An Improved Biomimetic Beaver Behavior Optimizer for Inverse Kinematics of Rehabilitation Robotic Arms

Accurate inverse kinematics for rehabilitation robotic arms remains challenging because of strong nonlinearity, multiple feasible joint configurations, and strict joint-limit constraints. Inspired by the cooperative construction, adaptive exploration, and collective information-sharing behaviors of beavers, this study develops an improved biomimetic beaver behavior optimizer (IBBO) for optimization-based inverse kinematics solving. In the proposed framework, biologically inspired cooperative search is translated into an engineering-oriented numerical strategy through four complementary mechanisms: a strict elitist replacement with rollback to preserve population fitness consistency, a momentum-inspired information transfer scheme to accumulate effective search directions, a lightweight memetic coordinate-wise local search to strengthen late-stage exploitation, and an adaptive builder-disturbance schedule to progressively shift the search from exploration to refinement. The optimization capability of IBBO is first evaluated on the CEC2017 benchmark suite, where it demonstrates competitive accuracy and robustness. It is then applied to inverse kinematics solving for representative rehabilitation robotic arms by minimizing pose errors under joint constraints. The experimental results show that IBBO can consistently generate feasible joint solutions with improved terminal pose accuracy and stable convergence compared with baseline metaheuristics. Beyond numerical improvement, this study provides a biomimetic optimization framework that transfers beaver-inspired cooperative behaviors into rehabilitation robotics, offering an effective computational approach for constrained inverse kinematics problems.