Loc Q. Huynh¹, Ly V. Tran¹, Phuc N. K. Phan¹, Zhiqiu Yu², Son V. T. Dao^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2281-2300, 2023, DOI:10.32604/cmc.2023.034872

Abstract Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles. In this paper, we propose a novel path planning algorithm–Intermediary RRT*-PSO-by utilizing the exploring speed advantages of Rapidly exploring Random Trees and using its solution to feed to a metaheuristic-based optimizer, Particle swarm optimization (PSO), for fine-tuning and enhancement. In Phase 1, the start and goal trees are initialized at the starting and goal positions, respectively, and the intermediary tree is initialized at a random unexplored region of the search space. The… More >

Chengchen Zhuge^1,2,3,*, Qun Wang^1,2,3, Jiayin Liu^1,2,3, Lingxiang Yao⁴

Computer Systems Science and Engineering, Vol.39, No.1, pp. 107-119, 2021, DOI:10.32604/csse.2021.016273

Abstract The Rapidly-exploring Random Tree (RRT) algorithm is an efficient path-planning algorithm based on random sampling. The RRT* algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal solution. However, it has been proven to take an infinite time to do so. An improved Quick-RRT* (Q-RRT*) algorithm based on a virtual light source is proposed in this paper to overcome this problem. The virtual light-based Q-RRT* (LQ-RRT*) takes advantage of the heuristic information generated by the virtual light on the map. In this way, the tree can find the initial solution quickly. Next, the LQ-RRT* algorithm… More >