Time-optimal trajectory planning for hyper-redundant manipulators in 3D workspaces

Xidias, Elias K.

doi:10.1016/j.rcim.2017.10.005

Cited by 63 publications

(23 citation statements)

References 13 publications

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“…The encoding of the genetic algorithm [21][22][23] is a process of converting the parameters from solution space into genetic spatial. The time target of trajectory planning is encoded by binary coding.…”

Section: Encoding and Decodingmentioning

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: Encoding and Decodingmentioning

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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“…Panames-Garcia et al [21] proposed a general formulation for the optimization of the path placement of redundant manipulators considering single or multiple objective optimizations. Redundancy and path planning can be merged also with heuristic searches, such as by using genetic algorithms [1,28,31] and neural networks [4]. However, while heuristics can be very valuable with a high number of degrees of freedom, they need to evaluate an objective function many times to obtain a suboptimal solution [31].…”

Section: Related Workmentioning

confidence: 99%

“…Redundancy and path planning can be merged also with heuristic searches, such as by using genetic algorithms [1,28,31] and neural networks [4]. However, while heuristics can be very valuable with a high number of degrees of freedom, they need to evaluate an objective function many times to obtain a suboptimal solution [31]. Doan and Lin [6] were able to optimize the task of a redundant robot while finding the best position of the robot base in relation to the task itself.…”

Section: Related Workmentioning

confidence: 99%

Trajectory Optimization of a Redundant Serial Robot Using Cartesian via Points and Kinematic Decoupling

Bottin

Rosati

2019

Robotics

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Moving from a given position to another with an industrial robot can be a challenging problem when the task is redundant around the tool axis. In this case, there are infinite ways of choosing both the starting and the ending configurations, so that the movement between the given points is not uniquely defined. In this paper, an algorithm that calculates the suboptimal movement between two positions is proposed, which automatically generates a cloud of safe via points around the workpiece and then by exploiting such points finds the suboptimal safe path between the two positions that minimizes movement time. The proposed method, in which the search of the suboptimal path is based on graph theory and the Dijkstra algorithm, can iteratively evaluate a high number of starting and ending configurations in low computational time, allowing performing a reasonably wide search of the suboptimal path within the infinite possible motions between the given points.

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“…After finding the way to move the manipulator around an obstacle, we solve the inverse problem of kinematics employing the method of forming a geometric solution to the inverse kinematics problem for chains with kinematic pairs of rotational type only [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Path Planning: Designed Software Implementation

Petrenko¹,

Тебуева²,

Antonov³

et al. 2019

Proceedings of the 21st International Workshop on Computer Science and Information Technologies (CSIT 2019)

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The paper continues developing the method of a real-time quasi-optimal trajectory planning for anthropomorphic manipulator. Our goal includes software implementation of previously designed methods and algorithms of the quasi-optimal trajectory for anthropomorphic manipulator. The general structure and interrelation of the developed methods and algorithms is described, as well as their significance for obtaining the final result. Component diagrams and communication diagrams are presented, on the basis of which the interfaces of software components are built. Their structure and composition of each software component are given. The description of the interfaces of various components of the system allows the use of design components as part of other software systems, for example, designed to control the manipulators of an anthropomorphic robot or as part of other modeling systems. The illustration of the developed software implementation is given. For the software implementation performed by the author, with standard input data, the necessary variable memory capacity is about 212 Kbytes, which allows using the proposed software implementation on the computer systems of existing manipulators in real time.

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Time-optimal trajectory planning for hyper-redundant manipulators in 3D workspaces

Cited by 63 publications

References 13 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

Trajectory Optimization of a Redundant Serial Robot Using Cartesian via Points and Kinematic Decoupling

Energy-Efficient Path Planning: Designed Software Implementation

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