  Collision avoidance is one of the most important topics in the robotics
field. The goal is to move the robots from initial locations to target
locations such that they follow shortest non-colliding paths in the shortest
time and with the least amount of energy. In this paper, a distributed and
real-time algorithm for dense and complex 2D and 3D environments is proposed.
This algorithm uses angular calculations to select the optimal direction for
the movement of each robot and it has been shown that these separate
calculations lead to a form of cooperative behavior among agents. We evaluated
the proposed approach on various simulation and experimental scenarios and
compared the results with FMP and ORCA, two important algorithms in this field.
The results show that the proposed approach is at least 25% faster than ORCA
and at least 7% faster than FMP and also more reliable than both methods. The
proposed method is shown to enable fully autonomous navigation of a swarm of
crazyflies.
