An Adaptive Whale Optimisation-Based Obstacle Modelling

This study presents an adaptive obstacle modelling approach for energy-efficient robotic arm motion planning in unstructured environments. Conventional representations, such as axis-aligned bounding boxes (AABB), often oversimplify irregular obstacles, leading to con- servative trajectories, redundant avoidance, and increased energy consumption. To address this limitation, obstacles are first converted into point clouds, and an improved adaptive whale optimisation algorithm (IAWOA) is employed to generate a set of adaptively dis- tributed spheres with variable radii. This representation preserves geometric fidelity while providing a compact and planning-friendly collision constraint model. Furthermore, a multi-objective motion planning framework is formulated by jointly minimising robotic arm energy consumption and configuration-space (C-space) trajectory length. Simulation experiments conducted in the Bullet physics environment demonstrate that, compared with AABB-based methods, the proposed approach reduces total trajectory energy consumption by 31%–66% and shortens C-space distance by 12.5%–37%, while producing smoother joint motions across diverse unstructured scenarios. The proposed modelling and planning framework is applicable to robotic manipulators and can be extended to mobile robots, aerial robots, and other autonomous systems operating in complex environments.