Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

Kobayashi, Masato; Motoi, Naoki

doi:10.1109/access.2022.3150036

Cited by 45 publications

(25 citation statements)

References 29 publications

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“…For the path planning of intelligent vehicle, many scholars in China and abroad have conducted numerous studies and proposed many related algorithms, such as A* algorithm, artificial potential field method (Khatib, O et al, 1986), dynamic window method (Kobayashi, M et al, 2022), RRT algorithm (Cao et al, 2019), intelligent bionics algorithm, such as simulated annealing algorithm (Baik, H et al, 2019), ant colony algorithm , particle swarm optimization algorithm (Das, P.K et al, 2016), genetic algorithm (Yun et al, 2022a), and algorithm improvement and hybrid. A* algorithm is mainly applied to the global search in the static environment.…”

Section: Related Workmentioning

confidence: 99%

Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm

Shi

Huang²,

Du³

et al. 2022

Front. Bioeng. Biotechnol.

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Intelligent vehicles were widely used in logistics handling, agriculture, medical service, industrial production, and other industries, but they were often not smooth enough in planning the path, and the number of turns was large, resulting in high energy consumption. Aiming at the unsmooth path planning problem of four-wheel intelligent vehicle path planning algorithm, this article proposed an improved genetic and ant colony hybrid algorithm, and the physical model of intelligent vehicle was established. This article first improved ant colony optimization algorithm about heuristic function with the adaptive change of evaporation factor. Then, it improved the genetic algorithm on fitness function, adaptive adjustment of crossover factor, and mutation factor. Last, this article proposed the improved hybrid algorithm with the addition of a deletion operator, adoption of an elite retention strategy, and addition of suboptimal solutions obtained from the improved ant colony algorithm to improved genetic algorithm to obtain optimized new populations. The simulation environment for this article is windows 10, the processor is Intel Core i5-5257U, the running memory is 4GB, the compilation environment is MATLAB2018b, the number of ant samples is 50, the maximum number of iterations is 100, the initial population size of the genetic algorithm is 200, and the maximum number of iterations is 50. Simulation and physical experiments show that the improved hybrid algorithm is effective. Compared with the traditional hybrid algorithm, the improved hybrid algorithm reduced by 46% in the average number of iterations and 75% in the average number of turns in a simple grid. The improved hybrid algorithm reduced by 47% in the average number of iterations and 21% in the average number of turns in a complex grid. The improved hybrid algorithm works better to reduce the number of turns in simple maps.

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Section: Related Workmentioning

confidence: 99%

Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm

Shi

Huang²,

Du³

et al. 2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…By analyzing the literature [28][29][30][31][32][33][34][35], AGV using conventional DWA can complete obstacle avoidance and reach the target point in a facile environment. However, the AGV usually works in complex and variable environments.…”

Section: Problems Of Traditional Dwamentioning

confidence: 99%

Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments

Yang

et al. 2022

IEEE Access

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The traditional Dynamic Window Approach (DWA) with constant weight values of the evaluation function leads to the inability of obstacle avoidance for the Automated Guided Vehicles (AGV) to perform obstacle avoidance and path planning in the complex environment. Effective avoidance of complex obstacles requires adaptive weight adjustment to address the evaluation function's challenges. This paper proposes an adaptive DWA (ADWA), which introduces neural network training on the basis of the Mamdani DWA (MDWA). Firstly, the Mamdani type fuzzy controller is designed, and then the adaptive neuro-fuzzy controller is obtained by neural network training. Then, experiments are carried out through the MATLAB simulation environment. The simulation experiment results show that the improved DWA compared to traditional DWA can make the AGV pass the obstacle environment with a better trajectory and reduce the time. The improved DWA improves the autonomous obstacle avoidance capability of AGVs, which not only perfectly fits our task requirements, but also has apparent scientific and practical significance in developing AGV autonomous obstacle avoidance technology.INDEX TERMS fuzzy control, dynamic window approach, neural network, automatic guided vehicle

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“…Only a few studies related to reacting collision avoidance for unmanned ships were included in the paper [ 16 ]. Traditional obstacle avoidance algorithms mainly study the smoothness and safety of the trajectory without considering the time constraints [ 17 ], and the usual obstacle avoidance algorithms require iteration through all possible states [ 18 ], which greatly increases the computational complexity of the algorithms [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Risk-Sensitive Markov Decision Processes of USV Trajectory Planning with Time-Limited Budget

Ding,

Zhu

2023

Sensors

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Trajectory planning plays a crucial role in ensuring the safe navigation of ships, as it involves complex decision making influenced by various factors. This paper presents a heuristic algorithm, named the Markov decision process Heuristic Algorithm (MHA), for time-optimized avoidance of Unmanned Surface Vehicles (USVs) based on a Risk-Sensitive Markov decision process model. The proposed method utilizes the Risk-Sensitive Markov decision process model to generate a set of states within the USV collision avoidance search space. These states are determined based on the reachable locations and directions considering the time cost associated with the set of actions. By incorporating an enhanced reward function and a constraint time-dependent cost function, the USV can effectively plan practical motion paths that align with its actual time constraints. Experimental results demonstrate that the MHA algorithm enables decision makers to evaluate the trade-off between the budget and the probability of achieving the goal within the given budget. Moreover, the local stochastic optimization criterion assists the agent in selecting collision avoidance paths without significantly increasing the risk of collision.

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Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

Cited by 45 publications

References 29 publications

Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm

Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm

Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments

Risk-Sensitive Markov Decision Processes of USV Trajectory Planning with Time-Limited Budget

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