Robot Local Planner: A Periodic Sampling-Based Motion Planner with Minimal Waypoints for Home Environments
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.
Method
In general, the RLP operates similarly to RRT-Connect in motion, performing planning periodically.
This video showcases the implementation with the robot. The semi-transparent robot models represent the trajectories generated.
Experiment
This experiment shows the integration of the Robot Local Planner into a Tidy-up task. Compared to conventional RRT-based motion planning, the task completion time was reduced by approximately 28%.
This video shows the RLP's ability to dynamically avoid collisions, proving its effectiveness in real-world scenarios.
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Citation
@inproceedings{RobotLocalPlanner2025,
title = {Robot Local Planner: A Periodic Sampling-Based Motion Planner with Minimal Waypoints for Home Environments},
author = {Takeshita, Keisuke and Yamazaki, Takahiro and Ono, Tomohiro and Yamamoto, Takashi},
booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
year = {2025},
pages = {X--X},
doi = {DOI URL},
publisher = {IEEE},
}Notification
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