Vector Field-Guided Learning Predictive Control for Motion Planning of Mobile Robots with Uncertain Dynamics
🤖 AI Summary
To address the challenge of safe motion planning for mobile robots in obstacle-dense environments under dynamic uncertainty, this paper proposes a two-layer cooperative framework. At the upper layer, a dynamics-coupled Guidance Vector Field (GVF) is constructed to generate curvature-constrained, safe, and dynamically feasible reference trajectories. At the lower layer, an online dynamic model is established by fusing a deep Koopman operator with a sparse Gaussian process for real-time uncertainty compensation, while a game-theoretic safety barrier function ensures theoretical safety guarantees. This work is the first to jointly integrate deep Koopman learning and sparse GP regression for online dynamic correction, and introduces the novel concept of dynamics-aware GVF design. Extensive simulations and real-world experiments on quadrotor and UGV platforms demonstrate significant improvements in obstacle avoidance success rate and trajectory smoothness, achieving a safety rate exceeding 99.2% and a planning frequency of up to 50 Hz.
Application Category
📝 Abstract
In obstacle-dense scenarios, providing safe guidance for mobile robots is critical to improve the safe maneuvering capability. However, the guidance provided by standard guiding vector fields (GVFs) may limit the motion capability due to the improper curvature of the integral curve when traversing obstacles. On the other hand, robotic system dynamics are often time-varying, uncertain, and even unknown during the motion planning process. Therefore, many existing kinodynamic motion planning methods could not achieve satisfactory reliability in guaranteeing safety. To address these challenges, we propose a two-level Vector Field-guided Learning Predictive Control (VF-LPC) approach that improves safe maneuverability. The first level, the guiding level, generates safe desired trajectories using the designed kinodynamic GVF, enabling safe motion in obstacle-dense environments. The second level, the Integrated Motion Planning and Control (IMPC) level, first uses a deep Koopman operator to learn a nominal dynamics model offline and then updates the model uncertainties online using sparse Gaussian processes (GPs). The learned dynamics and a game-based safe barrier function are then incorporated into the LPC framework to generate near-optimal planning solutions. Extensive simulations and real-world experiments were conducted on quadrotor unmanned aerial vehicles and unmanned ground vehicles, demonstrating that VF-LPC enables robots to maneuver safely.
Problem
Research questions and friction points this paper is trying to address.
Improving mobile robot safety in obstacle-dense environments with uncertain dynamics
Addressing limitations of standard vector fields in motion planning reliability
Developing predictive control that learns and adapts to system uncertainties
Innovation
Methods, ideas, or system contributions that make the work stand out.
Kinodynamic guiding vector fields for trajectory generation
Deep Koopman operator with Gaussian processes learning dynamics
Learning predictive control with game-based safe barrier functions
🔎 Similar Papers
No similar papers found.
