No abstract

In this article, we present a monocular visual reactive navigation system capable of navigating at high speeds, without GPS, in unknown complex cluttered environments. The system, called R-ADVANCE (Rapid Adaptive Prediction for Vision-based Autonomous Navigation, Control, and Evasion), consists of a set of biologically inspired visual perception and reactive control algorithms that provide low-computation reactive obstacle avoidance while navigating at high speeds in search of a goal object. These algorithms, along with basic planning, and augmented with lowprecision visual odometry, were implemented on a micro unmanned aerial vehicle and tested in a number of challenging environments. While each of the individual algorithmic and hardware elements has been previously studied in limited environments, this work is the first time that these novel components have been integrated and flight-tested. To achieve fast flight, an NVIDIA Tegra TK1 was used as the main processor, allowing us to parallelize the system to process 1280 × 720 video streams at 40 fps, reaching flight speeds up to 19 m/s (≈68 km/h) or 42 mph.

Safe, autonomous navigation by aerial microsystems in less-structured environments is a difficult challenge to overcome with current technology. This paper presents a novel visual-navigation approach that combines bioinspired wide-field processing of optic flow information with control-theoretic tools for synthesis of closed loop systems, resulting in robustness and performance guarantees. Structured singular value analysis is used to synthesize a dynamic controller that provides good tracking performance in uncertain environments without resorting to explicit pose estimation or extraction of a detailed environmental depth map. Experimental results with a quadrotor demonstrate the vehicle's robust obstacle-avoidance behaviour in a straight line corridor, an S-shaped corridor and a corridor with obstacles distributed in the vehicle's path. The computational efficiency and simplicity of the current approach offers a promising alternative to satisfying the payload, power and bandwidth constraints imposed by aerial microsystems.

No abstract

No abstract