This paper presents a guide path-free multimodal trajectory planning framework for autonomous surface vehicles operating in dynamic environments that alleviates the local-minimum and deadlock problems of single-mode MPC while maintaining computational efficiency.
This paper presents a fast adaptive planning method for tractor-trailer systems that resolves parameter uncertainties by adapting a nominal trajectory to true parameters via iLQR, eliminating the need to store MPs for various configurations.
High-precision control of heavy-duty autonomous ground vehicles (AGVs) in cluttered factories requires paths that account for the full vehicle–cargo envelope and numerically reliable reference trajectories across widely varying segment durations. This paper presents a two-stage planning and reference-generation framewo...
The proposed Turn-Deviation Optimized Multi-Stage Framework (TDO-MSF) improves trajectory smoothness and obstacle-avoidance performance while maintaining curvature-constrained and trajectory-guided obstacle-avoidance capabilities.
This work introduces a control-informed online trajectory planning framework that learns from its own execution errors, and demonstrates that feeding control-induced deviations back into the planning layer unlocks performance previously inaccessible to modular architectures and enables autonomous vehicles to exploit tr...
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