Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

Abstract

The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). Firstly, in local path planning, the machine learning algorithms are used to train the datasets, the real-time obstacle avoidance decision model is established, and cross validation is performed. Secondly, in global path planning, the greedy algorithm idea and B-spline curve are introduced into the RRT algorithm, redundant nodes are removed, and the reverse iteration is performed to generate a smooth path. Then, in path planning, the fitness function and genetic operation method of genetic algorithm are optimized, the pheromone update strategy and deadlock elimination strategy of ant colony algorithm are optimized, and the genetic-ant colony fusion strategy is used to fuse the two algorithms. Finally, the optimized path planning algorithm is used for simulation experiment. Comparative simulation experiments show that the random forest has the highest real-time obstacle avoidance prediction accuracy in local path planning, and the S-RRT algorithm can effectively shorten the total path length generated by the RRT algorithm in global path planning. The HGA-ACO algorithm can reduce the iteration number reasonably, reduce the search time effectively, and obtain the optimal solution in path planning.

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

“…Table 2 lists industrial applications in maritime industries [262,82], transportation [2,13,10,171,199,16], autonomous vehicles [263,228], health [2,178,10], behavior analysis [199,16], manufacturing [216,10,77] and indoor positioning [216,2].…”

“…Comment: Tracking worker movements is important in manufacturing as well [228] Collision avoidance of mobile robot ML+ random tree + heuristics Manufacturing, transportation [77] Manufacturing Optimization (static) + situational awareness and rescheduling…”

“…While the focus of Schabus and Scholz [216] was to identify the deviation from shortest paths and bottleneck paths with using movement behavior analysis, Song et al [228] worked with the objective of finding safe paths in an indoor environment. They proposed mobile robot path planning to produce the optimal safe path.…”

Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

“…Several academics have researched it and produced some findings. Among them, intelligent path planning algorithms like the genetic algorithm (Sarkar et al, 2022;Song et al, 2021) and the ant colony algorithm (Liu et al, 2019;Luo et al, 2020) can make better path planning in complex environments, but because iterative computation is required, their timeliness is difficult to meet the requirements of intelligent vehicles (Song et al, 2017b). A* algorithm Ma et al, 2020) and D* algorithm (Liu et al, 2020;Yan-long et al, 2021) are examples of heuristic search algorithms that may satisfy real-time demands in complicated surroundings, however their planned paths may not always adhere to driving restrictions.…”

IHSSAO: An Improved Hybrid Salp Swarm Algorithm and Aquila Optimizer for UAV Path Planning in Complex Terrain

“…Path planning means that the robot (such as UAV, mobile robot, and underwater autonomous vehicle) plans a complete path from the starting location to the target location safely based on the available information and satisfies the various indicators (such as terrain constraint, threat constraint, energy consumption, and path length). Its goal is to find an optimal path with the least costs [3].…”