The autonomous operation of small quadcopters moving at high speed in an unknown cluttered environment is a challenging task. Current works in the literature formulate it as a Sense-And-Avoid (SAA) problem and address it by either developing new sensing capabilities or small form-factor processors. However, the SAA, with the high-speed operation, remains an open problem. The significant complexity arises due to the computational latency, which is critical for fast-moving quadcopters. In this paper, a novel Fast Obstacle Avoidance Motion (FOAM) algorithm is proposed to perform SAA operations. FOAM is a low-latency perception-based algorithm that uses multi-sensor fusion of a monocular camera and a 2-D LIDAR. A 2-D probabilistic occupancy map of the sensing region is generated to estimate a free space for avoiding obstacles. Also, a local planner is used to navigate the high-speed quadcopter towards a given target location while avoiding obstacles. The performance evaluation of FOAM is evaluated in simulated environments in Gazebo and AIRSIM. Real-time implementation of the same has been presented in outdoor environments using a custom-designed quadcopter operating at a speed of $4.5$ m/s. The FOAM algorithm is implemented on a low-cost computing device to demonstrate its efficacy. The results indicate that FOAM enables a small quadcopter to operate at high speed in a cluttered environment efficiently.
翻译:在一个未知的杂乱环境中高速移动的小象棋的自主操作是一项具有挑战性的任务。文献中的当前作品将它发展成一个“感知和避免”问题,并通过开发新的感知能力或小型形式因素处理器来解决它。然而,具有高速操作的SAA仍然是一个尚未解决的问题。由于对快速移动的象棋机来说至关重要的计算时空关系,产生了相当复杂的问题。在本文中,提议采用新的快速快快快快加速移动(FOAM)算法来进行SAA操作。FOAM是一种基于低延迟感知的算法,使用多感测或多感应器结合一台单色照相机和2DLIDAR。生成了遥感区域2D概率占用图,以估计一个自由的空间来避免障碍。此外,一个当地规划员用来将高速象棋机拖向一个特定的目标地点,同时避免障碍。在Gazebo的模拟环境中对FOAM进行绩效评估。在GOSAMA公司和AIRS快速操作的一种实时操作环境中,在OSAMA机运行一个实时操作的实时环境上显示一个实时应用了AMAMA标准。