Active visual tracking in multi-agent scenarios
We propose an active visual tracker with collision avoidance for camera-equipped robots in dense multi-agent scenarios. The objective of each tracking agent (robot) is to maintain visual fixation on its moving target while updating its velocity to avoid other agents. However, when multiple robots are present or targets intensively intersect each other, robots may have no accessible collision-avoiding paths. We address this problem with an adaptive mechanism that sets the pair-wise responsibilities to increase the total accessible collision-avoiding controls. The final collision-avoiding control accounts for motion smoothness and view performance, i.e. maintaining the target centered in the field of view and at a certain size. We validate the proposed approach under different target-intersecting scenarios and compare it with the Optimal Reciprocal Collision Avoidance and the Reciprocal Velocity Obstacle methods.