HybridDWAVO: Finite-horizon velocity-obstacle filtering within the dynamic window approach for dynamic obstacle avoidance

This article proposes HybridDWAVO, a deterministic local-planning framework that combines the dynamics-aware trajectory generation of the dynamic window approach with the predictive collision-avoidance capability of the velocity obstacle method. The proposed framework uses finite-horizon velocity obstacle filtering as a pre-evaluation feasibility step applied to the dynamic window approach–admissible velocity set. As a result, predictively unsafe candidates are removed before dynamic window approach trajectory scoring, and the dynamic window approach objective function is evaluated only over the remaining velocity obstacle–filtered safe candidates. The framework is evaluated in controlled simulations using deterministic dynamic window approach–based planner variants under matched parameter settings, obstacle trajectories, random seeds, and computational environment. To support a simulation-based assessment, the evaluation includes implementation-relevant factors such as robot-state estimation noise, obstacle-perception uncertainty, sensing and control latency, bounded actuation, and nonholonomic velocity constraints. The analysis combines repeated-trial ablation results, obstacle-density-dependent navigation outcomes, runtime and candidate-processing metrics, and representative trajectory-level case studies. The repeated-trial results indicate that the proposed pre-evaluation velocity obstacle–filtering variant improves goal-completion consistency and reduces collision counts relative to conventional dynamic window approach under the tested dynamic-obstacle conditions. In addition, the ablation and candidate-processing results suggest that placing velocity obstacle filtering before trajectory scoring reduces the number of candidates entering dynamic window approach objective-function evaluation while maintaining favorable navigation outcomes among the tested variants; the associated runtime results are interpreted within the specified simulation and workstation environment. Overall, the results provide simulation-based algorithmic evidence for the proposed dynamic window approach–velocity obstacle integration structure, while physical robot validation and broader benchmarking under real sensing, actuation, communication, and environmental conditions remain necessary future work.