Robot trajectory planning method based on genetic chaos optimization algorithm

Zhang, Qiwan; Yuan, Mingting; Song, Rui

doi:10.1109/icar.2017.8023673

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…Subsequently, a genetic chaotic optimization algorithm, merging genetic and chaotic algorithms, is applied. The study demonstrates, through simulation and analysis, that this approach, considering velocity, acceleration, and acceleration constraints, achieves a smooth and time-optimal trajectory for the robot's end-effector [100]. The algorithm leverages the randomness and sensitivity of the chaotic nature to enhance search diversity.…”

Section: Chaos Optimization Algorithmmentioning

confidence: 99%

Review of path planning of welding robot based on spline curve

He,

Teo,

Moey

2024

J. Mech. Eng. Sci.

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This paper assesses the efficacy of intelligent path planning for welding robots utilizing splines. Traditional path planning methods can result in inefficient and inaccurate welding operations. The study reviews current research and case studies to appraise the practical application of spline-based path planning across diverse industrial scenarios. It underscores the benefits of discovering the shortest path and reducing cycle time while acknowledging challenges such as calibration accuracy and sensitivity to sensor data noise. The introduction of artificial intelligence algorithms in automobile welding path planning enables a more precise replication of the body's design curve, ensuring the continuity and smoothness of the welding process. This, in turn, fosters further automation and optimization of the automotive welding manufacturing process. The current research concentrates on integrating intelligent optimization algorithms and spline curves to provide an efficient and intelligent method for welding path planning. Intelligent path planning based on spline curves demonstrates significant potential in enhancing welding efficiency, determining the shortest path, and holds promising applications in the broader research field of welding path planning.

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Section: Chaos Optimization Algorithmmentioning

confidence: 99%

Review of path planning of welding robot based on spline curve

He,

Teo,

Moey

2024

J. Mech. Eng. Sci.

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“…In [Porawagama 2014] the authors introduce a new trajectory planning method for generating bounded and continuous jerk trajectories in joint space using polynomial segmented 5-3-5 splines. In [Chen 2011] the authors propose computational techniques to find the maximal acceleration and minimal jerk along the trajectory and in [Zhang 2017] a new robot trajectory planning method is introduced based on a genetic chaos optimization algorithm. An algorithm for jerk bounded Synchronized Trigonometric S-curve Trajectory (STST) and the 'forbidden-sphere' technique to avoid obstacles have also been proposed in [Perumaal 2013] and in [Komak 2018] authors propose smoothed collision-free trajectory around the obstacles.…”

Section: Introductionmentioning

confidence: 99%

Dimensional Optimization of the Robotic Arm to Reduce Energy Consumption

Mostýn¹,

Huczala²,

Moczulski³

et al. 2020

MM SJ

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This study examines selected components of the optimization function, used to evaluate the optimal kinematic structure of a robot for a given task. Automated generation of the kinematic structure is based on scalable drive modules of the joints and modules of the carrying arms with a check on the permissible torque of the drive and bending moment of the carrier element. An optimization algorithm is used to generate variations of kinematic structures, the base requirement of the fitness function is the ability to traverse a given trajectory with a defined orientation of the tool. The suitability of a given kinematic structure is evaluated further by a set of evaluation functions such as a check for spatial collisions, energy consumption, minimization of total weight, minimization of degrees of freedom for a given task and several other criteria. Two of these criteria -evaluation of the total weight of a robotic arm with drives in joints and evaluation of power consumption for a defined handling task are examined here.

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