From NLVO to NAO: Reactive Robot Navigation using Velocity and Acceleration Obstacles

2025-06-06

AI Summary

To address unsafe obstacle avoidance and loose decision-making caused by the coupling of nonlinear moving obstacles and robot velocity/acceleration constraints in dynamic environments, this paper proposes a reactive navigation framework based on Acceleration Obstacles (AO) and Nonlinear Acceleration Obstacles (NAO). We extend the Nonlinear Velocity Obstacle (NLVO) theory to the acceleration domain for the first time, explicitly modeling robot kinematic constraints and multi-agent trajectory prediction to construct dynamic obstacle cones and prune the real-time feasible velocity–acceleration space. Integrated with a distributed cooperative collision avoidance algorithm, the framework achieves millisecond-level, collision-free responses. Evaluated in highly dynamic multi-robot scenarios, it significantly improves obstacle avoidance safety, trajectory smoothness, and computational efficiency, enabling real-time deployment of autonomous vehicles in dense pedestrian and vehicular traffic.

Technology Category

Intelligent Robots: Motion and Path PlanningPlanning, Routing, and Scheduling: Replanning and Plan RepairMultiagent Systems: Adversarial Agents

Application Category

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

Abstract

This paper introduces a novel approach for robot navigation in challenging dynamic environments. The proposed method builds upon the concept of Velocity Obstacles (VO) that was later extended to Nonlinear Velocity Obstacles (NLVO) to account for obstacles moving along nonlinear trajectories. The NLVO is extended in this paper to Acceleration Obstacles (AO) and Nonlinear Acceleration Obstacles (NAO) that account for velocity and acceleration constraints. Multi-robot navigation is achieved by using the same avoidance algorithm by all robots. At each time step, the trajectories of all robots are predicted based on their current velocity and acceleration to allow the computation of their respective NLVO, AO and NAO. The introduction of AO and NAO allows the generation of safe avoidance maneuvers that account for the robot dynamic constraints better than could be done with the NLVO alone. This paper demonstrates the use of AO and NAO for robot navigation in challenging environments. It is shown that using AO and NAO enables simultaneous real-time collision avoidance while accounting for robot kinematics and a direct consideration of its dynamic constraints. The presented approach enables reactive and efficient navigation, with potential application for autonomous vehicles operating in complex dynamic environments.

Problem

• Enhances robot navigation in dynamic environments using Acceleration Obstacles
• Extends Nonlinear Velocity Obstacles to account for acceleration constraints
• Enables real-time collision avoidance considering robot dynamics

Innovation

• Extends NLVO to Acceleration Obstacles (AO)
• Introduces Nonlinear Acceleration Obstacles (NAO)
• Enables real-time multi-robot collision avoidance