A Method on Dynamic Path Planning for Robotic Manipulator Autonomous Obstacle Avoidance Based on an Improved RRT Algorithm

Wei, Kun; Ren, Bo

doi:10.3390/s18020571

Cited by 198 publications

(103 citation statements)

References 13 publications

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“…where is a constant about the gravitational field and then the gravitation att ( ) can be solved by calculating the negative gradient value of the gravitational field, which is described as Formula (14).…”

Section: Harvesting Sequence Planning Based On Minimum Energymentioning

confidence: 99%

“…Experimental studies based on a 6 DOF industrial robot show the proposed method can improve the movement accuracy of the industrial robot significantly. To make the robotic manipulator work efficiently and safely in a dynamic unstructured environment, Wei et al [14] proposed an autonomous obstacle avoidance dynamic path-planning method for a robotic manipulator based on an improved RRT algorithm. For solving serial manipulators path-planning when grabbing target in obstacle workspace, Dai et al [15] presented a solution based on screw theory and ant colony algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Collision‐Free Path‐Planning for Six‐DOF Serial Harvesting Robot Based on Energy Optimal and Artificial Potential Field

Luo

Wen

et al. 2018

Complexity

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Collision-free autonomous path planning under a dynamic and uncertainty vineyard environment is the most important issue which needs to be resolved firstly in the process of improving robotic harvesting manipulator intelligence. We present and apply energy optimal and artificial potential field to develop a path planning method for six degree of freedom (DOF) serial harvesting robot under dynamic uncertain environment. Firstly, the kinematical model of Six-DOF serial manipulator was constructed by using the Denavit-Hartenberg (D-H) method. The model of obstacles was defined by axis-aligned bounding box, and then the configuration space of harvesting robot was described by combining the obstacles and arm space of robot. Secondly, the harvesting sequence in path planning was computed by energy optimal method, and the anticollision path points were automatically generated based on the artificial potential field and sampling searching method. Finally, to verify and test the proposed path planning algorithm, a virtual test system based on virtual reality was developed. After obtaining the space coordinates of grape picking point and anticollision bounding volume, the path points were drew out by the proposed method. 10 times picking tests for grape anticollision path planning were implemented on the developed simulation system, and the success rate was up to 90%. The results showed that the proposed path planning method can be used to the harvesting robot.

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Section: Harvesting Sequence Planning Based On Minimum Energymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collision‐Free Path‐Planning for Six‐DOF Serial Harvesting Robot Based on Energy Optimal and Artificial Potential Field

Luo

Wen

et al. 2018

Complexity

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“…These improved methods enhance the performance of Basic-RRT from various aspects, but the obtained solutions are still highly suboptimal in most cases. Therefore, various methods of pruning and smoothing [8][9][10][11] have been proposed to implement path post processing.…”

Section: Introductionmentioning

confidence: 99%

Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm

et al. 2020

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Sampling-based methods are popular in the motion planning of robots, especially in high-dimensional spaces. Among the many such methods, the Rapidly-exploring Random Tree (RRT) algorithm has been widely used in multi-degree-of-freedom manipulators and has yielded good results. However, existing RRT planners have low exploration efficiency and slow convergence speed and have been unable to meet the requirements of the intelligence level in the Industry 4.0 mode. To solve these problems, a general autonomous path planning algorithm of Node Control (NC-RRT) is proposed in this paper based on the architecture of the RRT algorithm. Firstly, a method of gradually changing the sampling area is proposed to guide exploration, thereby effectively improving the search speed. In addition, the node control mechanism is introduced to constrain the extended nodes of the tree and thus reduce the extension of invalid nodes and extract boundary nodes (or near-boundary nodes). By changing the value of the node control factor, the random tree is prevented from falling into a so-called “local trap” phenomenon, and boundary nodes are selected as extended nodes. The proposed algorithm is simulated in different environments. Results reveal that the algorithm greatly reduces the invalid exploration in the configuration space and significantly improves planning efficiency. In addition, because this method can efficiently use boundary nodes, it has a stronger applicability to narrow environments compared with existing RRT algorithms and can effectively improve the success rate of exploration.

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“…Jiankun Wang et al proposed an improved RRT algorithm incorporating obstacle boundary information to deal with an optimal path in 2016 [12]. Wei, k et al proposed a dynamic path planning method for robotic autonomous obstacle avoidance based on an improved RRT algorithm in 2018 [13]. This method is used for obstacle avoidance of robots in smart factories.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method

Zhou

et al. 2020

Applied Sciences

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In order to realize the technique of quick picking and obstacle avoidance, this work proposes a trajectory optimization method for the pickup manipulator under the obstacle condition. The proposed method is based on the improved artificial potential field method and the cosine adaptive genetic algorithm. Firstly, the Denavit–Hartenberg (D-H) method is used to carry out the kinematics modeling of the pickup manipulator. Taking into account the motion constraints, the cosine adaptive genetic algorithm is utilized to complete the time-optimal trajectory planning. Then, for the collision problem in the obstacle environment, the artificial potential field method is used to establish the attraction, repulsion, and resultant potential field functions. By improving the repulsion potential field function and increasing the sub-target point, obstacle avoidance planning of the improved artificial potential field method is completed. Finally, combined with the improved artificial potential field method and cosine adaptive genetic algorithm, the movement simulation analysis of the five-Degree-of-Freedom pickup manipulator is carried out. The trajectory optimization under the obstacle environment is realized, and the picking efficiency is improved.

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A Method on Dynamic Path Planning for Robotic Manipulator Autonomous Obstacle Avoidance Based on an Improved RRT Algorithm

Cited by 198 publications

References 13 publications

Collision‐Free Path‐Planning for Six‐DOF Serial Harvesting Robot Based on Energy Optimal and Artificial Potential Field

Collision‐Free Path‐Planning for Six‐DOF Serial Harvesting Robot Based on Energy Optimal and Artificial Potential Field

Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm

Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method

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