A Modified Biogeography-Based Optimization Approach for Visual Position-Based Inverse Kinematics of Robotic Arms

Precise end-effector positioning of a robotic arm is critical for accurate performance in robotics, which directly impacts the system's performance in applications requiring high precision. This paper presents an optimized tag-based inverse kinematics approach for a 6 DoF robotic arm using a Modified Biogeography-Based Optimization (MBBO) algorithm, which is an enhanced version of the original Biogeography-Based Optimization (BBO), a population-based evolutionary algorithm inspired by the natural distribution of species across habitats. The target position is identified via AprilTag visual fiducial markers and integrated into the inverse kinematics solver. Performance was evaluated against Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and BBO through 3-dimensional simulations involving ten target points. Results show that MBBO achieves reduced positioning error compared to GA, PSO, and BBO, resulting in lower end-effector position errors. The findings highlight the effectiveness of combining visual tag detection with advanced optimization for precise robotic arm control.