Autonomous Vision-Based Navigation of a Quadrotor in Corridor-Like Environments

Abstract

Vision-based bio-inspired control strategies offer great promise in demonstrating safe autonomous navigation of aerial microsystems in completely unstructured environments. This paper presents an innovative navigation technique that embeds bio-inspired widefield processing of instantaneous optic flow patterns within the H ∞ loop shaping synthesis framework, resulting in a dynamic controller that enables robust stabilization and command tracking behavior in obstacle-laden environments. The local environment is parameterized as a series of simpler corridor-like environments in the optic flow model, and the loop shaping controller is synthesized to provide robust stability across the range of modeled environments. Experimental validation is provided using a quadrotor aerial vehicle across environments with large variation in local structure, with the loop shaping controller demonstrating better tracking performance than other comparable controllers in straight-line corridors of different widths. The current approach is computationally efficient, as it does not involve explicit extraction of an environment depth map, and makes for an attractive paradigm for aerial microsystem navigation in urban environments. Corridor half-width, m Nomenclature INTRODUCTIONThe ability to sense and react to clutter within the payload and bandwidth constraints imposed by aerial microsystems is the primary requirement in demonstrating safe autonomous navigation in unstructured environments. This rules out the use of laser rangefinders and GPS-IMU based estimates of velocity and proximity to obstacles in the surrounding environment [1,2], as well as machine-vision based approaches that extract vehicle pose from camera imagery [3,4]. A safe control strategy usually relies on the synthesis of a flight controller that tracks the instantaneous obstacle-symmetric reference trajectory as the vehicle traverses the local environment. In unknown and unstructured environments, implementation becomes difficult requiring the resolution of the twin issues of local depth map extraction and accurate motion-state estimation at the bandwidths suitable for aerial navigation. Thus, most prior efforts have typically considered the simpler problem of navigation in known, well-structured environments [3][4][5][6][7][8][9][10][11][12], although onboard implementations of the coupled structure-motion state estimation problem in cluttered, urban environments exist [13,2].Bio-inspired vision-based control strategies offer great promise in demonstrating autonomous navigation in completely unstructured environments. Insects leverage information contained in optic flow, the patterns of visual motion that form on the retina as they move, by employing wide-field interneurons that transform large numbers of distributed, local estimates of optic flow into a reduced number of motor commands that stabilize and regulate flight behavior without explicit extraction of the local depth map in cluttered environments [14][15][16][17][18]. The resulting closed loop has b...

Enhancing LGMD’s Looming Selectivity for UAV With Spatial–Temporal Distributed Presynaptic Connections

“…The first approach requires real-time knowledge about the 3D environment. This can be obtained with the use of a stereocamera [16], LIDAR [17], [18], optic-flow based distance maintenance [19], [20] or SLAM [3]. Such methods are commonly used in UAVs that have sufficient power.…”

Taking Inspiration from Flying Insects to Navigate inside Buildings

“…To compare the bioinspired visuomotor convergence theory to the "optic flow balance strategy" that frequently fails in one-sided corridors or those with openings in a wall (see review [99]) or the switching mode strategy employed in such environments [87,88], the bioinspired visuomotor convergence in [109,110] retains the strategy of balancing lateral optic flows and leverages the stability and performance guarantees of the closed loop to achieve stable quadrotor flight in corridor-like environments which include large openings in a wall or additional structures such as small poles.…”

Optic Flow‐Based Robotics

“…Recently, a theoretical proof for stability of the bio-inspired visuomotor convergence theory was demonstrated ( [78]; [79]), but also an analysis of the robustness and quantification of the level of uncertainty in the environment (corridor-like environments with additional structure such as small poles, cylinders, or gaps and holes in the corridor) that the closed loop system can tolerate was provided ( [78]; [79]). This is a major point because [78] & [79] are the only works that have provided robustness guarantees; none of the other work listed in this review provides robustness or performance guarantees, which is a fundamental requirement for analysis of closed loop feedback systems. Actually, the only other references that provide a basic stability analysis (without robustness) of the closed loop system are references [69], [70],& [80].…”

“…In contrast the switching mode strategy employed in such environments ( Fig. 5; [41]; [39]), the bio-inspired visuomotor convergence in [78] & [79] retains the strategy of balancing lateral optic flows and leverages the stability and performance guarantees of the closed loop to achieve stable quadrotor flight in environments that include a corridor with a large opening in a wall.…”