An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place Tasks using Reinforcement Learning

LASTIC pollution in rivers has become a pressing global issue, with 11 million tons of plastic waste entering the ocean annually, 80% of which is caused by 1,000 major polluting rivers [1]. To address this problem, it is desired to develop a solution capable of removing plastic and other waste objects without interfering with the existing flora and fauna essential to river ecosystems [2] . Our Autonomous River Cleanup (ARC) project, initiated in 2019, leverages robotics and automation to remove plastic waste from rivers. In order to increase the capacity at which this can be done, we enhance the existing single arm sorting station [3] with additional robot arms. For multiple robot agents to efficiently sort waste on a conveyor belt, we develop and evaluate novel strategy algorithms using reinforcement learning that assign pick-and-place (PnP) tasks to the respective robot agents (Figure 1). Given a set of objects on the moving conveyor belt, the robot agents are tasked with removing waste objects, whilst bio-matter is ignored and collected at the end of the belt. The challenge is to allocate each robot optimally with PnP operations for objects within its reachable workspace.