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Obstacle avoidance of quadrotors in dynamic environments is still a very openproblem. Current works commonly leverage traditional static maps to representstatic obstacles and the detection and tracking of moving objects (DATMO)method to model dynamic obstacles separately. The detection module requirespre-training, and the dynamic obstacles can only be modeled with certainshapes, such as cylinders or ellipsoids. This work utilizes the dual-structureparticle-based (DSP) dynamic occupancy map to represent the arbitrary-shapedstatic obstacles and dynamic obstacles simultaneously, and proposes anefficient risk-aware sampling-based local trajectory planner to realize safeflights in this map. The trajectory is planned by sampling motion primitivesgenerated 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-termphase designed to avoid high-risk regions. The risk is evaluated with thepredicted particle-form future occupancy status, considering the timedimension. With an approach to split from and merge to an arbitrary globaltrajectory, the planner can also be used in the tasks with preplanned globaltrajectories. Comparison experiments show that the obstacle avoidance systemcomposed of the DSP map and our planner performs the best in dynamicenvironments. In real-world tests, our quadrotor reaches a speed of 6 m/s withthe motion capture system and 2.5 m/s with everything running on a low-pricesingle-board computer.

Risk-aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments