Simultaneous Learning and Planning Within Sensing Range: An Approach for Local Path Planning

This article proposes an approach for local path planning. Unlike traditional approaches, the proposed local path planner simultaneously learns and plans within the sensing range (SLPA-SR) during local path planning. SLPA-SR is the synergy between the local path planner, the dynamic window approach (DWA), the obstacle avoidance by velocity obstacle (VO) approach, and the proposed next-best reward learning (NBR) algorithms. In the proposed SLPA-SR, the DWA acts as an actuator and helps to balance exploration and exploitation in the proposed NBR. In the proposed NBR, dimensions of state and action do not need to be defined a <i>priori</i>; rather, dimensions of state and action change dynamically. The proposed SLPA-SR is simulated and experimentally validated on the TurtleBot3 Waffle Pi. The performance of the proposed SLPA-SR is tested in several typical environments, both in simulation and hardware experiments. The proposed SLPA-SR outperforms the contender algorithms (i.e., DWA, DWA-RL, improved time elastic band, predictive artificial potential field, and artificial potential field) by a significant margin in terms of run-time, linear velocity, angular velocity, success rate, average trajectory length, and average velocity. The efficacy of the proposed NBR is established by analyzing the percentage of exploitation, average reward, and state-action pair count.