Robot. 25(3), 749---754 (11)) for 2D navigation of a ground vehicle. The paper extends this theory to 3D navigation of a quadrotor.The proposed self-localization methods are tested with several image databases. Finally, an online experiment of proposed VT&R technique is demonstrated using Ar.Drone quadrotor model.

In order to properly function in real-world environments, the gait of a humanoid robot must be able to adapt to new situations as well as to deal with unexpected perturbations. A promising research direction is the modular generation of movements that results from the combination of a set of basic primitives. In this paper, we present a robot control framework that provides adaptive biped locomotion by combining the modulation of dynamic movement primitives (DMPs) with rhythm and phase coordination. The first objective is to explore the use of rhythmic movement primitives for generating biped locomotion from human demonstrations. The second objective is to evaluate how the proposed framework can be used to generalize and adapt the human demonstrations by adjusting a few open control parameters of the learned model. This paper contributes with a particular view into the problem of adaptive locomotion by addressing three aspects that, in the specific context of biped robots, have not received much attention. First, the demonstrations examples are extracted from human gaits in which the human stance foot will be constrained to remain in flat contact with the ground, forcing the "bent-knee" at all times in contrast with the typical straight-legged style. Second, this paper addresses the important concept of generalization from a single demonstration. Third, a clear departure is assumed from the classical control that forces the robot's motion to follow a predefined fixed timing into a more event-based controller. The applicability of the proposed control architecture is demonstrated by numerical simulations, focusing on the adaptation of the robot's gait pattern to irregularities on the ground surface, stepping over obstacles and, at the same time, on the tolerance to external disturbances.

This paper focuses on unmanned aircraft guidance laws for a straight path and a circular orbit following using the vector field approach. The vector fields introduced in this paper can be applied to not only path following, but also other purposes such as arrival position, angle, and time control. Therefore, they could be applied to various missions providing advantages over other previous vector fields. Stability and performance of the path following has been proved and analyzed using the classical control theory. Simulation using a six degrees-of-freedom aircraft model shows that these guidance laws are effective for the missions even under the existence of wind disturbance. Furthermore, flight experiments with a blended-wing-body type unmanned aircraft are performed to evaluate the proposed vector field guidance algorithm.

This paper explores the use of a learning algorithm in the "guarding a territory" game. The game occurs in continuous time, where a single learning invader tries to get as close as possible to a territory before being captured by a guard. Previous research has approached the problem by letting only the guard learn. We will examine the other possibility of the game, in which only the invader is going to learn. Furthermore, in our case the guard is superior (faster) to the invader. We will also consider using models with non-holonomic constraints. A control system is designed and optimized for the invader to play the game and reach Nash Equilibrium. The paper shows how the learning system is able to adapt itself. The system's performance is evaluated through different simulations and compared to the Nash Equilibrium. Experiments with real robots were conducted and verified our simulations in a real-life environment. Our results show that our learning invader behaved rationally in different circumstances.