Adaptive Speed Collision Avoidance For Dynamic Environments

This paper presents a dynamic collision avoidance framework using predictive approaches. This predictive model exploits temporal and spatial characteristics of dynamic obstacles to predict future events thus avoiding collisions. To achieve this we implement a novel combination of two Poisson processes with independent outcomes and test this on a series of dynamic events with a crazyflie drone. Our approach initiates a speed adjustment when needed, which is a natural human response to avoid collisions with obstacles in crowded and congested environments. Our results in simulation and realworld tests indicate that our method removes the need for replanning and reduces the cost of time which is a huge bottleneck in trajectory planning. Our method outperforms baseline algorithms in terms of well-accepted metrics like the number of collisions, percentage accuracy, and execution time.