Fast Obstacle Avoidance Motion in SmallQuadcopter operation in a Cluttered Environment
This addresses the Sense-And-Avoid problem for autonomous drones, offering a low-latency solution that could enhance safety and efficiency in applications like delivery or inspection, though it appears incremental as it builds on existing SAA frameworks.
The paper tackles the challenge of enabling small quadcopters to autonomously avoid obstacles at high speeds in cluttered environments by proposing the FOAM algorithm, which uses multi-sensor fusion and a probabilistic occupancy map, achieving operation at 4.5 m/s in real-world tests.
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.