arXiv subjects

Wule Mao

Publications and source records attributed to Wule Mao.

3 recordsLinked to original sources

arXiv2025

Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition

Achieving safe, efficient, and kinematically feasible planning in dynamic environments remains a significant challenge, as planners must simultaneously handle moving obstacles, sensor uncertainty, and strict motion constraints. To address this problem, we propose an energy-parameterized diffusion planning framework that learns a conservative energy field to realize safe and stable generalization across diverse scenarios. The energy-parameterized diffusion formulation enables flexible integration of multiple constraints, allowing the planner to generalize to previously unseen environments without retraining. To ensure real-time safety during deployment, we further incorporate a lightweight safety filter that enforces safety and kinematic feasibility constraints in real-time. Additionally, we develop a scene-agnostic, MPC-based data generation pipeline to produce large-scale, dynamically feasible training trajectories. In simulation, the proposed method achieves real-time performance with a mean planning time of 0.21s and a low planning failure rate of 0.57%. Real-world experiments on the F1TENTH platform further validate the effectiveness of the proposed framework. Under sensor uncertainty in previously unseen dynamic environments, the planner consistently generates collision-free trajectories, which remain safe after being tracked by a simple controller, maintaining a mean obstacle clearance of 0.26 m, demonstrating strong robustness and practical applicability. Project page: https://rstp-comp-diffuser.github.io.

cs.RO↗

arXiv2025

FSDP: Fast and Safe Data-Driven Overtaking Trajectory Planning for Head-to-Head Autonomous Racing Competitions

Generating overtaking trajectories in autonomous racing is a challenging task, as the trajectory must satisfy the vehicle's dynamics and ensure safety and real-time performance running on resource-constrained hardware. This work proposes the Fast and Safe Data-Driven Planner to address this challenge. Sparse Gaussian predictions are introduced to improve both the computational efficiency and accuracy of opponent predictions. Furthermore, the proposed approach employs a bi-level quadratic programming framework to generate an overtaking trajectory leveraging the opponent predictions. The first level uses polynomial fitting to generate a rough trajectory, from which reference states and control inputs are derived for the second level. The second level formulates a model predictive control optimization problem in the Frenet frame, generating a trajectory that satisfies both kinematic feasibility and safety. Experimental results on the F1TENTH platform show that our method outperforms the State-of-the-Art, achieving an 8.93% higher overtaking success rate, allowing the maximum opponent speed, ensuring a smoother ego trajectory, and reducing 74.04% computational time compared to the Predictive Spliner method. The code is available at: https://github.com/ZJU-DDRX/FSDP.

cs.RO↗

arXiv2024

An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency

Generating overtaking trajectories in high-speed scenarios is typically addressed through hierarchical planning, which often suffers from local optima due to single initial solutions and low computational efficiency during numerical optimization. To overcome these limitations, this paper proposes a Spatio-temporal topology and Reachable set analysis enhanced Overtaking trajectory Planning framework (SROP). Specifically, by introducing topological classes to represent distinct overtaking behaviors, the upper-layer planner performs a spatio-temporal search to extract diverse initial paths, effectively preventing local optima. Subsequently, a lower-layer planner conducts parallel trajectory evaluation using reachable sets, which decouples vehicle kinematic constraints from the optimization process to ensure feasibility and significantly accelerate computation. Numerical experiments demonstrate that SROP improves trajectory smoothness by 66.8% and reduces computation time by 62.9% compared to state-of-the-art methods. Furthermore, by seamlessly integrating the method into the F1TENTH autonomous racing simulation platform, a 100-lap sensitivity analysis demonstrates high overtaking success rates in challenging scenarios, thereby validating its practical utility, real-time efficiency, and robustness.

cs.RO↗