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The increasing implementation of autonomous robots in industries and daily life demands the development of a robust control algorithm that enables robots to perform their tasks successfully. Critical tasks such as rescue missions and area exploration require robots to work cooperatively in a formation to accomplish the tasks quickly. Moreover, the existence of obstacles in the environment requires all the robots' to rapidly process environmental changes to maintain the formation pattern. Thus, this paper introduces a distributed robot formation control system using the consensus algorithm that enables a group of robots to establish and maintain formations using only the local information of the robots. Furthermore, an obstacle avoidance algorithm based on the distance and angle between robots and obstacles is introduced to ensure safe navigation for the group of robots. The algorithm's effectiveness is demonstrated by a multi-robot system with randomly generated starting positions and velocities, where it is shown that the robots can agree on the control variables and establish the required formation while also avoiding obstacles in the environment.

Thanks to recent developments in service robotics technologies, precision agriculture (PA) is becoming an increasingly prominent research field, and several studies were made to present and outline how the use of mobile robotic systems can help and improve farm production. In this paper, the integration of a custom-designed mobile base with a commercial robotic arm is presented, showing the functionality and features of the overall system for crop monitoring and sampling. To this aim, the motion planning problem is addressed, developing a tailored algorithm based on the so-called manipulability index, that treats the base and robotic arm mobility as two independent degrees of motion; also developing an open source closed-form inverse kinematics algorithm for the kinematically redundant manipulator. The presented methods and sub-system, even though strictly related to a specific mobile manipulator system, can be adapted not only to PA applications where a mobile manipulator is involved but also to the wider field of assistive robotics.

International audience; This article describes the robotic assistive technologies developed for users of electrically powered wheelchairs, within the framework of the European Union’s Interreg ADAPT (Assistive Devices for Empowering Disabled People Through Robotic Technologies) project. In particular, special attention is devoted to the integration of advanced sensing modalities and the design of new shared control algorithms. In response to the clinical needs identified by our medical partners, two novel smart wheelchairs with complementary capabilities and a virtual reality (VR)-based wheelchair simulator have been developed. These systems have been validated via extensive experimental campaigns in France and the United Kingdom.

In this letter, environmental monitoring by mobile robots is considered where expensive or time-consuming sampling has to be carried out in order to obtain a metamodel of the phenomenon investigated. Due to limited resources, often not only a limited number of samples can be taken, but also the cost and time of the traveled distance between the sample points must be considered. We present an adaptive sampling method that greatly reduces the robot’s travel costs for all common sampling criteria with minimal impact on model accuracy. This is achieved by predicting future sample points based on virtual sampling over a horizon in each iteration of the algorithm and suggesting a next sample point after a cost optimization. The algorithm is simulatively evaluated for application to global exploration and reconstruction of unknown phenomena on a variety of randomly generated phenomena. It is shown that our method vastly outperforms standard adaptive sampling.

[EN] The use of trailers allows robots to increase their load capacity to perform multiple tasks, but their use carries multiple risks. In this research, three metrics are developed to assess the navigability of robots with coupled trailers when moving at low speeds on inclined surfaces: an index that predicts the initiation of rollover in the robot or trailers; another index that estimates the start of the total slip due to the slopes of the terrain, either in the robot or the trailers; and finally, an index that quantifies the robot's ability to address itself and follow a path. These three metrics were developed based on the reaction forces of the wheels with the ground and were validated through simulation and experimental tests using a Skid Steer robot called Lázaro, demonstrating their effectiveness in estimating the risk condition for which they were designed., [ES] El uso de remolques permite a los robots aumentar su capacidad de carga para realizar múltiples tareas, pero su uso conlleva múltiples riesgos. En esta investigación, se desarrollan tres métricas para evaluar la navegabilidad de robots con remolques acoplados cuando se mueven a bajas velocidades sobre superficies inclinadas: un í­ndice que predice el inicio del vuelco en el robot o los remolques; otro í­ndice que estima el inicio del deslizamiento total debido a las inclinaciones del terreno, ya sea en el robot o los remolques; y finalmente, un í­ndice que cuantifica la capacidad del robot para direccionarse y seguir una trayectoria. Estas tres métricas fueron desarrolladas con base en las fuerzas de reacción de las ruedas con el suelo y fueron validados a través de simulación y pruebas experimentales utilizando un robot Skid Steer llamado Lázaro, demostrándose su efectividad al estimar la condición de riesgo para la cual fueron diseñados., Este trabajo ha sido realizado parcialmente gracias al apoyo del Decanato de Investigación de la Universidad Nacional Experimental del Táchira bajo los proyectos No. 01-025-2016 y 01-004-2019.

Robotics has made it possible to change and improve many support processes for vulnerable people in different settings. In recent years, its use has been oriented toward supporting therapeutic interventions of neurodevelopmental disorders (NDD), including attention deficit hyperactivity disorder (ADHD). This review of the literature highlights how advances in robotics have evolved in different scenarios of ADHD treatment, its collaboration with other emerging technologies, its results, its limitations, and the research challenges for the future development of robotics in the field of supporting children with ADHD. The authors conducted a literature review based on the location of keywords ‘robotics’ and several NNDs such as ‘ADHD’, ’Autism Spectrum Disorder (ASD)’, ‘cerebral palsy’, and ‘dementia’ in titles, abstracts, and introduction of scientific articles in the Scopus and Web of Science (WoS) database. The reviewed literature was classified according to the type of therapy supported by the robots, the type of robot and the associated technologies. From this analysis, we can solve the research question: Which types of robots have the potential for specific applications in ADHD treatment? Furthermore, this article shows that despite favorable technical results, robotic technologies that support ADHD therapies require significant improvements in terms of scalability, human-machine interaction, and treatment and processing of acquired information to be applied effectively in real-world therapies. The most significant research challenges are proposed to drive research efforts to develop new approaches to enable robotic assistants to participate in ADHD therapies.

Tento článek představuje cloudovou multimodální architekturu pro interakci člověka s robotem. Architektura se skládá z částí běžících na mobilní platformě a částí běžících na clusterovém serveru. Software na mobilní platformě představuje především Robot Operating System, zprostředkovatel MQTT a klientská strana architektury SpeechCloud, která zajišťuje komunikaci s výpočetním clusterem. Její podrobný popis je součástí článku. Hlavním přínosem článku je návrh architektury založené na standardních rámcích, protokolech a zprávách ve validovaném formátu JSON. Článek se také zaměřuje na popis testované mobilní platformy vybavené počítačem rodiny NVIDIA Jetson a informace o úkolech, které bude robot schopen pomocí navržené architektury vykonávat This paper presents a cloud-based multi-modal human-robot interaction architecture. The architecture consists of parts running on the edge (mobile) platform and cluster services. Software on the mobile platform is represented primarily by a Robot Operating System, an MQTT broker and the client side of the SpeechCloud architecture, which provides the communication with the computing cluster. Its detailed description is a part of the paper. The main contribution of the paper is a design of an architecture based on standard frameworks, protocols, and validated JSON messages. The paper also focuses on the description of a tested mobile platform equipped with an NVIDIA Jetson family computer and information about tasks that the robot will be able to perform using the proposed architecture

Coordination of robot swarms has received significant research interest over the last decade due to its wide real-world applications including precision agriculture, target surveillance, planetary exploration, etc. Many of these practical activities can be formulated as a formation tracking problem. This brief aims to design a robust control strategy for networked robot swarms subjected to nonlinear dynamics and unknown disturbances. Firstly, a robust adaptive formation coordination protocol is proposed for robot swarms, which utilizes only local information for tracking a dynamic target with uncertain maneuvers. A rigorous theoretical proof utilizing the Lyapunov stability approach is then provided to guarantee the control performance. Towards the end, real-time hardware experiments with wheeled mobile robots are conducted to validate the robustness and feasibility of the proposed formation coordination approach.

In this paper, the path planning problem for an unicycle-like mobile robot is considered. By using some results borrowed from algebraic geometry, a technique is given to determine a dynamical system that is affine in the input and whose trajectories tend to a chosen algebraic set independently of the control input. Since this does not guarantee that the corresponding paths of motion are collision free, an optimal control problem is formulated to enforce this behavior, and its approximate solution is determined via integral reinforcement learning. Finally, it is shown how such results can be used to derive a feedback control law for unicycle-like mobile robots.

This paper investigates human–robot collaboration in a novel setup: a human helps a mobile robot that can move and navigate freely in an environment. Specifically, the human helps by remotely taking over control during the learning of a task. The task is to find and collect several items in a walled arena, and Reinforcement Learning is used to seek a suitable controller. If the human observes undesired robot behavior, they can directly issue commands for the wheels through a game joystick. Experiments in a simulator showed that human assistance improved robot behavior efficacy by 30% and efficiency by 12%. The best policies were also tested in real life, using physical robots. Hardware experiments showed no significant difference concerning the simulations, providing empirical validation of our approach in practice.

Internet of production : fundamentals, applications and proceedings / editors: Christian Brecher, Günther Schuh, Wil van der Aalst, Matthias Jarke, Frank T. Piller, Melanie Padberg; Cham : Springer International Publishing, Interdisciplinary Excellence Accelerator Series : IDEAS 1-26 (2023). doi:10.1007/978-3-030-98062-7_13-1, Published by Springer International Publishing, Cham

In this article, we present an online scalable tele-impedance framework, which enables the individual and collaborative control of multiple different robotic platforms. The framework provides an intuitive low-cost interface with visual feedback and a SpaceMouse, through which the operator can define the desired task-level trajectories and impedance profiles. With a simple mouse click, the user can switch between the robots and the collaborative operation mode. The control, subsequently, manages the distribution of the required parameters into the involved robots. Thanks to the introduced virtual hand concept, where each robot is defined as a finger, new robots can be easily added or removed via their kinodynamic parameters. The proposed framework was evaluated with three different experiments: a simulated auscultation on a mock-up patient, a cooperative task where a robot drives the patient on a wheelchair and a different robot performs the auscultation, and a collaborative task where two robots relocate a container. The results demonstrate the capabilities of the framework in terms of adaptability to different robotic platforms, the number of robots involved, and the task requirements. Additionally, quantitative and subjective analysis of 12 subjects showed how the developed interface, even in the presence of inaccurate visual feedback, allowed a smooth and accurate execution of the tasks.

In recent years, formation control has received a great deal of attention as one of the most interesting issues in multiple mobile robots systems. In variable formation control systems, multiple mobile vehicles can form an appropriate formation from a list of possible formations, such that all mobile vehicles can pass even when the width of the course is narrow, and simultaneously the vehicles maintain a set distance between the course and each vehicle. This paper presents a predictive‐based automatic formation control. In our formation control system, a unique formation is determined from feasible formations at any given sampling point. This control is formulated by its corresponding mixed‐integer quadratic programing by introducing binary variables that are used to specify the formation. The effectiveness of the proposed methods is verified by applying two‐wheeled mobile vehicles through simulations and experiments.

This work was supported by the King Abdullah University of Science and Technology (KAUST). The work of Samet Guler was also supported by the TUB.ITAK 2232 International Fellowship for Outstanding Researchers Program. Robots in swarms take advantage of localization infrastructure, such as a motion capture system or global positioning system (GPS) sensors to obtain their global position, which can then be communicated to other robots for swarm coordination. However, the availability of localization infrastructure needs not to be guaranteed, e.g., in GPS-denied environments. Likewise, the communication overhead associated with broadcasting locations may be undesirable. For reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, we propose an onboard relative localization framework for multirobot systems. The setup consists of an anchor robot with three onboard ultrawideband (UWB) sensors and a tag robot with a single onboard UWB sensor. The anchor robot utilizes the three UWB sensors to estimate the tag robot's location by using its onboard sensing and computational capabilities solely, without explicit interrobot communication. Because the anchor UWB sensors lack the physical separation that is typical in fixed UWB localization systems, we introduce filtering methods to improve the estimation of the tag's location. In particular, we utilize a mixture Monte Carlo localization (MCL) approach to capture maneuvers of the tag robot with acceptable precision. We validate the effectiveness of our algorithm with simulations as well as indoor and outdoor field experiments on a two-drone setup. The proposed mixture MCL algorithm yields highly accurate estimates for various speed profiles of the tag robot and demonstrates superior performance over the standard particle filter and the extended Kalman filter. King Abdullah University of Science & Technology TUB.ITAK 2232 International Fellowship for Outstanding Researchers Program

Spatiotemporal maps are data-driven estimates of time changing phenomena. For environmental science, rather than collect data from an array of static sensors, a mobile sensor platform could reduce setup time and cost, maintain flexibility to be deployed to any area of interest, and provide active feedback during observations. While promising, mapping is challenging with mobile sensors because vehicle constraints limit not only where, but also when observations can be made. By assuming spatial and temporal correlations in the data through kernel functions, this paper uses Gaussian process regression (GPR) to generate a maximum likelihood estimate of the phenomenon while also tracking the estimate uncertainty. Spatiotemporal mapping by GPR is simulated for a single fixed- path mobile robot observing a latent spatiotemporal scalar field. The learned spatiotemporal map captures the structure of the latent scalar field with the largest uncertainties in areas the robot never visited.

Learning from Demonstration is a powerful method that allows robots to acquire skills from humans through imitation. There are several strategies in the literature for teaching robots manipulation tasks. However, the aim of this paper is to propose a preliminary study on the possibility of teaching a mobile robot navigation tasks using a single demonstration. To this end, we employed a framework, based on Dynamic Movement Primitives, which has already been proven effective in manipulation tasks. We tried to adapt it to our scenario, taking into account the differences between the two types of tasks. Our approach was validated in a simulated environment with a mobile base equipped with a 2-D laser scan. Preliminary results seem to confirm that it is possible to extend that framework to navigation tasks.

In this paper, we suggest an implementation of non-commutative logic and apply its operators for decision-making in a group of autonomous agents. The suggested operators extend the uninorm and absorbing norm aggregators and use an additional asymmetry parameter that defines the “level of non-commutativity”. The value of this parameter is specified using the perception bias of humans measured in the experiments. The suggested operators and decision-making method are illustrated by the simulated behavior of mobile robots in the group, which verified the possibility of processing systematic sensing errors, as well as of distinguishing and mimicking the biased decisions.

The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic Situational Awareness by discussing interesting recent research directions., Comment: 22 pages, 8 figures, 5 Tables

In this study, a microscopic model for a swarm of mobile robots is developed to implement self-organized aggregation behavior. The proposed model relies on the concept of subjective expectation, which is defined as the “minimum wished cluster size” of a robot in the swarm. During the whole process, a robot’s expectation constantly changes based on context awareness. This awareness is obtained by employing a low-cost communication system commonly found in swarm robot studies: infrared-based communication. Robots can make their own decisions by comparing their expected and estimated observed cluster sizes, which leads to macroscopic swarm aggregation. However, due to the limitations of local communication and mobility, robots are restricted in their ability to perceive global information, particularly regarding cluster size. Inspired by the slime mold aggregation process, a wave-based communication mechanism is implemented to help robots estimate a cluster size. Moreover, each transmission includes a modulated message that allows robots to explicitly share their knowledge with others. In this way, despite the fact that the robot may not belong to that cluster due to its perception range, it can easily grasp the cluster size when passing the cluster. Once a robot detects a desired cluster, it can approach this cluster with its direction determined by using average origin of wave (AOW) method. The performance of the aggregation algorithm was tested both in simulation and with a real swarm robot. Dispersion metrics and cluster metrics were employed to evaluate the proposed algorithm’s performance. The results show that the proposed microscopic model utilizes collective behavior which aggregates all randomly distributed robots into a single aggregate cluster with a reasonable swarm density and evaluation time.

Efficient and collision-free pathfinding, between source and destination locations for multi-Unmanned Aerial Vehicles (UAVs), in a predefined environment is an important topic in 3D Path planning methods. Since path planning is a Non-deterministic Polynomial-time (NP-hard) problem, metaheuristic approaches can be applied to find a suitable solution. In this study, two efficient 3D path planning methods, which are inspired by Incremental Grey Wolf Optimization (I-GWO) and Expanded Grey Wolf Optimization (Ex-GWO), are proposed to solve the problem of determining the optimal path for UAVs with minimum cost and low execution time. The proposed methods have been simulated using two different maps with three UAVs with diverse sets of starting and ending points. The proposed methods have been analyzed in three parameters (optimal path costs, time and complexity, and convergence curve) by varying population sizes as well as iteration numbers. They are compared with well-known different variations of grey wolf algorithms (GWO, mGWO, EGWO, and RWGWO). According to path cost results of the defined case studies in this study, the I-GWO-based proposed path planning method (PPI-GWO) outperformed the best with %36.11. In the other analysis parameters, this method also achieved the highest success compared to the other five methods. WOS:000682214900003 Q2

In obstacle spatial path planning, the traditional A* algorithm has the problem of too many turning points and slow search speed. With this in mind, a path planning method that improves the A* (A-Star) algorithm is proposed. The mobile robot platform was equipped with a lidar and inertial measurement unit (IMU). The Hdl_graph_slam mapping algorithm was used to construct a two-dimensional grid map, and the improved A* algorithm was used for path planning of the mobile robot. The algorithm introduced the path smoothing strategy and safety protection mechanism, and it eliminated redundant points and minimal corner points by judging whether there were obstacles in the connection of two path nodes. The algorithm effectively improved the smoothness of the path and facilitated the robot to move in the actual operation. It could avoid the wear of the robot by expanding obstacles and improving the safety performance of the robot. Subsequently, the algorithm introduced the steering cost model and the adaptive cost function to improve the search efficiency, making the search purposeful and effective. Lastly, the effectiveness of the proposed algorithm was verified by experiments. The average path search time was reduced by 13%. The average search extension node was reduced by 11%. The problems of too many turning points and slow search speed of traditional A* algorithm in path planning were improved.

Deep reinforcement learning-based mobile robot navigation has attracted some recent interest. In the single-agent case, a robot can learn to navigate autonomously without a map of the environment. In the multi-agent case, robots can learn to avoid collisions with each other. In this work, we propose a behavior-based mobile robot navigation method which directly maps the raw sensor data and goal information to the control command. The learned navigation policy can be applied in both single-agent and multi-agent scenarios. Two basic navigation behaviors are considered in our method, which are goal reaching and collision avoidance. The two behaviors are fused based on the risk-level estimation of the current state. The navigation task is decomposed using the behavior-based framework, which is capable of reducing the complexity of the learning process. The simulations and real-world experiments demonstrate that the proposed method can enable the collision-free autonomous navigation of multiple mobile robots in unknown environments.

This paper presents an exciting and meaningful design to make mobile robots capable of adapting to various terrains. We designed a relatively simple and novel composite motion mechanism called the flexible spoked mecanum (FSM) wheel and created a mobile robot, LZ-1, with multiple motion modes based on the FSM wheel. Based on the motion analysis of the FSM wheel, we designed an omnidirectional motion mode for this robot, allowing it to move flexibly in all directions and successfully traverse rugged terrains. In addition, we designed a crawl motion mode for this robot, which can climb stairs effectively. We used a multilayer control method to move the robot according to the designed motion modes. Multiple experiments showed that these two motion modes for the robot are effective on various terrains.

Model mismatch is inevitable in robot control due to the presence of unknown dynamics and unknown perturbations. Traditional model predictive control algorithms are usually based on constant value assumptions and are not able to overcome the degradation of controller performance due to model mismatch. In this paper, a model predictive control (MPC) algorithm based on Gaussian process regression (GPR) is proposed. Firstly, the kinematic equations of the mobile robot are established by the mechanistic analysis method; similarly, the dynamics of the mobile robot system are modeled using the second-class Lagrangian equations. Secondly, the problem of stability and reliability degradation due to model mismatch during the operation of mobile robot is considered. This paper uses a MPC algorithm with a main model plus residual model to solve the MPC closed-loop control strategy. The state at each moment is decomposed into a predicted state based on the first-principles model and a residual state. The residual state is learned by GPR in real-time and used to compensate for deviations between the real process model and the predicted model. The proposed method requires fewer data samples, enhancing the technique’s practicality. Finally, the simulation results show that the proposed algorithm is more stable and achieves the desired tracking faster. Compared with the MPC algorithm, the arrival time of the system is reduced by 28% and the speed error is controlled within 0.07.

A key research topic in the field of robotics is the formation control of a group of robots in trajectory tracking problems. Using organized robots has many advantages over using them individually, such as efficient use of resources, increased reliability due to cooperation, and better resistance against defects. To achieve this, a controller is proposed that steers the leader robot and subsequent follower robots asymptotically to a reference trajectory. The basic controller is feedback linearization. To ensure stability against perturbations, a compensator based on type-3 fuzzy logic systems (T3-FLSs) and a data-driven control strategy is designed. The approach involves employing a finite number of open-loop data and using the model-based predictive controller (MPC) approach to acquire sufficient criteria for stability. An infinite-horizon function is minimized online, which allows the data-based control policy to be considered the optimal control method. The gains of the constrained data-based control signal are computed at each time step to enhance accuracy. Applying the data-based state feedback controller to the system yields positive and stable state trajectories with appropriate transient responses. The suggested data-driven compensator is guaranteed to handle constraints. A practical example is simulated to evaluate the proposed strategy.

Significant developments have been made in the navigation of autonomous mobile robots within indoor environments; however, there still remain challenges in the face of poor map construction accuracy and suboptimal path planning, which limit the practical applications of such robots. To solve these challenges, an enhanced Rao Blackwell Particle Filter (RBPF-SLAM) algorithm, called Lidar-based RBPF-SLAM (LRBPF-SLAM), is proposed. In LRBPF, the adjacent bit poses difference data from the 2D Lidar sensor which is used to replace the odometer data in the proposed distribution function, overcoming the vulnerability of the proposed distribution function to environmental disturbances, and thus enabling more accurate pose estimation of the robot. Additionally, a probabilistic guided search-based path planning algorithm, gravitation bidirectional rapidly exploring random tree (GBI-RRT), is also proposed, which incorporates a target bias sampling to efficiently guide nodes toward the goal and reduce ineffective searches. Finally, to further improve the efficiency of navigation, a path reorganization strategy aiming at eliminating low-quality nodes and improving the path curvature of the path is proposed. To validate the effectiveness of the proposed method, the improved algorithm is integrated into a mobile robot based on a ROS system and evaluated in simulations and field experiments. The results show that LRBPF-SLAM and GBI-RRT perform superior to the existing algorithms in various indoor environments.

This paper presents an architecture for controlling a quadrotor transporting a cable-suspended load of uncertain mass. A family of trajectories is proposed that is composed by three phases—lift-off, transit, and landing—and implemented as a hybrid system. The proposed control system uses an adaptive geometric controller with asymptotic tracking stability. The mass of the transported load was estimated using an adaptive mechanism, which adjusts the action resorting to a geometric control method. The resulting system was validated in simulation with a mid-flight mass reduction of the transported load, and tests were performed using a range of values of load mass and maximum forward velocity. There is work in the literature that approaches the cable-suspended component of the problem, and there are also papers focused on uncertainty in the model, mass included. This work aimed to solve these two problems simultaneously, having the uncertain component being the mass of the suspended load.

In this paper, simulation and experimental tests are reported for a hybrid robot being used for indoor and outdoor inspections. Automatic or tele-operated surveys can be performed by mobile robots, which represent the most efficient solution in terms of power consumption, control, robustness, and overall costs. In the context of structures and infrastructure inspection, robots must be able to move on horizontal or sloped surfaces and overpass obstacles. In this paper, the mechatronic design, simulations, and experimental activity are proposed for a hybrid robot being used for indoor and outdoor inspections, when the environmental conditions do not allow autonomous navigation. In particular, the hybrid robot is equipped with external and internal sensors to acquire information on the main structural elements, avoiding the need for experienced personnel being directly inside the inspection site, taking information from the environment and aiding the pilot to understand the best maneuvers/decisions to take. Given the current state of research and shortcomings worldwide, this paper discusses inspection robots taking into account the main issues in their use, functionality and standard systems, and how internal sensors can be set in order to improve inspection robots’ performances. On this basis, an illustrative study case is proposed.

Humans can express their emotions with not only facial expressions or gestures but also whole-body motion, even while walking, which are essential for our interpersonal relationships and our interaction with others. Communication with people is also considered an important task for robots, however emotional expressions by wheeled mobile humanoid robots when moving has not been well studied. In this paper, we implemented emotional human-like gait-induced upper body motion in a mobile android ibuki. We hypothesize that the gait-induced upper body motion with vertical oscillation enhances the human perception of a mobile robot’s emotional expressions. ibuki is a mobile robot, which has a vertical oscillation mechanism, which enables the robot to move its body with a human gait-induced upper body motion. We ran two experiments in which participants watched videos of ibuki moving in a room expressing different emotions - anger, happiness, and sadness–, before answering how they perceived the emotion and their respective confidence level in their answer. Our results show that for motions with vertical oscillation, recognition rates were: 56.0 % for anger, 77.7 % for happiness, and 97.0 % for sadness. We also found that the recognition rate and confidence level of motions expressing happiness with vertical oscillation were higher. Our results play an important role in opening up avenues for more natural ways of interaction between humans and robots. For example, by approaching humans closer while expressing emotions, a robot can naturally provide an opportunity to get started conversation with them.

This paper presents the design and implementation of COVID-Bot, an open-source robotic platform for sanitizing single plant environments such as offices, houses, apartments, among others. This development seeks to create a tool that contributes to the global fight against the COVID-19 pandemic, from a low-cost and easy-to-replicate robot, which disinfects surfaces through type C ultraviolet radiation. The platform is based on a differential robotic base, an RGB-D camera, a tracking camera, three UV-C lamps, and an embedded computer running the ROS-based control software. In addition, this paper presents the description of the hardware used, the software implemented, and the tests carried out to corroborate the operation of the integrated system. These tests demonstrated that the system is adequate to autonomously cover a one-floor apartment, based on the theoretical radiation distance of the used lamps.

This study proposes a heterogeneous modular reconfigurable robot called SABER (short for Step, Assembler, and Bridge Explorer Robot) that is suitable for future autonomous extraterrestrial missions. SABER comprises a platform and a reconfigurable rail and can operate in monowheel, rail trolley, and manipulator configurations. The monowheel configuration provides locomotion at a top speed of 10 km/h. The rail trolley configuration allows the robot to traverse gaps whose length is greater than its wheel diameter, climb steps whose height is no taller than the wheel radius, and pass through passages narrower than the wheel diameter. The manipulator configuration enables the robot to manipulate objects using two robotic arms with changeable tools. This design allows several SABERs to work as a team to enlarge their working area or enhance their mechanical capabilities. As such, SABERs can undertake various tasks in extraterrestrial missions, including exploration, outpost building and equipment maintenance. The key feature of the SABER is its mechanical transmission that allows its platform to move rapidly along a rail made from the manipulators of the robot. We also formulated the novel concept of a jamming safety diagram that is used to determine controller precision requirements to prevent the transmission from jamming. Finally, a new positioning controller meeting those requirements was introduced and implemented as field-programmable-gate-array firmware that is compatible with a wide range of low-cost integrated circuits. In numerical simulations, we evaluated SABER with respect to its speed, acceleration, oscillation, and ability to negotiate gaps and steps; SABER performed as well as or better than existing autonomous mobile robots and modular reconfigurable robots.

Wheeled skid-steered vehicles have the advantages of compact structure, small steering radius and wide application. However, poor steering performance is an important limitation restricting the development of wheeled skid-steered vehicles. A kinematic model for analysing the relationship between wheels’ rotation speed and the motion state of the vehicles’ mass centre based on Newton's second law was established, then the correctness of the kinematic model was verified by comparing the calculation results with simulation results. The key design perimeters affecting the steering performance were analysed by a control-variable method based on the established kinematic model. The results indicate that the established kinematic model can evaluate the steering performance of skid-steered vehicles correctly and can provide a reference for optimising the design of the vehicles.

In this paper, a novel path planning algorithm with Reinforcement Learning is proposed based on the topological map. The proposed algorithm has a two-level structure. At the first level, the proposed method generates the topological area using the region dynamic growth algorithm based on the grid map. In the next level, the Multi-SARSA method divided into two layers is applied to find a near-optimal global planning path, in which the artificial potential field method, first of all, is used to initialize the first Q table for faster learning speed, and then the second Q table is initialized with the connected domain obtained by topological map, which provides the prior information. A combination of the two algorithms makes the algorithm easier to converge. Simulation experiments for path planning have been executed. The results indicate that the method proposed in this paper can find the optimal path with a shorter path length, which demonstrates the effectiveness of the presented method.

Mobile robots need to move in the real world for the majority of tasks. Their control is often intertwined with the tasks they have to solve. Unforeseen events must have an adequate and prompt reaction, in order to solve the corresponding task satisfactorily. A robust system must be able to respond to a variety of events with specific solutions and strategies to keep the system running. Robot navigation control systems are essential for this. In this thesis we present a robot navigation control system that fulfills these requirements: Move Base Flex. Furthermore, the map representation used to model the environment is essential for path planning. Depending on the representation of the map, path planners can solve problems like simple 2D indoor navigation, but also complex rough terrain outdoor navigation with multiple levels and varying slopes, if the corresponding representation can model them accurately. With Move Base Flex, we present a middle layer navigation framework for navigation control, that is map independent at its core. Based on this, we present the Mesh Navigation Stack to master path planning in complex outdoor environments using a developed mesh map to model surfaces in 3D. Finally, to solve path planning in complex outdoor environments, we have developed and integrated the Continuous Vector Field Planner with the aforementioned solutions and evaluated it on five challenging and complex outdoor datasets in simulation and in the real-world. Beyond that, the corresponding developed software packages are open source available and have been released to easily reproduce the provided scientific results.

Наведені результати моделювання процесу переміщення крокуючого робота довільної орієнтації. Відмінність наданої динамічної моделі педіпуляторів мобільного робота полягає в тому, що переміщення робота по поверхні будь-якої орієнтації в просторі здійснюється завдяки перетворенню накопиченої потенціальної енергії в кінетичну енергію руху. Такий підхід до керування крокуючими роботами дозволяє заощадити до 30…40% енергоресурсів робота та спрямувати їх на виконання технологічних операцій. Стаття містить системи диференціальних рівнянь руху робота, що розв‘язувались числовим методом Рунге-Кутта четвертого порядку точності, завдяки чому отримані графоаналітичні залежності між конструктивними параметрами робота для подальшої побудови інженерної методики проектування крокуючих роботів довільної орієнтації, що містять пристрої накопичення потенціальної енергії та перетворення її в кінетичну енергію руху робота. The results of modelling the movement of a walking robot of arbitrary orientation are presented. The difference between the presented dynamic models of the mobile robot is that the movement of the robot on the surface of any orientation in space is realized due to the transformation of the accumulated potential energy into kinetic energy of motion. This approach to controlling walking robots can save up to 30 ... 40% of the robot's energy resources and direct them to perform technological operations. The article contains systems of differential equations of motion of the robot, which were solved by the numerical Runge-Kutta method of the fourth order of accuracy. As a result, graph-analytical relationships between the constructive parameters of the walking robot are obtained. This will create an engineering technique for designing walking robots of arbitrary orientation, which contain devices for accumulating potential energy and converting it into kinetic energy of the robot's motion. Приведены результаты моделирования процесса перемещения шагающего робота произвольной ориентации. Отличие представленной динамической модели педипуляторов мобильного робота заключается в том, что перемещение робота по поверхности любой ориентации в пространстве осуществляется благодаря преобразованию накопленной потенциальной энергии в кинетическую энергию движения. Такой подход к управлению шагающими роботами позволяет сэкономить до 30…40% энергоресурсов робота и направить их на выполнение технологических операций. Статья содержит системы дифференциальных уравнений движения робота, которые решались числовым методом Рунге-Кутта четвертого порядка точности, благодаря чему получены графоаналитические зависимости между конструктивными параметрами робота для дальнейшего построения инженерной методики проектирования шагающих роботов произвольной ориентации, которые содержат устройства накопления потенциальной энергии и превращение ее в кинетическую энергию движения робота.

The unmanned warehouse dispatching system of the ‘goods to people’ model uses a structure mainly based on a handling robot, which saves considerable manpower and improves the efficiency of the warehouse picking operation. However, the optimal performance of the scheduling system algorithm has high requirements. This study uses a deep Q-network (DQN) algorithm in a deep reinforcement learning algorithm, which combines the Q-learning algorithm, an empirical playback mechanism, and the volume-based technology of productive neural networks to generate target Q-values to solve the problem of multi-robot path planning. The aim of the Q-learning algorithm in deep reinforcement learning is to address two shortcomings of the robot path-planning problem: slow convergence and excessive randomness. Preceding the start of the algorithmic process, prior knowledge and prior rules are used to improve the DQN algorithm. Simulation results show that the improved DQN algorithm converges faster than the classic deep reinforcement learning algorithm and can more quickly learn the solutions to path-planning problems. This improves the efficiency of multi-robot path planning.

In this work, we present an optimization-based trajectory tracking solution for multirotor aerial robots given a geometrically feasible path. A trajectory planner generates a minimum-time kinematically and dynamically feasible trajectory that includes not only standard restrictions such as continuity and limits on the trajectory, constraints in the waypoints, and maximum distance between the planned trajectory and the given path, but also restrictions in the actuators of the aerial robot based on its dynamic model, guaranteeing that the planned trajectory is achievable. Our novel compact multi-phase trajectory definition, as a set of two different kinds of polynomials, provides a higher semantic encoding of the trajectory, which allows calculating an optimal solution but following a predefined simple profile. A Model Predictive Controller ensures that the planned trajectory is tracked by the aerial robot with the smallest deviation. Its novel formulation takes as inputs all the magnitudes of the planned trajectory (i.e. position and heading, velocity, and acceleration) to generate the control commands, demonstrating through in-lab real flights an improvement of the tracking performance when compared with a controller that only uses the planned position and heading. To support our optimization-based solution, we discuss the most commonly used representations of orientations, as well as both the difference as well as the scalar error between two rotations, in both tridimensional and bidimensional spaces $SO(3)$ and $SO(2)$. We demonstrate that quaternions and error-quaternions have some advantages when compared to other formulations.

Summarization: Fish-cage dysfunction in aquaculture installations can trigger significant negative consequences affecting the operational costs. Low oxygen levels, due to excessive fooling's, leads to decrease growth performance, and feed efficiency. Therefore, frequent periodic inspection of fish-cage nets is required, but this task can become quite expensive with the traditional means of employing professional divers that perform visual inspections at regular time intervals. The modern trend in aquaculture is to take advantage of IT technologies with the use of a small-sized, low-cost autonomous underwater vehicle, permanently residing within a fish cage and performing regular video inspection of the infrastructure for the entire net surface. In this study, we explore specialised image processing schemes to detect net holes of multiple area size and shape. These techniques are designed with the vision to provide robust solutions that take advantage of either global or local image structures to provide the efficient inspection of multiple net holes. Presented on: IET Image Processing

Teleoperation provides a promising way for human–robot collaboration in the unknown or unstructured environments to perform a cooperative task. It enables humans to complete a task at a remote side and combines both the human's intelligence and the robots’ capabilities in a collaborative task. Therefore, it is necessary to conduct cross researches in terms of robotics, artificial intelligence, sensors, and mechatronics. This study covers the state-of-the-arts research in terms of perception, control, and learning. In this review, key technologies about information perception mechanism, control algorithms, human–robot interface, and robot learning strategies for teleoperation are introduced. Then, a comprehensive survey is summarised in representative applications in teleoperation, and the existing challenges and potential directions of the development are discussed.

Wireless communications is nowadays an important aspect of robotics. There are many applications in which a robot must move to a certain goal point while transmitting information through a wireless channel which depends on the particular trajectory chosen by the robot to reach the goal point. In this context, we develop a method to generate optimum trajectories which allow the robot to reach the goal point using little mechanical energy while transmitting as much data as possible. This is done by optimizing the trajectory (path and velocity profile) so that the robot consumes less energy while also offering good wireless channel conditions. We consider a realistic wireless channel model as well as a realistic dynamic model for the mobile robot (considered here to be a drone). Simulations results illustrate the merits of the proposed method., IEEE Transactions on Robotics, Vol. 36, No. 2, pp. 431-442, April 2020

Robotic automation has proliferated various industrial deployments including manufacturing, retail warehousing and logistics supply chains. In order for robots to advance to the next stage of cognitive autonomy, a robust framework for planning, execution and adaptation is needed. While there have been advances in abstract automated planning systems, they are still ill-suited to be applied within runtime robotic executions, which take place in uncertain environments. In this study, the authors provide a deliberative robotic planning and simulated execution framework called RoboPlanner that provides a pragmatic integration of automated planning, orchestration and adaptive deployments. This is coupled with an execution monitor and plan repair module, that allows reconfiguration to various template actions with runtime changes. Structured rules for re-planning in the case of state changes, unforeseen obstacles or execution failures are provided. They demonstrate their simulation framework on a realistic example of mobile pick & delivery robots in Industry 4.0 warehouses, that plan, execute, adapt and re-plan in sync with a knowledge base.

Motion planning is a vital module for unmanned aerial vehicles (UAVs), especially in scenarios of autonomous navigation and operation. This survey delivers some recent state-of-the-art UAV motion planning algorithms and related applications. The logic flow of this survey is divided as the path finding, which is the front-end of most motion planning systems, and the trajectory optimisation, which usually serves as the back-end. Motivation, methodology, problem formulation and derivation are given in this survey, in detail. Finally, a section about real-world applications is given, where roles and effectiveness of most popular motion planning methods are revealed.

The performance of a formation control algorithm for a group of autonomous underwater vehicles (AUVs) may be degraded due to communication constraints between the leader and the follower AUVs. Moreover, as the distance between the AUVs change due to ocean waves, the delay in communication also varies. In this study, a modified constrained adaptive controller (CAC) is proposed to resolve the issue of variable-delay as well as actuator saturation. Due to the on-line reduction of the cost function, the gains of the CAC are updated at each sampling time taking into account the effects of communication delay. This improves the path-following accuracy of the leader AUV as well as follower AUVs. The effectiveness of the proposed control algorithm is demonstrated by implementing it on a prototype AUV (ODRA-I) model. The obtained simulation and experimental results show that the follower AUVs maintain a desired distance from the leader AUV to avoid a collision. For the proposed algorithm, the desired path-following performance of the follower AUV is maintained even when the delay is changed from 0 to 5 s. Moreover, the proposed control algorithm is efficient in avoiding actuator saturation.

In order to handle undesirable failures of a multicopter which occur in either the pre-flight process or the in-flight process, a failsafe mechanism design method based on supervisory control theory is proposed for the semi-autonomous control mode. Failsafe mechanism is a control logic that guides what subsequent actions the multicopter should take, by taking account of real-time information from guidance, attitude control, diagnosis, and other low-level subsystems. In order to design a failsafe mechanism for multicopters, safety issues of multicopters are introduced. Then, user requirements including functional requirements and safety requirements are textually described, where function requirements determine a general multicopter plant, and safety requirements cover the failsafe measures dealing with the presented safety issues. In order to model the user requirements by discrete-event systems, several multicopter modes and events are defined. On this basis, the multicopter plant and control specifications are modeled by automata. Then, a supervisor is synthesized by monolithic supervisory control theory. In addition, we present three examples to demonstrate the potential blocking phenomenon due to inappropriate design of control specifications. Also, we discuss the meaning of correctness and the properties of the obtained supervisor. This makes the failsafe mechanism convincingly correct and effective. Finally, based on the obtained supervisory controller generated by TCT software, an implementation method suitable for multicopters is presented, in which the supervisory controller is transformed into decision-making codes., 33 pages, 15 figures

Real-world autonomous systems operate under uncertainty about both their pose and dynamics. Autonomous control systems must simultaneously perform estimation and control tasks to maintain robustness to changing dynamics or modelling errors. However, information gathering actions often conflict with optimal actions for reaching control objectives, requiring a trade-off between exploration and exploitation. The specific problem setting considered here is for discrete-time non-linear systems, with process noise, input-constraints, and parameter uncertainty. This study frames this problem as a Bayes-adaptive Markov decision process and solves it online using Monte Carlo tree search with an unscented Kalman filter to account for process noise and parameter uncertainty. This method is compared with certainty equivalent model predictive control and a tree search method that approximates the QMDP solution, providing insight into when information gathering is useful. Discrete time simulations characterise performance over a range of process noise and bounds on unknown parameters. An offline optimisation method is used to select the Monte Carlo tree search parameters without hand-tuning. In lieu of recursive feasibility guarantees, a probabilistic bounding heuristic is offered that increases the probability of keeping the state within a desired region.

This article proposes a new kind of map for mobile robot localization and its generation method. We call the map a highlighted map, on which uniquely shaped objects (landmarks) in monotonous environments are highlighted. By using this map, robots can use such landmarks as clues for localization, and thus, their localization performance can be improved without having to update their sensors or online computation. Furthermore, this map can be easily combined with many other existing localization algorithms. We formulate the problem of making a highlighted map and propose a numerical optimization method based on reinforcement learning. This optimization method automatically identifies and emphasizes the important landmarks on the map. The generated highlighted map is adapted to situations such as the sensor characteristics and robot dynamics because this method uses the actual sensor measurement data. It is proven that the optimization converges under certain technical assumptions. We performed a numerical simulation and real-world experiment showing that the highlighted map provides better localization accuracy than a conventional map.

Multi-robot systems are becoming widely popular in applications where a rapid response is required or where various different robotic capabilities are required. Applications such as surveillance, or search and rescue, would require an efficient team that can be deployed and optimally dispersed over the environment. This is known as the coverage control problem. The solution to this research optimization problem is affected by several external aspects, such as characteristics of the environment as well as factors that pertain to the robotic team. This work proposes a novel solution to the complete coverage problem where the team of robots is restricted with energy limitations, and must cover an environment that has time-varying regions of importance. Our results show that in a realistic scenario, where the robots have limited energy for the task in question, the proposed solution performs significantly better than a typical coverage algorithm which disregards the energy considerations of the robotic team., peer-reviewed

The manufacturing industry needs to increase productivity and flexibility to stay competitive. This requires more adaptable and versatile production capabilities. It is expected that dynamic systems, consisting of mobile robots, will be particularly prominent in manufacturing environments where it is difficult to move components and products in a flexible manner. This paper compares the relative advantages of a dynamic, mobile robot-based system with traditional dedicated automation systems. The study uses simulations to evaluate several representative scenarios with different product supply bottlenecks, interference among mobile robots and mixes of products inspired by the aerospace industry. The results show that mobility enables higher resource utilisation and increased flexibility. This highlights the potential operational advantages mobile robot-based systems would offer and gives clear justification to continue the development of dynamic, self-organising production systems based on mobile robots.