A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented

An improved adaptive genetic algorithm for mobile robot path planning analogous to TSP with constraints on city priorities

The material transportation planning with a mobile robot can be regarded as the ordered clustered traveling salesman problem. To solve such problems with different priorities at stations, an improved adaptive genetic simulated annealing algorithm is proposed. Firstly, the priority matrix is defined according to station priorities. Based on standard genetic algorithm, the generating strategy of the initial population is improved to prevent the emergence of non-feasible solutions, and an improved adaptive operator is introduced to improve the population ability for escaping local optimal solutions and avoid premature phenomena. Moreover, to speed up the convergence of the proposed algorithm, the simulated annealing strategy is utilized in mutation operations. The experimental results indicate that the proposed algorithm has the characteristics of strong ability to avoid local optima and the faster convergence speed

Motion Planning for Autonomous Ground Vehicles Using Artificial Potential Fields: A Review

Autonomous ground vehicle systems have found extensive potential and practical applications in the modern world. The development of an autonomous ground vehicle poses a significant challenge, particularly in identifying the best path plan, based on defined performance metrics such as safety margin, shortest time, and energy consumption. Various techniques for motion planning have been proposed by researchers, one of which is the use of artificial potential fields. Several authors in the past two decades have proposed various modified versions of the artificial potential field algorithms. The variations of the traditional APF approach have given an answer to prior shortcomings. This gives potential rise to a strategic survey on the improved versions of this algorithm. This study presents a review of motion planning for autonomous ground vehicles using artificial potential fields. Each article is evaluated based on criteria that involve the environment type, which may be either static or dynamic, the evaluation scenario, which may be real-time or simulated, and the method used for improving the search performance of the algorithm. All the customized designs of planning models are analyzed and evaluated. At the end, the results of the review are discussed, and future works are proposed

Autonomous robots path planning: An adaptive roadmap approach

Developing algorithms that allow robots to independently navigate unknown environments is a widely researched area of robotics. The potential for autonomous mobile robots use, in industrial and military applications, is boundless. Path planning entails computing a collision free path from a robots current position to a desired target. The problem of path planning for these robots remains underdeveloped. Computational complexity, path optimization and robustness are some of the issues that arise. Current algorithms do not generate general solutions for different situations and require user experience and optimization. Classical algorithms are computationally extensive. This reduces the possibility of their use in real time applications. Additionally, classical algorithms do not allow for any control over attributes of the generated path. A new roadmap path planning algorithm is proposed in this paper. This method generates waypoints, through which the robot can avoid obstacles and reach its goal. At the heart of this algorithm is a method to control the distance of the waypoints from obstacles, without increasing its computational complexity. Several simulations were run to illustrate the robustness and adaptability of this approach, compared to the most commonly used path planning methods

A one decade survey of autonomous mobile robot systems

Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability

Mobile Robot Navigation in Static and Dynamic Environments using Various Soft Computing Techniques

The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. The present research focuses on the design and implementation of the intelligent navigation algorithms, which is capable of navigating a mobile robot autonomously in static as well as dynamic environments. Navigation and obstacle avoidance are one of the most important tasks for any mobile robots. The primary objective of this research work is to improve the navigation accuracy and efficiency of the mobile robot using various soft computing techniques. In this research work, Hybrid Fuzzy (H-Fuzzy) architecture, Cascade Neuro-Fuzzy (CN-Fuzzy) architecture, Fuzzy-Simulated Annealing (Fuzzy-SA) algorithm, Wind Driven Optimization (WDO) algorithm, and Fuzzy-Wind Driven Optimization (Fuzzy-WDO) algorithm have been designed and implemented to solve the navigation problems of a mobile robot in different static and dynamic environments. The performances of these proposed techniques are demonstrated through computer simulations using MATLAB software and implemented in real time by using experimental mobile robots. Furthermore, the performances of Wind Driven Optimization algorithm and Fuzzy-Wind Driven Optimization algorithm are found to be most efficient (in terms of path length and navigation time) as compared to rest of the techniques, which verifies the effectiveness and efficiency of these newly built techniques for mobile robot navigation. The results obtained from the proposed techniques are compared with other developed techniques such as Fuzzy Logics, Genetic algorithm (GA), Neural Network, and Particle Swarm Optimization (PSO) algorithm, etc. to prove the authenticity of the proposed developed techniques