Online decentralized planning for a cable-driven parallel robot with aerial and ground actuators through multi-agent reinforcement learning

Scholars have proposed a cable-driven parallel robot (CDPR) with aerial and ground actuators, referred to as a cable-towed aerial platform (CTAP), to address the limited ability to withstand external forces of existing unmanned arrival vehicle (UAV)-based aerial platforms. To tackle practical problems (e.g., firefighting), a CTAP may need to perform motion planning in an environment with obstacles and communication failures. To this end, this article proposes an online decentralized planning approach based on multi-agent reinforcement learning (MARL) for a CTAP to achieve real-time motion planning in a communication-denied environment with obstacles. This article then defines the state and action spaces of a MARL-based planner. A reward function is designed for the MARL-based planning approach according to the widely used optimization-based planning approach. This study has successfully trained a MARL-based planner using this approach, and the important training techniques used in this study are reported. Statistical comparison of the MARL-based decentralized planner and an optimization-based centralized planner is conducted in simulation. The MARL-based and optimization-based planners are deployed to a CTAP prototype to address motion planning problems in the real world. Experimental results show that the MARL-based planner can achieve successful, decentralized, and online motion planning for a CTAP.