A mobile robot safe planner for multiple tasks in human-shared environments

Various approaches have been studied to solve the path planning problem of a mobile robot designing with multiple tasks. However, safe operation for a mobile robot in dynamic environments remains a challenging problem. This paper focuses on safe path planning for a mobile robot executing multiple tasks in an environment with randomly moving humans. To plan a safe path and achieve high task success rate, a safe planner is developed where a double-layer finite state automaton (FSA)-based risk search (FSARS) method considering environmental risks is proposed. The low-level of FSARS is a novel safe approach to prioritize a safe path rather than merely seeking the shortest path in dynamic environments. Meanwhile, the high-level implements a safety-first search structure utilizing FSA transitions. This structure aims to generating optimal paths while multitasking, avoiding collisions with humans moving completely randomly at the planning level instead of aiming at real-time collision avoidance. FSARS is verified through a series of comparative simulations involving seven types of environmental settings, each with distinct task number, grid size, and human number. We evaluate FSARS based on several metrics, including conflict number, conflict distribution, task success rate, reward, and computational time. Compared with the reinforcement learning method, FSARS reduces the average conflict by 65.4% and improves the task success rate by 34.4%. Simulation results demonstrate the effectiveness of FSARS with the lowest collisions and the highest success rate compared with classic approaches.