MfNeuPAN: Proactive End-to-End Navigation in Dynamic Environments via Direct Multi-Frame Point Constraints

TL;DR

MfNeuPAN addresses robust autonomous navigation in dynamic environments by extending NeuPAN with a predictive, multi-frame perspective. The framework combines a perception pipeline (downsampling, Gaussian filtering, DBSCAN clustering, Kalman tracking), a multi-frame prediction module (three-component Gaussian Mixture Model to generate future obstacle samples), and an enhanced planning/controller (DUNE latent distance features and NRMP optimization) to produce real-time control commands. Across both IR-SIM simulations and real-world experiments, MfNeuPAN demonstrates improved path efficiency and robustness in dense, moving-obstacle scenarios, including zero-collision trials in 20 runs. This work enables proactive, end-to-end navigation in cluttered dynamic spaces, with potential to improve reliability and safety in real-world robotic systems.

Abstract

Obstacle avoidance in complex and dynamic environments is a critical challenge for real-time robot navigation. Model-based and learning-based methods often fail in highly dynamic scenarios because traditional methods assume a static environment and cannot adapt to real-time changes, while learning-based methods rely on single-frame observations for motion constraint estimation, limiting their adaptability. To overcome these limitations, this paper proposes a novel framework that leverages multi-frame point constraints, including current and future frames predicted by a dedicated module, to enable proactive end-to-end navigation. By incorporating a prediction module that forecasts the future path of moving obstacles based on multi-frame observations, our method allows the robot to proactively anticipate and avoid potential dangers. This proactive planning capability significantly enhances navigation robustness and efficiency in unknown dynamic environments. Simulations and real-world experiments validate the effectiveness of our approach.

Figures (5)

• Figure 1: Illustration of our method working in a dynamic environment. When a dynamic obstacle crosses a predetermined trajectory, the robot avoids it in the opposite direction of the predetermined trajectory, demonstrating its proactive planning ability.
• Figure 2: System overview. The obstacle estimation module processes point clouds to estimate obstacle motion states. Prediction forecasts obstacle trajectories. Planning and control generates control series.
• Figure 3: Various Simulation environment: The red radial lines simulate radar data for obstacle detection. The solid red line is the local planner's path, tracking the black route while avoiding obstacles. Black shapes are obstacles, with yellow arrows showing motion direction.
• Figure 4: NeuPAN's reactive obstacle avoidance strategy. In frame n-1 (left), the robot encounters an obstacle. By frame n (right), it reacts by taking a right detour to avoid collision.
• Figure 5: MfNeuPAN's proactive obstacle avoidance strategy. In frame n-1 (left), the robot anticipates an obstacle on the right. By frame n (right), it proactively turns left to avoid the obstacle.