ArticleSensors (Basel, Switzerland)2026
Trajectory Planning Method for a Robotic Arm Based on an Improved Multi-Objective Golden Jackal Optimization Algorithm.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
To address the complex challenge of simultaneously optimizing the operation time, motion impact, and energy consumption in industrial robotic arm trajectory planning, this study proposes a novel multi-objective optimization framework based on an improved multi-objective golden jackal optimization (IMGJO) algorithm. Firstly, the original single-objective Golden Jackal Optimization is extended into a multi-objective formulation by integrating an external Pareto archive and a crowding distance sorting mechanism. This extension effectively generates a well-distributed and highly convergent Pareto-optimal solution set. Secondly, to enhance global exploration capabilities and improve convergence stability, the escape energy model is refined. This is achieved through the synergistic integration of three key strategies: tent chaotic mapping for enhancing the initial population diversity, opposition-based learning to accelerate the early-stage search process, and an elitism preservation strategy to prevent premature convergence and mitigate the risk of entrapment in local optima. Thirdly, the IMGJO algorithm is integrated with a 3-5-3 polynomial interpolation scheme to establish a kinematically constrained trajectory planning model, ensuring a generation of smooth, continuous, and dynamically feasible joint space trajectories. Finally, comprehensive comparative experiments against several state-of-the-art benchmark algorithms demonstrate that the proposed IMGJO framework significantly outperforms its counterparts in terms of both convergence speed and the quality of the Pareto solution set. Furthermore, experimental validation on the Yaskawa HP-20D robotic arm platform demonstrates that the proposed method can effectively achieve a comprehensive optimization of execution time, impact, and energy consumption. Compared with the pre-optimization trajectory, the total operation time is reduced by 2.42%; the impacts of Joint 1 and Joint 2 are reduced by 74.65% and 75.82%, respectively; and the energy consumption of Joint 1 and Joint 2 are reduced by 27.11% and 26.83%, respectively. Moreover, the generated trajectory is smooth and continuous, thereby significantly improving the operational efficiency and stability of the robotic arm.
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