Reinforced Arctic Puffin Optimization: A Multi-Strategy Fusion Approach with a Case Study in Manipulator Trajectory Planning

In agricultural automation, trajectory planning for fruit-picking robot arms must satisfy dynamic obstacle avoidance and real-time control constraints in complex orchards, forming a high-dimensional, constrained optimization problem. Due to strong nonlinearity and steep gradients, traditional planners often yield high-cost trajectories with unstable quality. This paper introduces a Reinforced Arctic Puffin Optimization (RAPO) algorithm for trajectory planning in high-dimensional, complex, constrained scenarios. RAPO improves Arctic Puffin Optimization (APO), which uses a two-stage foraging strategy but may suffer premature convergence, insufficient population diversity, and weak boundary handling. Dynamic fitness–distance balance (DFDB) adaptively coordinates exploration and exploitation. An elite-pool dynamic search strategy (DEPSS) combines t-distribution perturbation and Lévy flight to maintain diversity and enhance exploitation. A convex-lens opposition-learning boundary control method (CLOBC) improves out-of-bounds handling and reduces invalid search. Stochastic centroid opposition learning (SOBL) further suppresses premature convergence and expands coverage. On the CEC2017 benchmark (30/50/100 dimensions), RAPO outperforms nine algorithms in convergence speed and solution quality, verified by Wilcoxon and Friedman tests. In dense, narrow, and dynamic obstacle scenarios, RAPO achieves the lowest path cost, converges within 30 iterations, reduces variance, and generates smoother trajectories. This case study demonstrates RAPO’s robust mathematical performance, providing a robust and efficient framework for agricultural picking robots.