Improved Genetic Algorithms Based on Data-Driven Operators for Path Planning of Unmanned Surface Vehicle

Xin, Junfeng; Zhong, Jiabao; Sheng, Jinlu; Li, Penghao; Cui, Ying

doi:10.2316/j.2019.206-0315

Cited by 3 publications

(2 citation statements)

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“…These algorithms don't require any environment modeling, which outperform previous algorithms such as A * in terms of computation cost [22]. Genetic algorithm is another effective path planning problem with high robustness in various scenearios such as robot manipulators and unmanned surface vehicle [26], [27].…”

Section: A Path Planningmentioning

confidence: 99%

Capability Iteration Network for Robot Path Planning

Nie,

Gao,

Mei

et al. 2021

Preprint

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Path planning is an important topic in robotics. Recently, value iteration based deep learning models have achieved good performance such as Value Iteration Network(VIN). However, previous methods suffer from slow convergence and low accuracy on large maps, hence restricted in path planning for agents with complex kinematics such as legged robots. Therefore, we propose a new value iteration based path planning method called Capability Iteration Network(CIN). CIN utilizes sparse reward maps and encodes the capability of the agent with state-action transition probability, rather than a convolution kernel in previous models. Furthermore, two training methods including end-toend training and training capability module alone are proposed, both of which speed up convergence greatly. Several path planning experiments in various scenarios, including on 2D, 3D grid world and real robot with different map sizes are conducted. The results demonstrate that CIN has higher accuracy, faster convergence and lower sensitivity to random seed compared to previous VI-based models, hence more applicable for real robot path planning.

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Section: A Path Planningmentioning

confidence: 99%

Capability Iteration Network for Robot Path Planning

Nie,

Gao,

Mei

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…The improved algorithm combines the invasive weed algorithm with reverse learning (OBL) to initialize the population, and uses breeding jumps to speed up the convergence speed of invasive weed algorithm. Literature [10] proposes an improved genetic algorithm with adaptive adjustment of crossover probability, which performs better in the path planning of driverless vehicles. Literature [11] proposed a parallel multi-start and multi-target clonal selection algorithm for solving multi-objective Energy Reduction multi-depot vehicle routing problem.…”

Section: Introductionmentioning

confidence: 99%

A Path Planning Method Based on Improved Single Player-Monte Carlo Tree Search

2020

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With the thorough application and development of Intelligent Logistics System, the Path Planning Problem, which is one of the key problems of logistics scheduling, is accelerating to the direction of dynamics, diversification and complexity. In order to better adapt to the new changes, this paper proposes a path planning method based on improved SP-MCTS (Single-Player Monte Carlo Tree Search) algorithm. Unlike the previous heuristic algorithms, SP-MCTS does not completely depend on parameters, and can more effectively solve variable combinatorial optimization problems. Aiming at the problem of path planning, this paper enhances the simulation process of SP-MCTS by clonal selection algorithm, in order to improve the algorithm's accuracy of locating high-potential branches, and further improve the accuracy of the decisions made by SP-MCTS. Experiments show that the method proposed in this paper is less affected by the parameters and can better solve the changing path planning problem. Compared with the existing path planning methods and original SP-MCTS algorithm, it can solve the changeable path planning problem more stably, faster and more accurately. INDEX TERMS single player-monte carlo tree search; clonal selection algorithm; path planning

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