Yameng Yin¹, Lieping Zhang^2,*, Xiaoxu Shi¹, Yilin Wang³, Jiansheng Peng⁴, Jianchu Zou⁴

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2769-2790, 2024, DOI:10.32604/cmc.2024.056791 - 18 November 2024

Abstract By integrating deep neural networks with reinforcement learning, the Double Deep Q Network (DDQN) algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots. However, the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data. Targeting those problems, an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed. First, to enhance the precision of the target Q-value, the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value… More >

Xiaoqing Wang¹, Peng Duan^1,*, Leilei Meng^1,*, Kaidong Yang²

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 931-947, 2024, DOI:10.32604/cmc.2024.050612 - 18 July 2024

Abstract Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario. In this study, we investigated the post-disaster rescue path planning problem and modeled this problem as a variant of the travel salesman problem (TSP) with life-strength constraints. To address this problem, we proposed an improved iterated greedy (IIG) algorithm. First, a push-forward insertion heuristic (PFIH) strategy was employed to generate a high-quality initial solution. Second, a greedy-based insertion strategy was designed and used in the destruction-construction stage to increase the algorithm’s exploration ability. Furthermore,… More >

Yuting Liu¹, Hongwei Ding^1,*, Zongshan Wang^1,*, Gaurav Dhiman^2,3,4, Zhijun Yang¹, Peng Hu⁵

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2385-2406, 2023, DOI:10.32604/cmc.2023.039883 - 29 November 2023

Abstract The equilibrium optimizer (EO) represents a new, physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equilibrium. Despite its innovative foundation, the EO exhibits certain limitations, including imbalances between exploration and exploitation, the tendency to local optima, and the susceptibility to loss of population diversity. To alleviate these drawbacks, this paper introduces an improved EO that adopts three strategies: adaptive inertia weight, Cauchy mutation, and adaptive sine cosine mechanism, called SCEO. Firstly, a new update formula is conceived by incorporating an adaptive inertia weight… More >

Shaher Alshammrei¹, Sahbi Boubaker^2,*, Lioua Kolsi^1,3

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5939-5954, 2022, DOI:10.32604/cmc.2022.028165 - 21 April 2022

Abstract Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots (MRs) in both research and education. In this paper, an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra algorithm. To achieve this research objectives, first, the MR obstacle-free environment is modeled as a diagraph including nodes, edges and weights. Second, Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target point. During its movement, the robot should follow the previously obtained path and stop at each… More >

Aisha Muhammad^1,5, Mohammed A. H. Ali^2,*, Sherzod Turaev³, Ibrahim Haruna Shanono^4,5, Fadhl Hujainah⁶, Mohd Nashrul Mohd Zubir², Muhammad Khairi Faiz², Erma Rahayu Mohd Faizal¹, Rawad Abdulghafor⁸

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2697-2719, 2022, DOI:10.32604/cmc.2022.020873 - 07 December 2021

Abstract This paper presents a development of a novel path planning algorithm, called Generalized Laser simulator (GLS), for solving the mobile robot path planning problem in a two-dimensional map with the presence of constraints. This approach gives the possibility to find the path for a wheel mobile robot considering some constraints during the robot movement in both known and unknown environments. The feasible path is determined between the start and goal positions by generating wave of points in all direction towards the goal point with adhering to constraints. In simulation, the proposed method has been tested… More >