Receding Horizon Smooth Trajectory Generation and Tracking for Autonomous Mapless Racing

Classical map-based approaches to autonomous racing are known for their high performance, as they can compute globally optimal reference trajectories for the vehicle to follow. However, these methods are limited to pre-mapped environments, which restricts their applicability in fully autonomous systems that must operate in inherently unknown or unmapped settings. In contrast, mapless racing approaches eliminate the need for a global map by relying solely on sensor data to construct a local representation of the environment. Despite their advantage of not requiring global maps, existing mapless methods often fall short in performance, particularly struggling to sustain high speeds through corners. To overcome this limitation, this paper propose a receding horizon minimum-curvature based trajectory planner that operates within local map framework, enabling high performance racing using only local observations and removing the reliance on global reference. The proposed method is integrated with a model predictive control (MPC) scheme, which ensures accurate trajectory tracking while respecting the vehicle’s physical constraints. As a result, the vehicle is able to take corners more effectively, advancing the capabilities of autonomous racing in unknown environments. Both Gazebo simulations and real-world experiments demonstrate that the proposed mapless racing method achieves better performance compared to the other two mapless methods. Racing videos from the Gazebo simulation and real-world experiments can be found at https://sites.google.com/view/recedinghorizonplanner.