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

Luo, Liang; Wen, Hanjin; Lu, Qinghua; Huang, Haojie; Chen, Weilin; Zou, Xiangjun; Chen, Zhong

doi:10.1155/2018/3563846

Cited by 32 publications

(21 citation statements)

References 23 publications

(26 reference statements)

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“…The artificial potential field method [27,28] is used for obstacle avoidance planning. It has the advantages of a simple and intuitive mathematical model, low computational complexity, and good real-time performance.…”

Section: Improved Methodsmentioning

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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Section: Improved Methodsmentioning

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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“…Location estimates are obtained by combining information from visual and odometric sensors as well as a real‐time differential global positioning system and gyro compass. An extended Kalman filter has been employed for detection of obstacles. A redundant set of sensors combines multiple measurements into a good quality estimate and allows the detection of sensor failures.…”

Section: Literature Surveymentioning

confidence: 99%

Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Gangadharan

Salgaonkar

2020

Engineering Reports

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Metaheuristic algorithms such as ant colony optimization (ACO) and firefly (FF) have been successfully employed to solve the optimization problems such as robot motion planning in dynamic environments. The systematic plantation of rubber trees on a rectangular grid motivated us to explore application of grid search algorithms. We compared the ACO and FF algorithms in various scenarios by changing simulation parameters like density of the environment, land size, number of robots simultaneously available, and hillock plantations. In all different scenarios, we evaluated the performance of ACO and FF in terms of path length and time of execution, we found that later is outperforming the former. Regression equations are framed to establish the contributions of different parameters. Statistical significance of the results has been in favor of this hypothesis. The shortest path on a plain land is the relatively simplest scenario, while the Hamiltonian on a concave surface is arguably the most difficult. The novelty of this work lies in the very idea of an autonomous robot for the rubber tapping and then path optimization by employing soft computing techniques. This proposal of rubber harvesting robot if implemented for latex collection, has a potential to drive the rubber farming and allied businesses to scale up the economy of the coastal areas of India, say for example, Kerala.

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“…Second, for high DOFs with a low obstacle density, an intermediate point obstacle avoidance algorithm for a 6 DOF serial manipulator and two static obstacles is presented in [ 25 ]. Similarly, an algorithm based on the artificial potential field was reported [ 26 ], where the analysis focused on a 6 DOF industrial manipulator over a workspace with few obstacles. Finally [ 27 ], reported a powerful obstacle avoidance path planning method, using forward- and backward-reaching inverse kinematics to compute a solution path of a seven-DOF manipulator for a 3D workspace with three static obstacles.…”

Section: Introductionmentioning

confidence: 99%

Exploring a Novel Multiple-Query Resistive Grid-Based Planning Method Applied to High-DOF Robotic Manipulators

Huerta-Chua

Diaz-Arango

Vázquez-Leal

et al. 2021

Sensors

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The applicability of the path planning strategy to robotic manipulators has been an exciting topic for researchers in the last few decades due to the large demand in the industrial sector and its enormous potential development for space, surgical, and pharmaceutical applications. The automation of high-degree-of-freedom (DOF) manipulator robots is a challenging task due to the high redundancy in the end-effector position. Additionally, in the presence of obstacles in the workspace, the task becomes even more complicated. Therefore, for decades, the most common method of integrating a manipulator in an industrial automated process has been the demonstration technique through human operator intervention. Although it is a simple strategy, some drawbacks must be considered: first, the path’s success, length, and execution time depend on operator experience; second, for a structured environment with few objects, the planning task is easy. However, for most typical industrial applications, the environments contain many obstacles, which poses challenges for planning a collision-free trajectory. In this paper, a multiple-query method capable of obtaining collision-free paths for high DOF manipulators with multiple surrounding obstacles is presented. The proposed method is inspired by the resistive grid-based planner method (RGBPM). Furthermore, several improvements are implemented to solve complex planning problems that cannot be handled by the original formulation. The most important features of the proposed planner are as follows: (1) the easy implementation of robotic manipulators with multiple degrees of freedom, (2) the ability to handle dozens of obstacles in the environment, (3) compatibility with various obstacle representations using mathematical models, (4) a new recycling of a previous simulation strategy to convert the RGBPM into a multiple-query planner, and (5) the capacity to handle large sparse matrices representing the configuration space. A numerical simulation was carried out to validate the proposed planning method’s effectiveness for manipulators with three, five, and six DOFs on environments with dozens of surrounding obstacles. The case study results show the applicability of the proposed novel strategy in quickly computing new collision-free paths using the first execution data. Each new query requires less than 0.2 s for a 3 DOF manipulator in a configuration space free-modeled by a 7291 × 7291 sparse matrix and less than 30 s for five and six DOF manipulators in a configuration space free-modeled by 313,958 × 313,958 and 204,087 × 204,087 sparse matrices, respectively. Finally, a simulation was conducted to validate the proposed multiple-query RGBPM planner’s efficacy in finding feasible paths without collision using a six-DOF manipulator (KUKA LBR iiwa 14R820) in a complex environment with dozens of surrounding obstacles.

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

Cited by 32 publications

References 23 publications

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

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

Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Exploring a Novel Multiple-Query Resistive Grid-Based Planning Method Applied to High-DOF Robotic Manipulators

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