HEPP: Hyper-efficient Perception and Planning for High-speed Obstacle Avoidance of UAVs

High-speed obstacle avoidance of uncrewed aerial vehicles (UAVs) in cluttered environments is a significant challenge. Existing UAV planning and obstacle avoidance systems can only fly at moderate speeds or at high speeds over empty or sparse fields. In this article, we propose a hyper-efficient perception and planning system for the high-speed obstacle avoidance of UAVs. The system mainly consists of three modules: 1) A novel incremental robocentric mapping method with distance and gradient information, which takes 89.5% less time compared to existing methods. 2) A novel obstacle-aware topological path search method that generates multiple distinct paths. 3) An adaptive gradient-based high-speed trajectory generation method with a novel time pre-allocation algorithm. With these innovations, the system has an excellent real-time performance with only milliseconds latency in each iteration, taking 79.24% less time than existing methods at high speeds (15 m/s in cluttered environments), allowing UAVs to fly swiftly and avoid obstacles in cluttered environments. The planned trajectory of the UAV is close to the global optimum in both temporal and spatial domains. Finally, extensive validations in both simulation and real-world experiments demonstrate the effectiveness of our proposed system for high-speed navigation in cluttered environments.

Flying in Highly Dynamic Environments with End-to-end Learning Approach

Obstacle avoidance for unmanned aerial vehicles like quadrotors is a popular research topic. Most existing research focuses only on static environments, and obstacle avoidance in environments with multiple dynamic obstacles remains challenging. This paper proposes a novel deep-reinforcement learning-based approach for the quadrotors to navigate through highly dynamic environments. We propose a lidar data encoder to extract obstacle information from the massive point cloud data from the lidar. Multi frames of historical scans will be compressed into a 2-dimension obstacle map while maintaining the obstacle features required. An end-to-end deep neural network is trained to extract the kinematics of dynamic and static obstacles from the obstacle map, and it will generate acceleration commands to the quadrotor to control it to avoid these obstacles. Our approach contains perception and navigating functions in a single neural network, which can change from a navigating state into a hovering state without mode switching. We also present simulations and real-world experiments to show the effectiveness of our approach while navigating in highly dynamic cluttered environments.

FAPP: Fast and Adaptive Perception and Planning for UAVs in Dynamic Cluttered Environments

Obstacle avoidance for Unmanned Aerial Vehicles (UAVs) in cluttered environments is significantly challenging. Existing obstacle avoidance for UAVs either focuses on fully static environments or static environments with only a few dynamic objects. In this paper, we take the initiative to consider the obstacle avoidance of UAVs in dynamic cluttered environments in which dynamic objects are the dominant objects. This type of environment poses significant challenges to both perception and planning. Multiple dynamic objects possess various motions, making it extremely difficult to estimate and predict their motions using one motion model. The planning must be highly efficient to avoid cluttered dynamic objects. This paper proposes Fast and Adaptive Perception and Planning (FAPP) for UAVs flying in complex dynamic cluttered environments. A novel and efficient point cloud segmentation strategy is proposed to distinguish static and dynamic objects. To address multiple dynamic objects with different motions, an adaptive estimation method with covariance adaptation is proposed to quickly and accurately predict their motions. Our proposed trajectory optimization algorithm is highly efficient, enabling it to avoid fast objects. Furthermore, an adaptive re-planning method is proposed to address the case when the trajectory optimization cannot find a feasible solution, which is common for dynamic cluttered environments. Extensive validations in both simulation and real-world experiments demonstrate the effectiveness of our proposed system for highly dynamic and cluttered environments.