Adaptive Directional Path Planner for Real-Time, Energy-Efficient, Robust Navigation of Mobile Robots

Abstract

Autonomous navigation through unknown and complex environments is a fundamental capability that is essential in almost all robotic applications. Optimal robot path planning is critical to enable efficient navigation. Path planning is a complex, compute and memory intensive task. Traditional methods employ either graph based search methods or sample based methods to implement path planning, which are sub-optimal and compute/memory-intensive. To this end, an Adaptive Directional Planner (ADP) algorithm is devised to achieve real-time, energy-efficient, memory-optimized, robust local path planning for enabling efficient autonomous navigation of mobile robots. The ADP algorithm ensures that the paths are optimal and kinematically-feasible. Further, the proposed algorithm is tested with different challenging scenarios verifying the functionality and robustness. The ADP algorithm implementation results demonstrate 40- 60X less number of nodes and 40 - 50X less execution time compared to the standard TP-RRT schemes, without compromising on accuracy. Finally, the algorithm has also been implemented as an accelerator for non-holonomic, multi-shape, small form factor mobile robots to provide a silicon solution with high performance and low memory footprint (28KB).