Risk Assessment and Motion Planning for MAVs in Dynamic Uncertain Environments

Abstract

Risk assessment to quantify the danger associated with a planned trajectory is critical for micro aerial vehicles (MAVs) navigating in dynamic uncertain environments. Existing works usually perform risk assessment by reasoning the occupancy status of the MAV’s surrounding space which only incorporates the position information of the MAV and the obstacles in the environment. In this paper, we further consider the MAV’s motion direction in risk assessment to reflect the fact that the obstacles in front of the MAV pose a higher risk while those behind pose a lower risk. In particular, we rely on a particle-based dynamic map which consists of a large number of particles to represent the local environment. The risk is defined to evaluate the safety level of a subspace in the map during some time interval and assessed by reasoning the occurrence of particles in the subspace. Those particles around the MAV are assigned different weights taking into account their relative positions to the MAV and its motion direction. We then incorporate the proposed risk assessment method into MAV motion planning by minimizing both the path length and the associated risk to achieve safer navigation. We compared our method with several state-of-the-art approaches in PX4+Gazebo simulations and real-world experiments. The results show that our method can achieve a 15% higher collision avoidance rate and a 20% lower flight risk in various environments with static and dynamic obstacles.