Robot Local Planner: A Periodic Sampling-Based Motion Planner with Minimal Waypoints for Home Environments

TL;DR

This paper introduces the Robot Local Planner (RLP), a periodic sampling-based motion planning method that minimizes waypoints for efficient, safe, and robust robot navigation in home environments, improving planning speed and success rates.

Contribution

The RLP is a novel motion planner that leverages environment features for efficiency, incorporates periodic re-planning for optimality, and uses robust inverse kinematics to handle errors.

Findings

RLP reduces planning time and motion duration.

RLP demonstrates higher robustness against recognition and control errors.

Application experiments show high success rates in tidy-up tasks.

Abstract

The objective of this study is to enable fast and safe manipulation tasks in home environments. Specifically, we aim to develop a system that can recognize its surroundings and identify target objects while in motion, enabling it to plan and execute actions accordingly. We propose a periodic sampling-based whole-body trajectory planning method, called the "Robot Local Planner (RLP)." This method leverages unique features of home environments to enhance computational efficiency, motion optimality, and robustness against recognition and control errors, all while ensuring safety. The RLP minimizes computation time by planning with minimal waypoints and generating safe trajectories. Furthermore, overall motion optimality is improved by periodically executing trajectory planning to select more optimal motions. This approach incorporates inverse kinematics that are robust to base position errors, further enhancing robustness. Evaluation experiments demonstrated that the RLP outperformed existing methods in terms of motion planning time, motion duration, and robustness, confirming its effectiveness in home environments. Moreover, application experiments using a tidy-up task achieved high success rates and short operation times, thereby underscoring its practical feasibility.