Real-Time Sampling-Based Safe Motion Planning for Robotic Manipulators in Dynamic Environments

2024-12-31

AI Summary

Real-time safe path planning for robotic manipulators in dynamic environments remains challenging due to stringent safety, computational, and kinematic constraints. Method: This paper proposes DRGBT—a novel algorithm that introduces a dynamically expanding bubble structure to model the free configuration space, integrating distance-awareness and safety inflation to derive provably safe, real-time sufficient conditions for motion planning under kinematic constraints. It unifies sampling-based planning (an RRT variant), dynamic configuration-space updating, real-time scheduling analysis, and sensor-based closed-loop feedback—without requiring GPU or parallel hardware. Contribution/Results: DRGBT guarantees strict safety on low-cost serial systems. Evaluations in simulation and on physical robots demonstrate millisecond-scale replanning latency and robust performance in complex, human-involved dynamic scenarios. The method validates real-time feasibility, formal safety assurance, and practical deployability on resource-constrained platforms.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In this paper, we present the main features of Dynamic Rapidly-exploring Generalized Bur Tree (DRGBT) algorithm, a sampling-based planner for dynamic environments. We provide a detailed time analysis and appropriate scheduling to facilitate a real-time operation. To this end, an extensive analysis is conducted to identify the time-critical routines and their dependence on the number of obstacles. Furthermore, information about the distance to obstacles is used to compute a structure called dynamic expanded bubble of free configuration space, which is then utilized to establish sufficient conditions for a guaranteed safe motion of the robot while satisfying all kinematic constraints. An extensive randomized simulation trial is conducted to compare the proposed algorithm to a competing state-of-the-art method. Finally, an experimental study on a real robot is carried out covering a variety of scenarios including those with human presence. The results show the effectiveness and feasibility of real-time execution of the proposed motion planning algorithm within a typical sensor-based arrangement, using cheap hardware and sequential architecture, without the necessity for GPUs or heavy parallelization.

Problem

• Dynamic Environment
• Safe Real-time Path Planning
• Robot Arm Obstacle Avoidance

Innovation

• DRGBT algorithm
• real-time path planning
• low-cost hardware efficiency