Dynamic Obstacle Avoidance Algorithm for Autonomous Mobile Robots | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 6, Volume 23, Issue 2, June 2023, Pages 63-82 PDF (1.55 M) | ||
| Document Type: Research Paper | ||
| DOI: https://doi.org/10.33103/uot.ijccce.23.2.6 | ||
| Authors | ||
| Noor Abdul Khaleq Zghair* 1; Ahmed S. Al-Araji2 | ||
| 1computer engineering department/ university of technology/ Baghdad/ Iraq | ||
| 2Computer Engineering Department | ||
| Abstract | ||
| A mobile robot's major purpose is to get to its destination by traveling over an optimum path defined by various parameters such as time, distance, and the robot's safety from any impediments in its path. As a result, the backbone of the autonomous mobile robot is path planning and obstacle avoidance. Several algorithms for path planning and obstacle avoidance have been presented by various researchers, each with its own set of benefits and drawbacks. This paper focuses on two parts; the first part finds the short and smooth collision-free path for a mobile robot to navigate in a static environment based on two proposed hybrid algorithms. The first hybrid is between Firefly Algorithm (FA) and Modify Chaotic Particle Swarm Optimization (MCPSO), namely (HFACPSO), while the other hybrid is between Genetic Algorithm (GA) and MCPSO, namely (HGACPSO).The second part suggests an algorithm planner for improving the efficiency of the route-planning algorithm with moving obstacle avoidance by adjusting the velocity or re-planning the path for the mobile robot. To demonstrate the effectiveness of the proposed algorithms in terms of the shortest path length and collision-free, as well as obtaining optimal or near-optimal wheel velocities with the minimum number of iterations. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equal to (0.82%) compared to the firefly algorithm (FA). Moreover, the hybrid (GAMCPSO) algorithm enhancement on the path length equals (0.67%) compared to the genetic algorithm (GA). All methods are simulated in a static and dynamic obstacle environment using MATLAB 2018b. | ||
| Keywords | ||
| Dynamic environment; Collision avoidance; obstacle avoidance; Path planning; PSO; FA; GA | ||
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