SSS–DDQN solution-space best-first search for robust trajectory tracking of Mecanum robots under partial observability

Robust trajectory tracking of Mecanum-wheel mobile robots under partial observability is challenging due to nonlinear motion coupling, noisy measurements, sensor dropout, and external disturbances. In this study, we propose an SSS*–DDQN solution-space best-first search controller for robust trajectory tracking of a four-Mecanum-wheel mobile robot. The proposed framework integrates extended Kalman filter (EKF)-based belief-state estimation, QMDP belief sampling, DDQN value evaluation, and SSS* OPEN-list search with LIVE/SOLVED node refinement. The EKF estimates the hidden robot state under noisy observations and dropout, the DDQN provides learned long-term value evaluation, and the SSS* search refines promising corrective-action clusters using beta-bound pruning, delayed MIN-sibling refinement, bounded OPEN-list management, and secondary lookahead. The controller is evaluated using lemniscate trajectory tracking and compared with EKF-belief PER-DDQN, QMDP-PER-DDQN, Alpha–Beta Minimax, and AB–DDQN Hybrid controllers. The proposed SSS*–DDQN achieved the best overall ranking with RMSE x y = 0.01906 m, MAE x y = 0.016074 m, and a cumulative reward of 1843.6. Compared with the AB–DDQN Hybrid baseline, it reduced RMSE x y by 12.93% and MAE x y by 17.80%. Statistical validation over multiple stochastic trials further showed the lowest mean RMSE x y of 0.01847 ± 0.00132 m and MAE x y of 0.01554 ± 0.00128 m, with narrow 95% confidence intervals. The results confirm that the proposed SSS*–DDQN improves robust action selection and provides consistent tracking performance under partial observability.