From VO to NAO: Reactive Robot Navigation Using Velocity and Acceleration Obstacles
Asher Stern, Oz Ben-Yosef, Zvi Shiller
AI summary
Problem
Reactive robot navigation in dynamic environments requires collision avoidance that respects both kinematic and dynamic constraints, yet existing methods often demand frequent control updates or fail to efficiently handle arbitrary obstacle trajectories.
Approach
The authors derive the Acceleration Obstacle (AO) and Nonlinear Acceleration Obstacle (NAO) to map moving obstacles directly to forbidden robot accelerations, enabling direct selection of safe control inputs for second-order systems.
Key results
- Formal derivation of the Acceleration Obstacle (AO) and Nonlinear Acceleration Obstacle (NAO) frameworks
- Demonstration that NAO reduces unnecessary acceleration adjustments compared to constant-acceleration models
- Geometric analysis of how initial velocities and obstacle trajectories shape the obstacle regions
- Validation of real-time collision avoidance in complex dynamic environments via simulation
Why it matters
Provides a computationally efficient, reactive navigation foundation for autonomous vehicles, drones, and service robots operating in crowded, dynamic settings.
Abstract
This paper addresses the problem of robot navigation in challenging dynamic environ- ments by extending the Velocity Obstacle (VO) framework to the Nonlinear Acceleration Obstacle (NAO). The NAO represents the set of robot acceler- ations that would lead to collisions with an obstacle moving along an arbitrary trajectory. By formulating the problem in the acceleration domain, the method allows direct selection of accelerations, the natural control input of second-order systems, to generate safe avoidance maneuvers in complex dynamic envi- ronments. Simulation results show that NAO enables real-time collision avoidance while explicitly account- ing for both robot kinematics (velocity) and dynamics (acceleration). The proposed framework thus provides a reactive and efficient basis for autonomous naviga- tion in complex dynamic environments.