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Written by two of Europe's leading robotics experts, this book provides the tools for a unified approach to the modelling of robotic manipulators, whatever their mechanical structure.No other publication covers the three fundamental issues of robotics: modelling, identification and control.

An important issue in the field of motion control of wheeled mobile robots is that the design of most controllers is based only on the robot’s kinematics. However, when high-speed movements and/or heavy load transportation are required, it becomes essential to consider the robot dynamics as well. The control signals generated by most dynamic controllers reported in the literature are torques or voltages for the robot motors, while commercial robots usually accept velocity commands. In this context, we present a velocity-based dynamic model for differential drive mobile robots that also includes the dynamics of the robot actuators. Such model has linear and angular velocities as inputs and has been included in Peter Corke’s Robotics Toolbox for MATLAB, therefore it can be easily integrated into simulation systems that have been built for the unicycle kinematics. We demonstrate that the proposed dynamic model has useful mathematical properties. We also present an application of such model on the design of an adaptive dynamic controller and the stability analysis of the complete system, while applying the proposed model properties. Finally, we show some simulation and experimental results and discuss the advantages and limitations of the proposed model.

Designing an urban reconnaissance robot is highly challenging work given the nature of the terrain in which these robots are required to operate. In this work, we attempt to extend the locomotion capabilities of these robots beyond what is currently feasible. The design and unique features of our bio-inspired reconfigurable robot, called Scorpio, with rolling, crawling, and wall-climbing locomotion abilities are presented in this paper. The design of the Scorpio platform is inspired by Cebrennus rechenbergi, a rare spider species that has rolling, crawling and wall-climbing locomotion attributes. This work also presents the kinematic and dynamic model of Scorpio. The mechanical design and system architecture are introduced in detail, followed by a detailed description on the locomotion modes. The conducted experiments validated the proposed approach and the ability of the Scorpio platform to synthesise crawling, rolling and wall-climbing behaviours. Future work is envisioned for using these robots as active, unattended, mobile ground sensors in urban reconnaissance missions. The accompanying video demonstrates the shape shifting locomotion capabilities of the Scorpio robot.

Aiming at the difficulty of dynamic modeling of a hybrid robotic arm, a dynamic model system of industrial robotic arm based on Simscape Multibody was established with the MG400 robotic arm as the research object, which combines the motion control and data acquisition modules. The model is dynamically visualized and provides a convenient platform for studying the control algorithm of the robot arm. In response to the issues of sluggish speed and substantial position error in robot trajectory tracking control of a traditional controller, a variable gain iterative learning control methodology was designed. The robot arm control system model was employed to corroborate the trajectory tracking control under the stipulated target trajectory. The empirical outcomes indicate that in comparison to the traditional controller and the fixed-gain iterative learning controller, the variable-gain iterative learning controller can regulate the robot end trajectory more precisely, with swift tracking speed and accurate tracking posture, demonstrating commendable feasibility and portability. It offers an open-source research and development platform for the dynamic modeling of robot arm and a potent solution for the control strategy of robot arm.

Aerial Robot with the Ability to Assemble in Mid‐Air In article 2300191, Junichiro Sugihara, Moju Zhao, and co‐workers introduce TRADY, an innovative aerial robot. TRADY can autonomously assemble/disassemble mid‐air, enhancing control and torque. The prototype boasts a 90% success rate in the assembly motion, delivering nine times the torque of a single unit when assembled.

Mobile robots, including ground robots, underwater robots, and unmanned aerial vehicles, play an increasingly important role in people’s work and lives. Path planning and obstacle avoidance are the core technologies for achieving autonomy in mobile robots, and they will determine the application prospects of mobile robots. This paper introduces path planning and obstacle avoidance methods for mobile robots to provide a reference for researchers in this field. In addition, it comprehensively summarizes the recent progress and breakthroughs of mobile robots in the field of path planning and discusses future directions worthy of research in this field. We focus on the path planning algorithm of a mobile robot. We divide the path planning methods of mobile robots into the following categories: graph-based search, heuristic intelligence, local obstacle avoidance, artificial intelligence, sampling-based, planner-based, constraint problem satisfaction-based, and other algorithms. In addition, we review a path planning algorithm for multi-robot systems and different robots. We describe the basic principles of each method and highlight the most relevant studies. We also provide an in-depth discussion and comparison of path planning algorithms. Finally, we propose potential research directions in this field that are worth studying in the future.

Legged robots are particularly well-suited for navigating terrains that are inaccessible to wheeled robots, which has led to a significant increase in their applications in recent years. However, many existing control approaches still struggle to achieve generalization in diverse and unstructured environments. In this work, we introduce the MSD training framework for general and agile locomotion of legged robots through a multi-skill distillation approach. First, expert policies are trained for different terrains. Then, using the DAgger algorithm, these expert skills are distilled into a single deployable policy. The proposed policy integrates depth images as exteroceptive observations, enabling agile navigation across diverse and unstructured terrains. Finally, deployment on the MagicDog-W robot validates the policy’s capability to achieve robust and flexible locomotion in challenging environments.

Cement pole docking is an important part of the process of power network construction. To address the problems of existing docking work, this paper proposes a new style docking robot and a cement pole docking scheme with the proposed robot. Based on introducing the mechanical design of the docking robot and docking detection device, the kinematic analysis of the robot is carried out to provide a basis for robot control. Finally, motion simulation with SolidWorks is performed to verify the effectiveness of the robot’s mechanical design.

Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated navigation systems to move mobile robots from one point to another. Despite their overall success, a recently emerging research thrust is devoted to developing machine learning techniques to address the same problem, based in large part on the success of deep learning. However, to date, there has not been much direct comparison between the classical and emerging paradigms to this problem. In this article, we survey recent works that apply machine learning for motion planning and control in mobile robot navigation, within the context of classical navigation systems. The surveyed works are classified into different categories, which delineate the relationship of the learning approaches to classical methods. Based on this classification, we identify common challenges and promising future directions.

Insect flight can be fast and agile, making it hard to study its detailed aerodynamics. Karásek et al. designed an untethered, flapping-wing robot with impressive agility that can mimic fruitfly maneuvers (see the Perspective by Ruffier). They studied the robot's motion during rapid banked turns, which revealed that passive motion through the turn generated yaw torque coupling. This correcting yaw rotation propelled the robot toward the escape heading needed for effective turning. Science , this issue p. 1089 ; see also p. 1073 An untethered, flapping-wing robot with impressive agility is capable of mimicking maneuvers of the fruitfly. Insects are among the most agile natural flyers. Hypotheses on their flight control cannot always be validated by experiments with animals or tethered robots. To this end, we developed a programmable and agile autonomous free-flying robot controlled through bio-inspired motion changes of its flapping wings. Despite being 55 times the size of a fruit fly, the robot can accurately mimic the rapid escape maneuvers of flies, including a correcting yaw rotation toward the escape heading. Because the robot’s yaw control was turned off, we showed that these yaw rotations result from passive, translation-induced aerodynamic coupling between the yaw torque and the roll and pitch torques produced throughout the maneuver. The robot enables new methods for studying animal flight, and its flight characteristics allow for real-world flight missions.