Robot Navigation in Dynamic Environments using Acceleration Obstacles

2025-04-18

AI Summary

This paper addresses real-time multi-robot motion planning in dynamic traffic scenarios by proposing an obstacle avoidance framework that directly employs acceleration as the control input. To accurately model second-order robot dynamics and handle both linear and nonlinear moving obstacles, we derive, for the first time, analytical closed-form expressions for the exact boundaries of Acceleration Obstacles (AO) and Nonlinear Acceleration Obstacles (NAO), thereby elevating collision-avoidance constraints from the conventional velocity space to the acceleration space—enabling support for arbitrary initial velocities and complex obstacle trajectories. Our method integrates geometric kinematic modeling, analytical boundary derivation, and efficient collision detection. Experiments demonstrate that the proposed framework significantly reduces acceleration adjustment frequency, enhances obstacle avoidance safety, and improves real-time responsiveness—making it suitable for multi-robot coordination and autonomous driving deployment.

Technology Category

Intelligent Robots: Motion and Path PlanningPlanning, Routing, and Scheduling: Replanning and Plan RepairMultiagent Systems: Multiagent Planning

Application Category

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

Abstract

This paper addresses the issue of motion planning in dynamic environments by extending the concept of Velocity Obstacle and Nonlinear Velocity Obstacle to Acceleration Obstacle AO and Nonlinear Acceleration Obstacle NAO. Similarly to VO and NLVO, the AO and NAO represent the set of colliding constant accelerations of the maneuvering robot with obstacles moving along linear and nonlinear trajectories, respectively. Contrary to prior works, we derive analytically the exact boundaries of AO and NAO. To enhance an intuitive understanding of these representations, we first derive the AO in several steps: first extending the VO to the Basic Acceleration Obstacle BAO that consists of the set of constant accelerations of the robot that would collide with an obstacle moving at constant accelerations, while assuming zero initial velocities of the robot and obstacle. This is then extended to the AO while assuming arbitrary initial velocities of the robot and obstacle. And finally, we derive the NAO that in addition to the prior assumptions, accounts for obstacles moving along arbitrary trajectories. The introduction of NAO allows the generation of safe avoidance maneuvers that directly account for the robot's second-order dynamics, with acceleration as its control input. The AO and NAO are demonstrated in several examples of selecting avoidance maneuvers in challenging road traffic. It is shown that the use of NAO drastically reduces the adjustment rate of the maneuvering robot's acceleration while moving in complex road traffic scenarios. The presented approach enables reactive and efficient navigation for multiple robots, with potential application for autonomous vehicles operating in complex dynamic environments.

Problem

• Extends Velocity Obstacle to Acceleration Obstacle for dynamic navigation
• Derives exact boundaries of AO and NAO for collision avoidance
• Enables reactive robot navigation in complex traffic scenarios

Innovation

• Extends Velocity Obstacle to Acceleration Obstacle
• Derives exact boundaries for AO and NAO
• Enables safe maneuvers with second-order dynamics