首页|Data from Saad Dahlab University Advance Knowledge in Robotics (Multi-objective Trajectory Planning for Industrial Robots Using a Hybrid Optimization Approach)
Data from Saad Dahlab University Advance Knowledge in Robotics (Multi-objective Trajectory Planning for Industrial Robots Using a Hybrid Optimization Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in Robotics. A ccording to news originating from Blida, Algeria, by NewsRx editors, the researc h stated, “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 mul ti-objective parametric optimization problem is achieved by adopting an adequate parametrization scheme for the continuous robot configuration variables.” Our news journalists obtained a quote from the research from Saad Dahlab Univers ity, “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 Paret o 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 orde r to generate new solutions. Finally, in the fourth phase, results of the global and local searches are gathered and postprocessed using a multi-objective direc t search method to enhance the quality of compromise solutions and to converge t oward the true optimal PF. By combining different optimization techniques, we in tend not only to improve the overall search mechanism of the optimization strate gy but also the resulting hybrid algorithm should keep the robustness of the pop ulation-based algorithm while enjoying the theoretical properties of convergence of the deterministic component. Also, the proposed approach is modular and flex ible, and it can be implemented in different ways according to the applied techn iques in the different phases.”
BlidaAlgeriaAlgorithmsEmerging Tec hnologiesMachine LearningNano-robotRoboticsSaad Dahlab University
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