  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.
