An improved A* algorithm for the industrial robot path planning with high success rate and short length

Bing, Fu; Lin, Chong; Zhou, Yuntao; Zheng, Dong; Wei, Zhehao; Dai, Jun; Pan, Haihong

doi:10.1016/j.robot.2018.04.007

Cited by 175 publications

(86 citation statements)

References 19 publications

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“…The so-called path planning of 2-dof manipulator is to find a path of minimum length from the initial point to a given target point in the network. Robot path planning algorithm problems include: traditional algorithms and intelligent algorithms, such as A* algorithm [1], Dijkstra algorithm [2], ant colony algorithm [3][4] and so on. Many scholars have introduced intelligent algorithms such as genetic algorithm [5] and neural network algorithm [6] into robot path planning.…”

Section: Introductionmentioning

confidence: 99%

Two-degree-of-freedom manipulator path planning based on zeroing neural network

Liu

2020

MATEC Web Conf.

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In this paper, the shortest path problem of manipulator path planning is transformed into a linear programming problem, and solved by zeroing neural network (ZNN). Firstly, the method of constructing the zeroing neural dynamics is given, and the ZNN model is constructed for shortest path problem of manipulator. Then, the Lyapunov method is utilized to prove the stability of the ZNN model. Finally, the ZNN model is applied to the path planning of manipulator to generate an optimal planning path. The simulation results show that the proposed method can effectively realize the optimal path planning of the manipulator.

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Section: Introductionmentioning

confidence: 99%

Two-degree-of-freedom manipulator path planning based on zeroing neural network

Liu

2020

MATEC Web Conf.

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“…Path planning [4,5], as the core of USV research, represents the intelligence level of the USV to a certain extent. The path planning method can be used to determine an optimal path from the starting point to the end point, which mainly includes the A* algorithm [6][7][8], genetic algorithm [9], artificial potential field method [10], ant colony algorithm [11], and particle swarm algorithm [12,13]. However, all the abovementioned methods of USV path planning present several drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…At present, there are several path planning smoothing methods. For the path planning of a mobile robot, based on the A* algorithm [6,14], all nodes in the planning path are traversed in the grid environment. When there is no obstacle on the connecting line of a node before and after, the intermediate node of the extended line is removed to reduce the number of path turns.…”

Section: Introductionmentioning

confidence: 99%

Second Path Planning for Unmanned Surface Vehicle Considering the Constraint of Motion Performance

Fan

Liao

et al. 2019

JMSE

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When utilizing the traditional path planning method for unmanned surface vehicles (USVs), ‘planning-failure’ is a common phenomenon caused by the inflection points of large curvatures in the planned path, which exceed the performances of USVs. This paper presents a second path planning method (SPP), which is an initial planning path optimization method based on the geometric relationship of the three-point path. First, to describe the motion performance of a USV in conjunction with the limited test data, a method of integral nonlinear least squares identification is proposed to rapidly obtain the motion constraint of the USV merely by employing a zig zag test. It is different from maneuverability identification, which is performed in combination with various tests. Second, the curvature of the planned path is limited according to the motion performance of the USV based on the traditional path planning, and SPP is proposed to make the maximum curvature radius of the optimized path smaller than the rotation curvature radius of the USV. Finally, based on the ‘Dolphin 1’ prototype USV, comparative simulation experiments were carried out. In the experiment, the path directly obtained by the initial path planning and the path optimized by the SPP method were considered as the tracking target path. The artificial potential field method was used as an example for the initial path planning. The experimental results demonstrate that the tracking accuracy of the USV significantly improved after the path optimization using the SPP method.

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“…From managerial insights, Sarkar et al have focused on increasing the safety factors and reducing the setting time [9]. Some well-known path-planning techniques like A * [10,11], Dijkstra [12], distance conversion [13,14], potential field [15][16][17][18][19], sampling-based [20,21], and piano stimulation problem [22][23][24] need more information and sometimes they require a full map. This weakness shows that in unknown environments, point-to-point guidance is necessary.…”

Section: Introductionmentioning

confidence: 99%

Robot Motion Planning in an Unknown Environment with Danger Space

et al. 2019

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This paper discusses the real-time optimal path planning of autonomous humanoid robots in unknown environments regarding the absence and presence of the danger space. The danger is defined as an environment which is not an obstacle nor free space and robot are permitted to cross when no free space options are available. In other words, the danger can be defined as the potentially risky areas of the map. For example, mud pits in a wooded area and greasy floor in a factory can be considered as a danger. The synthetic potential field, linguistic method, and Markov decision processes are methods which have been reviewed for path planning in a free-danger unknown environment. The modified Markov decision processes based on the Takagi–Sugeno fuzzy inference system is implemented to reach the target in the presence and absence of the danger space. In the proposed method, the reward function has been calculated without the exact estimation of the distance and shape of the obstacles. Unlike other existing path planning algorithms, the proposed methods can work with noisy data. Additionally, the entire motion planning procedure is fully autonomous. This feature makes the robot able to work in a real situation. The discussed methods ensure the collision avoidance and convergence to the target in an optimal and safe path. An Aldebaran humanoid robot, NAO H25, has been selected to verify the presented methods. The proposed methods require only vision data which can be obtained by only one camera. The experimental results demonstrate the efficiency of the proposed methods.

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An improved A* algorithm for the industrial robot path planning with high success rate and short length

Cited by 175 publications

References 19 publications

Two-degree-of-freedom manipulator path planning based on zeroing neural network

Two-degree-of-freedom manipulator path planning based on zeroing neural network

Second Path Planning for Unmanned Surface Vehicle Considering the Constraint of Motion Performance

Robot Motion Planning in an Unknown Environment with Danger Space

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