3D path planning for a robot based on improved ant colony algorithm

Pu, Xiaorong; Xiong, Chaowen; Ji, Lianghao; Zhao, Longlong

doi:10.1007/s12065-020-00397-6

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…That means a more efficient algorithm and a shorter path. Luo; Pu [47], [48] in this paper improve ACO to solve the problem of slow convergence and low search efficiency. itemized ACO in the equation and flowchart algorithm in Figure 9 show down, [54].…”

Section:  Ant Colony Optimization Algorithms (Aco)mentioning

confidence: 99%

A Review on Path Planning Algorithms for Mobile Robots

Abed¹,

Lutfy²,

Al-Doori³

2021

ETJ

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Mobile robots use is rising every day. Path planning algorithms are needed to make a traveler of robots with the least cost and without collisions. Many techniques have been developed in path planning for mobile robot worldwide, however, the most commonly used techniques are presented here for further study. This essay aims to review various path planning strategies for mobile robots using different optimization methods taken recent publisher’s paper in last five year.

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Section:  Ant Colony Optimization Algorithms (Aco)mentioning

confidence: 99%

A Review on Path Planning Algorithms for Mobile Robots

Abed¹,

Lutfy²,

Al-Doori³

2021

ETJ

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“…Therefore, a large number of intelligent optimization algorithms such as ant colony algorithm [2], cuckoo search algorithm [3], whale optimization algorithm [4] and other algorithms are applied to robot path planning. Pu X et al [5] proposed to use pseudo-random state transition strategy and pheromone update strategy to improve the ant colony algorithm, so that the ant colony algorithm can quickly and effectively search for a feasible 3D path; Song P C et al [6] introduced a parallel communication strategy based on the cuckoo search algorithm to improve the efficiency of the algorithm search; Kumar S V et al [7] combined the whale optimization algorithm with the cuckoo search algorithm, and the combination of search algorithms improves the global search capability of the algorithm. However, the performance of the above improved algorithm still has much room for improvement.…”

Section: Introductionmentioning

confidence: 99%

3D path planning of underwater robot based on sparrow search algorithm with potential field heuristic

Wang

Zhao

Yuan

2022

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022)

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Aiming at the problem of large amount of calculation and difficulty in convergence of robot path planning in complex underwater 3D environments, the paper proposes a 3D path planning algorithm based on the sparrow search algorithm with potential field heuristic. First, establish an underwater 3D grid model, and use artificial potential field forces to generate an initial feasible path. Secondly, use the infinite folding chaotic map to expand the sparrow population and increase the diversity of the population.Then use the dynamic adjustment strategy to improve the location update formula of the sparrow search algorithm to adjust the search area range. Finally, the improved algorithm proposed is applied to solve the 3D path planning problem of underwater robots. The simulation results show that the optimization performance of the potential field sparrow search algorithm (PFSSA) proposed in this paper is better than the particle swarm optimization algorithm (PSO) and the sparrow search algorithm (SSA), and it can quickly obtain an optimal and safe feasible path, which proves the effectiveness of the method, and it has application value in the field of path planning of underwater robot.

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“…In Nazarahari et al (2019), an enhanced genetic algorithm to improve the initial paths in continuous space and find the optimal path between start and destination locations is given. In Pu et al (2020), an improved ant colony optimization algorithm integrated to the pseudo-random state transition strategy is designed in the three-dimensional space. In Mohammed et al (2020), an enhanced particle swarm optimization algorithm to find a safer path is presented.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Algorithms for Multiple Arc-Welding Robots

Chen

2021

Front. Control Eng.

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The applications of the deep reinforcement learning method to achieve the arcs welding by multi-robot systems are presented, where the states and the actions of each robot are continuous and obstacles are considered in the welding environment. In order to adapt to the time-varying welding task and local information available to each robot in the welding environment, the so-called multi-agent deep deterministic policy gradient (MADDPG) algorithm is designed with a new set of rewards. Based on the idea of the distributed execution and centralized training, the proposed MADDPG algorithm is distributed. Simulation results demonstrate the effectiveness of the proposed method.

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3D path planning for a robot based on improved ant colony algorithm

Cited by 11 publications

References 27 publications

A Review on Path Planning Algorithms for Mobile Robots

A Review on Path Planning Algorithms for Mobile Robots

3D path planning of underwater robot based on sparrow search algorithm with potential field heuristic

Deep Reinforcement Learning Algorithms for Multiple Arc-Welding Robots

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