NMPCB: A Lightweight and Safety-Critical Motion Control Framework (2505.01752v1)

Abstract: In multi-obstacle environments, real-time performance and safety in robot motion control have long been challenging issues, as conventional methods often struggle to balance the two. In this paper, we propose a novel motion control framework composed of a Neural network-based path planner and a Model Predictive Control (MPC) controller based on control Barrier function (NMPCB) . The planner predicts the next target point through a lightweight neural network and generates a reference trajectory for the controller. In the design of the controller, we introduce the dual problem of control barrier function (CBF) as the obstacle avoidance constraint, enabling it to ensure robot motion safety while significantly reducing computation time. The controller directly outputs control commands to the robot by tracking the reference trajectory. This framework achieves a balance between real-time performance and safety. We validate the feasibility of the framework through numerical simulations and real-world experiments.

Insights into NMPCB: A Lightweight and Safety-Critical Motion Control Framework

In the paper titled "NMPCB: A Lightweight and Safety-Critical Motion Control Framework," the authors propose an innovative framework designed to address safety and real-time performance in robot motion control within multi-obstacle environments. The framework, termed NMPCB, integrates a neural network-based path planner and a model predictive control (MPC) controller enhanced with control barrier functions (CBF), targeting a delicate balance between computational efficiency and safety.

Key Components and Methodology

The NMPCB framework is comprised of two core components:

1. Neural Network-Based Path Planner: This component employs a lightweight neural network that serves as a predictive tool for future target points in the robot's trajectory. The network is designed with an encoder-decoder architecture. Historical path data is processed to forecast subsequent positions which are used to formulate Dubins paths, acknowledged for their optimal curvature, as reference trajectories.
2. MPC with Dual Control Barrier Function (MPC-DCBF): This controller capitalizes on a novel use of CBFs to manage obstacle avoidance constraints. The dual CBF strategy innovatively bypasses traditional computational burdens by using dual problem formulations that facilitate real-time application. The dual CBF constraints ensure that control commands allow the robot to steer clear of obstacles with heightened precision and reduced computational time.

Numerical and Experimental Validation

The framework’s efficacy was validated through a blend of numerical simulations and real-world experimental deployments. Comparison across different algorithm setups, such as Dubins Curves paired with MPC-DCBF versus Neural Dubins Model integrated with the enhanced dual DCBF controller, demonstrated significant improvements in both trajectory planning and safety outcomes. Simulation results highlighted the robustness of NMPCB in yielding collision-free paths in complex and cluttered environments. Empirical analysis through robotic experiments further validated these findings, underscoring the framework’s adaptability in dynamic scenarios with system setups like the Ackermann steering robot platform.

Strong Numerical Results

From the experiments conducted, the NMPCB framework showed an impressive reduction in computation time while maintaining a high success rate for collision avoidance. In numerous simulated trials, the framework outperformed conventional methods, particularly in high-density obstacle scenarios. The MDD-I variant, leveraging the dual DCBF, was found to present substantial computational advantages without sacrificing trajectory optimization quality.

Contributions and Implications

The paper makes distinct contributions by improving neural network-based path planning in robotics and reinforcing safety constraints through dual CBFs. It addresses the pressing challenge of real-time computational demands in robotics, particularly for non-linear kinematic systems which frequently encounter solution failures.

Theoretically, the integration of dual CBFs with MPC paves the path for new investigations into model predictive control methodologies, with potential expansions to other robotic systems beyond ground vehicles. Practically, the NMPCB framework holds promise for applications in autonomous vehicles, drones, or industrial robotics, where both real-time decision-making and operational safety are paramount.

Future Research Directions

Future advancements may explore refining the neural network models to further boost prediction accuracy and generalize across more diverse environmental contexts. Furthermore, exploring alternative optimization formulations could catalyze even faster computational efficiencies, expanding the framework's applicability in more resource-constrained settings.

In conclusion, NMPCB represents a crucial step towards achieving a sophisticated balance between safety and performance in motion control frameworks, establishing itself as a competitive methodology within the robotics research landscape.