Real-time motion planning in cluttered and dynamically evolving environments remains challenging due to the need to ensure rapid convergence, collision avoidance, computational efficiency, and robustness against local minima under frequent changes. Although sampling-based planners such as RRTX* and ABIT* provide strong theoretical guarantees, their practical deployment in dense dynamic scenarios is often limited by high sampling overhead and computational latency. This paper proposes a Fast Converging Adaptive Algorithm (FCAA), a deterministic sampling-based framework integrating adaptive sampling density, temperature-controlled exploration, and dynamic step-size regulation within a unified heating and annealing mechanism. The temperature parameter governs both the spatial sampling band and incremental expansion radius, enabling controlled transitions between goal-directed expansion and stochastic exploration when stagnation occurs. The algorithm is evaluated using a two-stage protocol comprising intrinsic validation and benchmarking. Across 36 environments with obstacle densities ranging from 3% to 20% and velocities between −30 and +30 m/s, FCAA achieved a 100% success rate within the defined experimental design while maintaining path quality comparable to or better than RRTX* and ABIT*. Unlike the reference planners, which typically required tens of thousands of samples and seconds of computation, FCAA operated with substantially reduced sampling effort, typically tens of nodes, and planning times from 0.1 to 320 ms depending on scenario complexity. Within the simulation framework, the results indicate that the proposed temperature-regulated strategy enables fast and computationally efficient motion planning under dynamic constraints, making FCAA suitable for time-critical robotic navigation scenarios.
Khalid et al. (2026) studied this question.