Advances In Motion Sensing And Control For Robotic Applications

Advances in Motion Sensing and Control for Robotic Applications

This book reports on advances in sensing, modeling and control methods for different robotic platforms such as multi-degree of freedom robotic arms, unmanned aerial vehicles and autonomous mobile platforms. Based on 2018 Symposium on Mechatronics, Robotics, and Control (SMTRC’18), held as part of the 2018 CSME International Congress, in York University, Toronto, Canada, the book covers a variety of topics, from filtering and state estimation to adaptive control of reconfigurable robots and more. Next-generation systems with advanced control, planning, perception and interaction capabilities will achieve functionalities far beyond today’s technology. Two key challenges remaining for advanced robot technologies are related to sensing and control in robotic systems. Advanced perception is needed to navigate changing environments. Adaptive and intelligent control systems must be developed to enable operation in unstructured and dynamic environments. Theselected chapters in this book focus on both of the aforementioned areas and highlight the main trends and challenges in robot sensing and control. The first part of the book introduces chapters which focus on advanced perception and sensing for robotics applications. They include sensor filtering and state estimation for bipedal robots and motion capture systems analysis. The second part focuses on different modeling and control methods for robotic systems including flight control for UAVs, multi-variable robust control for modular and reconfigurable robotics and control for precision micromanipulation.

Human-Like Advances in Robotics: Motion, Actuation, Sensing, Cognition and Control

Advanced Motion Control and Navigation of Robots in Extreme Environments

Advances in robotics and autonomous systems have opened new horizons for the scientists by creating new opportunities to explore extreme environments that would previously not have been possible. For example, robots that are deployed to study environmental processes such remote volcanos, monitor the climate variables under the adverse weather conditions, understand underground mines, and explore deep oceans which are all inaccessible or hazardous for the human. Industrial applications can also often be situated in extreme environments such as offshore oil and gas and nuclear industries. In such applications the autonomous robot is expected to complete tasks such as repair and maintenance, exploration, reconnaissance, inspection, and transportation which is either done in isolation or as a team of cooperative robots. Due to the harsh and severe conditions of such environments, designing an advanced robotic system that can endure them is a challenging task. The robot needs to cope with the time-varying, restricted, uncertain, and unstructured nature of the environment to achieve the planning and execution of the tasks. This in turn demands development of advanced, robust and adaptive motion control and navigation algorithms along with machine learning and deep learning algorithms with high cognitive capability for the robot to perceive the surrounding environment effectively. The use of both single and multi-robot platforms can be advantageous depending on the specific application and environment.