DYNUS: Uncertainty-aware Trajectory Planner in Dynamic Unknown Environments

2025-04-23

AI Summary

To address the challenge of real-time trajectory invalidation and collision risk in dynamic, unknown environments—where obstacle trajectories are unpredictable—this paper proposes a safe, fast, and adaptive online trajectory planning method. The approach integrates uncertainty-aware modeling with multi-modal trajectory co-optimization. Its key contributions are: (1) a novel spatio-temporal joint hard-constraint local planning framework; (2) a dynamic re-planning mechanism unifying exploration, safety, and emergency response modalities; and (3) an efficient spatio-temporal safe corridor generation algorithm based on variable elimination. Evaluated in simulation and real-world experiments across quadrotor, wheeled, and quadruped robot platforms, the method achieves 100% task success rate and reduces average traversal time by 25.0% compared to state-of-the-art methods.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This paper introduces DYNUS, an uncertainty-aware trajectory planner designed for dynamic unknown environments. Operating in such settings presents many challenges -- most notably, because the agent cannot predict the ground-truth future paths of obstacles, a previously planned trajectory can become unsafe at any moment, requiring rapid replanning to avoid collisions. Recently developed planners have used soft-constraint approaches to achieve the necessary fast computation times; however, these methods do not guarantee collision-free paths even with static obstacles. In contrast, hard-constraint methods ensure collision-free safety, but typically have longer computation times. To address these issues, we propose three key contributions. First, the DYNUS Global Planner (DGP) and Temporal Safe Corridor Generation operate in spatio-temporal space and handle both static and dynamic obstacles in the 3D environment. Second, the Safe Planning Framework leverages a combination of exploratory, safe, and contingency trajectories to flexibly re-route when potential future collisions with dynamic obstacles are detected. Finally, the Fast Hard-Constraint Local Trajectory Formulation uses a variable elimination approach to reduce the problem size and enable faster computation by pre-computing dependencies between free and dependent variables while still ensuring collision-free trajectories. We evaluated DYNUS in a variety of simulations, including dense forests, confined office spaces, cave systems, and dynamic environments. Our experiments show that DYNUS achieves a success rate of 100% and travel times that are approximately 25.0% faster than state-of-the-art methods. We also evaluated DYNUS on multiple platforms -- a quadrotor, a wheeled robot, and a quadruped -- in both simulation and hardware experiments.

Problem

• Plans safe trajectories in dynamic unknown environments
• Ensures collision-free paths with fast replanning
• Handles both static and dynamic 3D obstacles

Innovation

• DGP and Temporal Safe Corridor handle 3D obstacles
• Safe Planning Framework uses multiple trajectory types
• Fast Hard-Constraint reduces problem size for speed