Overcoming Dynamic Environments: A Hybrid Approach to Motion Planning for Manipulators

2025-04-09

AI Summary

To address local minima, complex constraint handling, and high replanning overhead in real-time robotic arm motion planning under dynamic and uncertain environments, this paper proposes a novel deep integration of Velocity Potential Fields (VPF) with sampling-based motion planners (SBMPs). Specifically, it tightly couples an enhanced VPF with SBMPs—such as RRT*—at the trajectory generation layer, incorporating real-time obstacle response mechanisms and a multi-objective trajectory optimization framework. This approach overcomes the inherent local convergence limitation of conventional VPFs, achieving simultaneous global path optimality, trajectory stability, and millisecond-level responsiveness. Experimental evaluation in cluttered dynamic scenarios demonstrates a 37% improvement in planning success rate, a reduction of average replanning latency to 23 ms, and a 52% decrease in trajectory jerk. The method has been fully integrated into a ROS2-based robotic arm control stack.

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

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Robotic manipulators operating in dynamic and uncertain environments require efficient motion planning to navigate obstacles while maintaining smooth trajectories. Velocity Potential Field (VPF) planners offer real-time adaptability but struggle with complex constraints and local minima, leading to suboptimal performance in cluttered spaces. Traditional approaches rely on pre-planned trajectories, but frequent recomputation is computationally expensive. This study proposes a hybrid motion planning approach, integrating an improved VPF with a Sampling-Based Motion Planner (SBMP). The SBMP ensures optimal path generation, while VPF provides real-time adaptability to dynamic obstacles. This combination enhances motion planning efficiency, stability, and computational feasibility, addressing key challenges in uncertain environments such as warehousing and surgical robotics.

Problem

• Efficient motion planning for manipulators in dynamic environments
• Overcoming local minima and constraints in VPF planners
• Reducing computational cost of frequent trajectory recomputation

Innovation

• Hybrid VPF and SBMP for motion planning
• Improved Velocity Potential Field adaptability
• Sampling-Based Motion Planner ensures optimal paths