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About this paper
Vinh Nguyen, Gia-Uy Le, Tien-Dat Nguyen, Tri-Tin Nguyen, Vinh-Hao Nguyen
An autonomous navigation system for Ackermann-steered mobile robots was developed and validated by researchers at Ho Chi Minh City University of Technology, focusing on bridging the simulation-to-reality gap for outdoor environments. The system integrates an improved A* path planner and a novel Ackermann Geometric Model Predictive Control (A-GMPC) tracker, yielding smoother paths and enhanced tracking accuracy in both simulated and real-world experiments.
Key insights
- Modifying the A* algorithm with turning cost penalties and post-processing effectively produces significantly smoother paths for non-holonomic robots, reducing total turning angles by over 20 times compared to baseline A*.
- Geometric Model Predictive Control (MPC) formulated on the SE(2) manifold with input-increment optimization consistently outperforms standard MPC in tracking accuracy for Ackermann-steered vehicles, particularly during sharp turns and at higher speeds.
- A dual-layer Extended Kalman Filter (EKF) combining high-rate local odometry with map-level consistency provides robust and accurate real-time state estimation essential for dynamic outdoor navigation.
Summary by alphaXiv.
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