Path planning algorithm for space manipulator with a minimum energy demand

Chung, Wing Kwong; Xu, Yangsheng

doi:10.1109/robio.2012.6491189

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Many scholars have studied this issue. James T. Allison proposed plant-limited co-design (PLCD) that is a design methodology for meeting new requirements at a minimum cost through limited physical systems [3], Wing Kwong Chung [4] designed and developed a novel genetic operator with an optimization goal of searching a path with a minimum energy demand, and Abhishek Agrawal proposed a design methodology which may allow a robot to approximately follow desired trajectories without actuator inputs [5]. Further, Amin Nikoobin [6] proposed a nonlinear disturbance observer to analyze the stability of a manipulator to get superior tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Multi-DOF Robotic Manipulator Trajectory Controlling based on Minimum Energy Optimization

Du¹,

Du²,

Chen³

et al. 2015

Advances in Intelligent Systems Research

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Abstract-This paper presents a methodology for determining an optimal trajectory based on minimizing energy consumption. Firstly we analyze a work process of a robotic manipulator between two given configurations at a given time, and the effect of main factors on motor energy consumption is also analyzed. A mathematical model of torque is used for obtaining a mathematical expression of each joint's torque and angle relationships. More specifically, for a multi-variable, higher order total torque model, a variational principle is used to obtain many differential equations for a multi-DOF (degrees of freedom) robotic manipulator, and iterative optimization is used to determine the optimal trajectory. Finally, a simulation shows that the optimal trajectory can save energy compared with two other trajectories.

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Section: Introductionmentioning

confidence: 99%

Multi-DOF Robotic Manipulator Trajectory Controlling based on Minimum Energy Optimization

Du¹,

Du²,

Chen³

et al. 2015

Advances in Intelligent Systems Research

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Minimizing energy usage in multi-point manufacturing: a dimensional transfer learning strategy

Liu,

Yap,

Khairuddin

2024

Journal of Computational Design and Engineering

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In this study, a minimum energy consumption scheduling strategy is proposed tailored to address Multi-Point Manufacturing (MPF) problems, such as drilling and spot welding tasks. Firstly, the MPF problem is formulated as a Multi-dimensional Weighted Traveling Salesman Problem (TSP), subsequently employing a Graph Neural Network (GNN)-based neural method for resolution. To the best of our knowledge, the proposed model is the first to utilize neural methodologies in scheduling MPF problems. During the neural network training phase, Transfer Learning is leveraged to utilize existing planning experience. A low-dimensional representation is first approximated using Self-Adjusting Multi-modal Artificial Neural Networks (SAMANN), and the entire model undergoes fine-tuning to enhance planner performance. It is found that the new proposed approach surpasses the performance of the current state-of-the-art (SOTA) neural model in terms of both solution quality and training time across multi-dimensional TSPs and MPF problem domains.

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Urban travel demand estimation using genetic algorithm

Saini

Sinha

Subramanian

2015

2015 International Conference on Cognitive Computing and Information Processing(CCIP)

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Constant increase in urban road traffic is forcing traffic authorities to confront unavoidable congestion problems these days. This creates new challenges for city planners to generate traffic routes with better designs and improve the existing roads. In order to analyze and improve traffic routes, there is a need to understand movement patterns of major vehicular traffic inside the city. Origin Destination(OD) estimation is one such method for understanding the movement patterns. The objective is to find an optimal OD matrix for city traffic, which is a subtle process for classic algorithms. The solution to this problem is Genetic Algorithm (GA) approach which is a search heuristics to find optimal solution from a set of random solutions. A Genetic Algorithm approach has been developed to find an optimal OD matrix, showing the actual travel patterns of significant number of vehicles inside the city. Once the best OD matrix is obtained, it yields a big opportunity for traffic planners to analyze and improve traffic scenario. Genetic Algorithm can play a major role in solving complex problems. In this paper, one such problem of estimating OD matrix of a city is discussed and implemented. In conjunction to genetic algorithm, Mean Absolute Percentage Error(MAPE) has been used as a fitness criteria for finding the optimal OD matrix.

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Path planning algorithm for space manipulator with a minimum energy demand

Cited by 3 publications

References 19 publications

Multi-DOF Robotic Manipulator Trajectory Controlling based on Minimum Energy Optimization

Multi-DOF Robotic Manipulator Trajectory Controlling based on Minimum Energy Optimization

Minimizing energy usage in multi-point manufacturing: a dimensional transfer learning strategy

Urban travel demand estimation using genetic algorithm

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