Obstacle Avoidance Strategy using Onboard Stereo Vision on a Flapping Wing MAV
This addresses the challenge of enabling obstacle avoidance for lightweight MAVs under strict weight and power constraints, though it is incremental as it builds on existing avoidance strategies with specific improvements.
The paper tackles autonomous navigation for lightweight flapping-wing micro air vehicles (MAVs) in unknown indoor environments by proposing the 'Droplet' strategy, which uses stereo vision to enable constant-speed, computationally efficient obstacle avoidance without storing images or maps, and demonstrates robustness in real-world tests on a 20-gram platform.
The development of autonomous lightweight MAVs, capable of navigating in unknown indoor environments, is one of the major challenges in robotics. The complexity of this challenge comes from constraints on weight and power consumption of onboard sensing and processing devices. In this paper we propose the "Droplet" strategy, an avoidance strategy based on stereo vision inputs that outperforms reactive avoidance strategies by allowing constant speed maneuvers while being computationally extremely efficient, and which does not need to store previous images or maps. The strategy deals with nonholonomic motion constraints of most fixed and flapping wing platforms, and with the limited field-of-view of stereo camera systems. It guarantees obstacle-free flight in the absence of sensor and motor noise. We first analyze the strategy in simulation, and then show its robustness in real-world conditions by implementing it on a 20-gram flapping wing MAV.