IVO: Inverse Velocity Obstacles for Real Time Navigation
This addresses real-time navigation challenges for robots and multi-agent systems by reducing dependency on state estimation, though it appears incremental as it builds on velocity obstacle concepts.
The paper tackles real-time collision avoidance in dynamic environments by introducing Inverse Velocity Obstacles (IVO), an ego-centric framework that computes collision-free maneuvers without needing robot pose or velocity information, showing efficacy and scalability in evaluations.
In this paper, we present "IVO: Inverse Velocity Obstacles" an ego-centric framework that improves the real time implementation. The proposed method stems from the concept of velocity obstacle and can be applied for both single agent and multi-agent system. It focuses on computing collision free maneuvers without any knowledge or assumption on the pose and the velocity of the robot. This is primarily achieved by reformulating the velocity obstacle to adapt to an ego-centric framework. This is a significant step towards improving real time implementations of collision avoidance in dynamic environments as there is no dependency on state estimation techniques to infer the robot pose and velocity. We evaluate IVO for both single agent and multi-agent in different scenarios and show it's efficacy over the existing formulations. We also show the real time scalability of the proposed methodology.