A Review on Autonomous Mobile Robot Path Planning Algorithms

Adzhar, Noraziah; Yuhani, Yusof; Ahmad, Muhammad Azrin

doi:10.25046/aj050330

Cited by 13 publications

(8 citation statements)

References 23 publications

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“…Аналіз літератури [2,3,[7][8][9][10][11][12], присвяченої методам планування шляху АМР, показав, що вказані методи можна класифікувати за різними ознаками (рис. 1), які можуть комбінуватися між собою.…”

Section: аналіз публікаційunclassified

Bio-inspired methods for planning the path of mobile robots

Gurko¹,

Hurko²

2022

Bulletin of KhNAHU

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Problem. The issue of path planning for a mobile robot is one of the most important ones of mobile robotics. Proper path planning ensures the safety of the robot and its environment, the efficiency of the tasks carried out by a robot, saves time and energy consumption for these tasks, etc. Therefore, research is constantly conducted on the implementation of new and improving existing optimization methods for the path planning for a mobile robot. The utilization of classical optimization methods is limited by their significant drawbacks, such as computational complexity and long time for searching the optimal path. To eliminate these issues, heuristic and then metaheuristic methods have been developed. Among metaheuristic methods, bio-inspired optimization methods, which are based on evolutionary processes in nature, as well as the behaviour of living organisms, are becoming increasingly popular. Goal. This paper aims to analyse the most popular bio-inspired algorithms used for mobile robot path planning. Methodology. The paper briefly reviews the bio-inspired optimization methods that are applicable to the path planning of a mobile robot. Particular emphasis is given to swarm intelligence algorithms, in which the relatively simple behaviour of individual agents interacting with each other and with the environment allows a swarm of these agents to achieve a given goal. Results. A classification of bio-inspired optimization methods used for mobile robot path planning is proposed. Pseudocodes for swarm optimization algorithms that are most frequently applied in mobile robotics are presented. Originality. This paper is one of the first in Ukraine to offer a comprehensive overview of bio-inspired methods of optimization used for mobile robot path planning. Practical value. The implementation of the considered algorithms in mobile robot control systems will improve the efficiency of robots in performing their assigned tasks. The given pseudocodes will simplify the development of software to implement the mentioned algorithms.

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Section: аналіз публікаційunclassified

Bio-inspired methods for planning the path of mobile robots

Gurko¹,

Hurko²

2022

Bulletin of KhNAHU

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“…Navigation and obstacle avoidance are the key aspects of a mobile robot. The shortest path length problem along with least time taken problem are one of the most active research areas in the field of mobile robotics [1] [2]. Several methods have been proposed to solve this path planning problem.…”

Section: Introductionmentioning

confidence: 99%

Modified Genetic Algorithm-based Robot Path Planning to Avoid Static Obstacles Collision

Bhanaria¹,

Pachori²,

Pandey³

2023

New Frontiers in Communication and Intelligent Systems

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In this paper, a modified Genetic Algorithm is presented for mobile robot path planning applications in a known environment. The algorithm provides the optimal path using the modified variable-length chromosomes study using the fitness function, which calculates the path length of the chromosomes such that the large path lengths are eliminated. The proposed algorithm uses the 8-way movement robot instead of the conventionally adopted 4-way movement commonly used in such applications. The results obtained using the proposed modified Genetic Algorithm during the study are compared with other approaches to the Genetic Algorithm. The proposed algorithm shows improvement in the convergence speed, provides better flexibility, provides shorter path length, and reduces the total time.

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“…TEB algorithm is to solve the optimized points and constrained edges, and then plan a path with high security. The real-time changing environmental information will cause the "elastic" deformation of the path, and the vehicle will react to the dynamic obstacles in the driving path [3] . The advantage of the dynamic window approach is that the planning is real-time and the planned path is smooth.…”

Section: Introductionmentioning

confidence: 99%

Research on local path planning of intelligent vehicle based on improved dynamic window approach

HANG¹,

YAN²,

FU³

et al. 2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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In order to make the intelligent vehicle run safely and improve the applicability of local path planning algorithm in intelligent vehicle, the paper presents a local path planning method for intelligent vehicles. Firstly, establish vehicle model of Ackerman steering. In addition, the minimum turning radius constraint is added to the speed screening mechanism based on the traditional dynamic window approach. Then, in order to avoid excessive changes in the driving speed of intelligent vehicles, the curvature retention evaluation index is added to the evaluation function. The simulation results show that the improved algorithm meets the requirements of intelligent vehicle dynamic obstacle avoidance and can plan a safe and reasonable path.

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A Review on Autonomous Mobile Robot Path Planning Algorithms

Cited by 13 publications

References 23 publications

Bio-inspired methods for planning the path of mobile robots

Bio-inspired methods for planning the path of mobile robots

Modified Genetic Algorithm-based Robot Path Planning to Avoid Static Obstacles Collision

Research on local path planning of intelligent vehicle based on improved dynamic window approach

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