Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks

Abstract

For massive large-scale tasks, a multi-robot system (MRS) can effectively improve efficiency by utilizing each robot's different capabilities, mobility, and functionality. In this paper, we focus on the multi-robot coverage path planning (mCPP) problem in large-scale planar areas with random dynamic interferers in the environment, where the robots have limited resources. We introduce a worker-station MRS consisting of multiple workers with limited resources for actual work, and one station with enough resources for resource replenishment. We aim to solve the mCPP problem for the worker-station MRS by formulating it as a fully cooperative multi-agent reinforcement learning problem. Then we propose an end-to-end decentralized online planning method, which simultaneously solves coverage planning for workers and rendezvous planning for station. Our method manages to reduce the influence of random dynamic interferers on planning, while the robots can avoid collisions with them. We conduct simulation and real robot experiments, and the comparison results show that our method has competitive performance in solving the mCPP problem for worker-station MRS in metric of task finish time.

Towards reuse and recycling of lithium-ion batteries: tele-robotics for disassembly of electric vehicle batteries

“…For damaged batteries, existing prior knowledge datasets may not be suitable, or autonomous disassembly processes may fail; e.g., a fastener must be dislodged manually, or autonomous grasping fails due to structural deformation of components, requiring manual alignment. Furthermore, for sorting and lifting applications, the complexity of planning and collision avoidance remains an outstanding problem Zhou et al (2019) ; Tang et al (2022) . Other approaches have aimed to reduce complexity of disassembly in various settings through tool design, such as gripper extensions Borràs et al (2018) or flexible grippers Schmitt et al (2011) , to assist with unscrewing, grasping and other contact-rich manipulation tasks.…”