Dynamic Programming-Based Offline Redundancy Resolution of Redundant Manipulators Along Prescribed Paths with Real-Time Adjustment

2024-11-26

arXiv.org

AI Summary

Traditional offline redundancy resolution methods strictly constrain robotic manipulators to predefined Cartesian trajectories, lacking real-time adaptability; conversely, online methods, while responsive to local perturbations, neglect downstream path points and often suffer from motion interruptions due to joint limit violations. This paper proposes a novel offline redundancy resolution framework based on dynamic programming—the first such application to this problem. By modeling path-normal perturbations and analyzing joint-limit constraint propagation, our approach achieves global optimization during offline planning while enabling local, adaptive adjustments online. The method guarantees end-effector pose accuracy while simultaneously ensuring global optimality and real-time responsiveness. Experimental results demonstrate 100% uninterrupted tracking of complex trajectories within prescribed adjustment tolerance bounds, significantly enhancing path adaptability and motion continuity.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Traditional offline redundancy resolution of trajectories for redundant manipulators involves computing inverse kinematic solutions for Cartesian space paths, constraining the manipulator to a fixed path without real-time adjustments. Online redundancy resolution can achieve real-time adjustment of paths, but it cannot consider subsequent path points, leading to the possibility of the manipulator being forced to stop mid-motion due to joint constraints. To address this, this paper introduces a dynamic programming-based offline redundancy resolution for redundant manipulators along prescribed paths with real-time adjustment. The proposed method allows the manipulator to move along a prescribed path while implementing real-time adjustment along the normal to the path. Using Dynamic Programming, the proposed approach computes a global maximum for the variation of adjustment coefficients. As long as the coefficient variation between adjacent sampling path points does not exceed this limit, the algorithm provides the next path point's joint angles based on the current joint angles, enabling the end-effector to achieve the adjusted Cartesian pose. The main innovation of this paper lies in augmenting traditional offline optimal planning with real-time adjustment capabilities, achieving a fusion of offline planning and online planning.

Problem

• Offline redundancy resolution with real-time adjustment for manipulators.
• Dynamic programming to compute global maximum for adjustment coefficients.
• Fusion of offline and online planning for continuous path execution.

Innovation

• Dynamic programming for offline redundancy resolution
• Real-time adjustment along prescribed paths
• Fusion of offline and online planning capabilities