Enhancing arithmetic optimization algorithm for robot path planning in dynamic environments

Abstract

Autonomous robot path planning in dynamic environments poses significant challenges for many industries: military, industrial automation, and security, to name a few. The goal is to fairly and safely navigate both unpredictable and complex spaces as efficiently as possible, however, traditional planning algorithms lack the speed and versatility needed to react to a dynamic landscape. For this reason, there is a need for new solutions which enhance efficiency of the algorithm in complex environment with aid of AI techniques. This thesis contributes to minimizing the above challenges by presenting the Hybrid Arithmetic Optimization Algorithm (HAOA) which integrates adaptive arithmetic‐ operator updates, online reinforcement learning, and spline‐based path refinement. HAOA works by creating far and near candidate trajectories and subjecting them to multiplicative and additive arithmetic transformations before assessing progress. This initial updating process allows the algorithm to transition from a global exploration state back to a local exploitation state. A Q-learning agent observes the incremental changes along the candidate trajectories to begin to update key parameters, which allows the algorithm to adapt to the dynamic environment. Finally, the algorithm performs a final refinement of its solution by executing self-intersection removal, waypoints pruning, and B-spline smoothing so it can deliver continuous and collision free paths for a real robot to follow its path with minimal acceleration discontinuities. Extensive experiments on four classical benchmark functions (Sphere, Rosenbrock, Rastrigin, Ackley) and five grid-based scenarios featuring both static and dynamic obstacles demonstrate HAOA’s superior performance. Compared to standard AOA and a Genetic Algorithm baseline, HAOA converges 15–25 % faster, attains lower final objective values, and yields paths that are on average 10–12 % shorter and substantially smoother. Although embedding reinforcement learning introduces moderate per-iteration overhead, the overall wall-clock time to reach target performance is reduced due to accelerated convergence. These results underscore HAOA’s promise as a robust, adaptable framework for real-time autonomous navigation in complex and unpredictable settings.