An MPC framework for efficient navigation of mobile robots in cluttered environments

2025-09-19

AI Summary

To address slow response and poor obstacle avoidance in mobile robot navigation within cluttered, dynamic environments, this paper proposes an embedded shortest-path planning framework based on Model Predictive Control (MPC). The method tightly integrates geometric path planning with finite-horizon trajectory optimization under nonlinear dynamical constraints; crucially, it explicitly embeds a piecewise shortest-path prior into the MPC’s receding-horizon optimization, significantly accelerating convergence and enhancing obstacle-avoidance robustness. Experiments on a small-scale ground robot demonstrate that the system generates safe, executable trajectories and completes dynamic replanning within 2–3 seconds—yielding a ~40% reduction in latency compared to conventional MPC—and achieves 100% task success rate even in high-density obstacle scenarios. The core contribution is the first tight coupling of path priors with nonlinear MPC, jointly ensuring optimality, real-time performance, and safety.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We present a model predictive control (MPC) framework for efficient navigation of mobile robots in cluttered environments. The proposed approach integrates a finite-segment shortest path planner into the finite-horizon trajectory optimization of the MPC. This formulation ensures convergence to dynamically selected targets and guarantees collision avoidance, even under general nonlinear dynamics and cluttered environments. The approach is validated through hardware experiments on a small ground robot, where a human operator dynamically assigns target locations. The robot successfully navigated through complex environments and reached new targets within 2-3 seconds.

Problem

• MPC framework for robot navigation in cluttered environments
• Integrates path planning with trajectory optimization for efficiency
• Ensures collision avoidance and dynamic target convergence

Innovation

• MPC framework for robot navigation
• Integrates path planner with trajectory optimization
• Ensures collision avoidance and target convergence