Energy Optimized Robot Arm Path Planning using Differential Evolution in Dynamic Environment

TL;DR

This paper introduces a differential evolution-based method for energy-efficient path planning of industrial robot arms in dynamic environments with obstacles, aiming to minimize energy consumption while ensuring obstacle avoidance.

Contribution

It presents a novel application of differential evolution for energy-optimized robot path planning in complex, obstacle-filled workspaces, improving over existing methods.

Findings

DE algorithm achieves shorter, more energy-efficient paths.

The method outperforms traditional path-planning algorithms.

Energy consumption is significantly reduced in experiments.

Abstract

Robots are widely used in industry due to their efficiency and high accuracy in performance. One of the most intriguing issues in manufacturing stage of production line is to minimize significantly high percentage of energy consumed by these robot manipulators. The energy optimal control of robotic manipulators is a complex problem, as it requires a deep understanding of the robot's kinematics and dynamic behaviors. This paper propose a novel method of energy efficient path planning of an industrial robot arm in a workspace with multiple obstacles using differential evolution (DE) algorithm. The path-planning problem is formulated as an optimization problem with an aim to determine the shortest and energy optimal path of the robot arm from its given initial position to the predefined goal location, without hitting obstacles. Application of such evolutionary algorithms in trajectory planning is advantageous because the exact solution to the path-planning problem is not always available beforehand and must be determined dynamically. Experiments undertaken reveal that the DE-based path-planning algorithm outperforms its contenders in a significant manner.