A review: On bio-inspired optimization methods for path planning of mobile robot

Abstract

In recent years, researchers have paid attention to algorithms inspired by nature where these algorithms have proven their efficiency in solving many optimization problems, especially in complex situations, due to their high precision, speed of optimization, simplicity of the techniques, and efficiency in agent cooperation. The primary issue in the field of autonomous mobile robots is navigation. An autonomous robot's navigation ability is one of its most crucial and distinctive features. There are four components of the autonomous robot navigation issue: vision, localization, cognition, and path planning. Many academics have used bio-inspired methods to solve navigation difficulties in mobile robots in recent years, including path planning where they considered the path planning problem as an optimization problem. Many novel path-planning methods have been created, and those using bio-inspired algorithms have received much attention. These algorithms have been shown to be useful in solving complex problems where the solution space isn't always adequately characterized and the problem necessitates solving complex mathematical models of live processes. More intricate optimization methods that transcend the constraints of classical procedures must be applied as the complexities of the optimization problem increase. This work contributes to presenting a group of algorithms inspired by nature that has been used to solve the problem of planning the path of mobile robots, and then making a comparison between these algorithms based on three factors (cost, time, and path length). Choosing an appropriate path-planning method contributes to ensuring safe and efficacious navigation from one point to another.

Path Planning for Autonomous Mobile Robots Using the RFO-GWO Optimization Algorithm

“…Several publications have also used bio-inspired methodologies to address different aspects of path planning strategies. [24]- [31], a Whale Optimization Algorithm (WOA), applied in fixed situations to meet prerequisites for the optimization length of path and smoothing path [32], and in [33], [34], proposed a technique that relies on the Cuckoo Optimization Algorithm for planning the robot's path in a moving situation. The simulation findings indicate that the method finds a barrier-free and short path under a variety of environmental situations.…”

“…This approach has lately been utilized to tackle a large number of optimization issues [40], [41]. The RFO method was used to solve the path planning problem of the autonomous mobile robot in [42]. The significance of this method is in using RFO to solve the navigational problems of autonomous mobile robots using polynomial logistic regression to reduce the fluctuations of the resulting path and produce a semi-smooth path.…”

Path Planning Using Combined Informed Rapidly- Exploring Random Tree Star and Particle Swarm Optimization Algorithms

“…Another path-planning approach uses bio-inspired heuristic methods [42], including Genetic Algorithm (GA) [43], Differential Evolution (DE) [44], and Ant Colony System (ACS) [45]. GA is an evolutionary algorithm that generates a new population through genetic crossover and mutation.…”