Robotic motion planning and reactive collision avoidance using circular fields
Creators: name: Becker, Marvin
Publisher: name: Hannover : Institutionelles Repositorium der Leibniz Universität Hannover
Year: 2024
Subjects: subject: Autonomous Robotic Systems
subject: Motion Planning
subject: Collision Avoidance
subject: Robotics
subject: Autonome Roboter
subject: Bewegungsplanung
subject: Kollisionsvermeidung
subject: Robotik
subject: Dewey Decimal Classification::600 | Technik::620 | Ingenieurwissenschaften und Maschinenbau::621 | Angewandte Physik::621,3 | Elektrotechnik, Elektronik

description: In recent years, industrial automation has experienced a paradigm shift towards closer interaction and collaboration between humans and robots, driven by technological advances and the growing market for collaborative robots. As robots now operate in close proximity to humans, often without the constraints of spatial safety barriers, the demands on motion planning have increased substantially. Traditional motion planning approaches are reaching their limits in these uncertain and dynamically changing environments, emphasizing the need for novel reactive motion planning algorithms with a major focus on safety. Consequently, the primary objective of this thesis is to develop a comprehensive robotic motion planning framework that bridges the gap between global trajectory planning and reactive control. This framework aims to provide rigorous mathematical guarantees for collision avoidance and goal convergence, validated through extensive simulations and real-world experiments. To achieve this goal, we extend and enhance the circular fields motion planning approach to serve as a robust foundation for the locally reactive planning component. Inspired by the principles of electromagnetic fields, circular fields are inherently free of local minima and enable immediate reactions to dynamic obstacles and changing environmental conditions. We introduce several global planning components tightly integrated with the local control unit, which are augmented with virtual predictive agents for efficient global exploration, and leverage global information about promising avoidance directions. Our framework heavily exploits the inherent parallelizability of these combined approaches, ensuring that the reactive command calculation of the circular field-based local controller remains unaffected by potential delays in global planning processes. Furthermore, our rigorous mathematical analysis verifies the collision avoidance properties of the framework, providing formal guarantees for collision avoidance and goal convergence in planar environments. Specifically, we show that a collision with a static point obstacle in the provided planar environment is only possible for initial conditions within a precisely characterized set of measure zero. We proceed to illustrate how these results naturally extend to environments with multiple obstacles, point cloud obstacle representations and when the planner is combined with an additional goal force. This is done by intermediately showing robustness with respect to additional bounded disturbances. Extensive evaluations and comparative analyses are conducted on several classes of robotic systems, including point mass robots, mobile soccer robots, collaborative robots, and dual-arm robotic systems. These simulations highlight the framework's efficacy in dynamic settings, showcasing its ability to generate smooth trajectories around arbitrarily shaped obstacles and its broad applicability across various scenarios. Finally, experimental verification in complex real-world environments demonstrates the framework's capability for reactive real-time computation of control signals across diverse applications, further validating its effectiveness in practical scenarios.