  Obstacle avoidance of quadrotors in dynamic environments is still a very open
problem. Current works commonly leverage traditional static maps to represent
static obstacles and the detection and tracking of moving objects (DATMO)
method to model dynamic obstacles separately. The detection module requires
pre-training, and the dynamic obstacles can only be modeled with certain
shapes, such as cylinders or ellipsoids. This work utilizes the dual-structure
particle-based (DSP) dynamic occupancy map to represent the arbitrary-shaped
static obstacles and dynamic obstacles simultaneously, and proposes an
efficient risk-aware sampling-based local trajectory planner to realize safe
flights in this map. The trajectory is planned by sampling motion primitives
generated in the state space. Each motion primitive is divided into two phases:
a short-term phase with a strict risk limitation and a relatively long-term
phase designed to avoid high-risk regions. The risk is evaluated with the
predicted particle-form future occupancy status, considering the time
dimension. With an approach to split from and merge to an arbitrary global
trajectory, the planner can also be used in the tasks with preplanned global
trajectories. Comparison experiments show that the obstacle avoidance system
composed of the DSP map and our planner performs the best in dynamic
environments. In real-world tests, our quadrotor reaches a speed of 6 m/s with
the motion capture system and 2.5 m/s with everything running on a low-price
single-board computer.
