使用混合优化方法进行工业机器人多目标轨迹规划

IF 1.9 4区 计算机科学 Q3 ROBOTICS Robotica Pub Date : 2024-05-10 DOI:10.1017/s0263574724000766
Taha Chettibi
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引用次数: 0

摘要

本文提出了一种分四个阶段解决工业机器人多目标轨迹规划问题的混合方法。在第一阶段,通过对连续的机器人配置变量采用适当的参数化方案,将原始问题转化为标准的多目标参数优化问题。然后,在第二阶段,使用基于群体的搜索元启发式进行全局搜索,以建立帕累托前沿(PF)的第一近似值。在第三阶段,使用确定性算法在帕累托前沿近似的每个解的邻域进行局部搜索,以生成新的解。最后,在第四阶段,收集全局搜索和局部搜索的结果,并使用多目标直接搜索法进行后处理,以提高折中解决方案的质量,并向真正的最优 PF 靠拢。通过结合不同的优化技术,我们不仅打算改进优化策略的整体搜索机制,而且所产生的混合算法应保持基于种群算法的鲁棒性,同时享有确定性部分收敛的理论特性。同时,所提出的方法具有模块化和灵活性的特点,可以根据不同阶段的应用技术以不同的方式实现。在本文中,我们考虑了各种数值优化库中的不同技术,并将其合理地结合起来,在各种案例研究中进行了测试,从而说明了混合框架的效率。
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Multi-objective trajectory planning for industrial robots using a hybrid optimization approach
In this paper, a hybrid approach organized in four phases is proposed to solve the multi-objective trajectory planning problem for industrial robots. In the first phase, a transcription of the original problem into a standard multi-objective parametric optimization problem is achieved by adopting an adequate parametrization scheme for the continuous robot configuration variables. Then, in the second phase, a global search is performed using a population-based search metaheuristic in order to build a first approximation of the Pareto front (PF). In the third phase, a local search is applied in the neighborhood of each solution of the PF approximation using a deterministic algorithm in order to generate new solutions. Finally, in the fourth phase, results of the global and local searches are gathered and postprocessed using a multi-objective direct search method to enhance the quality of compromise solutions and to converge toward the true optimal PF. By combining different optimization techniques, we intend not only to improve the overall search mechanism of the optimization strategy but also the resulting hybrid algorithm should keep the robustness of the population-based algorithm while enjoying the theoretical properties of convergence of the deterministic component. Also, the proposed approach is modular and flexible, and it can be implemented in different ways according to the applied techniques in the different phases. In this paper, we illustrate the efficiency of the hybrid framework by considering different techniques available in various numerical optimization libraries which are combined judiciously and tested on various case studies.
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来源期刊
Robotica
Robotica 工程技术-机器人学
CiteScore
4.50
自引率
22.20%
发文量
181
审稿时长
9.9 months
期刊介绍: Robotica is a forum for the multidisciplinary subject of robotics and encourages developments, applications and research in this important field of automation and robotics with regard to industry, health, education and economic and social aspects of relevance. Coverage includes activities in hostile environments, applications in the service and manufacturing industries, biological robotics, dynamics and kinematics involved in robot design and uses, on-line robots, robot task planning, rehabilitation robotics, sensory perception, software in the widest sense, particularly in respect of programming languages and links with CAD/CAM systems, telerobotics and various other areas. In addition, interest is focused on various Artificial Intelligence topics of theoretical and practical interest.
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