This paper addresses the issue of energy-efficient trajectory optimization for planetary surface manipulators under kinematic and dynamic constraints. To mitigate the inefficiency of existing algorithms, an adaptive boundary adjustment strategy for the multi-dimensional decision space is proposed, which modifies the time intervals between neighboring configuration nodes, enabling precise adaptation of the decision space boundaries. Additionally, a complementary dual-archive guided boundary exploration strategy is introduced to connect the feasible and infeasible regions, allowing for the effective utilization of information from infeasible solutions near the constraint boundaries. This heuristic approach guides the particle swarm in efficiently exploring areas close to the constraints, significantly enhancing the evolutionary optimization capability of the swarm. Furthermore, a swarm optimal position updating strategy based on sparsity sorting is developed. This guides the particle swarm to concentrate on exploring positions where non-dominated solutions on the Pareto front are more sparsely distributed, ensuring uniformity and completeness in the final Pareto front. Finally, the aforementioned strategies are integrated into a heuristic multi-objective particle swarm optimization (HMOPSO) algorithm for the trajectory optimization of manipulators. Comparative experiments are conducted with HMOPSO and existing advanced algorithms in the field of multi-objective optimization. Experimental results demonstrate that HMOPSO exhibits superior evolutionary optimization capabilities and faster convergence rates. Moreover, performance metrics such as inverse generation distance and dominant area during the iterative process of HMOPSO significantly outperform those of existing optimization algorithms.

This paper presents a trajectory planning method based on multi-objective optimization, including time optimal and jerk optimal for the manipulators in the presence of obstacles. The proposed method generates a trajectory configuration in the joint space with kinematic and obstacle constraints using quintic B-spline. Gilbert–Johnson–Keerthi detecting algorithm is utilized to detect whether there is a collision and obtain the minimum distance between the manipulator and obstacles. The degree of constraint violations is introduced to redefine the Pareto domination, and the constrained multi-objective particle swarm algorithm (CMOPSO) is adopted to solve the time-jerk optimization problem. Finally, the Z-type fuzzy membership function is proposed to select the best optimal solution in the Pareto front obtained by CMOPSO. Test results show the effectiveness of the proposed method.

The control of dynamic spatial posture for cantilever roadheader is one of the vital problems for intelligent mining, which directly affect the forming quality of cutting tunnel. Therefore, this paper proposed an intelligent optimal combination compensation strategy to adjust the real-time dynamic posture of cantilever roadheader. First, based on the topological structure analysis of cantilever roadheader, the structural loop compensation model for spatial posture deviation was established. Afterward, the principal component analysis (PCA) and multi-objective particle swarm optimization (MPSO) algorithm were applied to improve the analysis speed and accuracy of posture deviation. Finally, parallel dynamic cooperative optimization (PDCO) strategy was combined to achieve the accurate adjusting of posture deviation. The actual experimental and application results indicate that the intelligent optimal combination compensation strategy proposed in the paper can significantly improve the accuracy of the cutting tunnel. The intelligent optimal compensation strategy proposed in this paper transforms the transient spatial posture deviation into structural loop compensation, and implements by parallel cooperative strategy, finally to realize the fast analysis and efficient implementation of spatial dynamic posture deviation for cantilever roadheader during cutting process. The work of this paper provides an effective reference for intelligent deep and remote underground mining, and it can also be applied to effective control of dynamic spatial posture for intelligent engineering machinery products.