An Adaptive Approach to Vision-based Formation Control

Abstract

In considering the problem of formation control in the deployment of intelligent munitions, it would be highly desirable, both from a mission and a cost perspective, to limit the information that is transmitted between vehicles in formation. However, the lack of information regarding the state of motion of neighboring vehicles can lead to degraded performance and even instability. This paper presents an adaptive output feedback approach for addressing this problem. We design adaptive formation controllers that allow each vehicle in formation to maintain separation and relative orientation with respect to neighboring vehicles, while avoiding obstacles. The method works by enabling each vehicle in the formation to adaptively correct for the effect that the motions of neighboring vehicles have when regulating relative variables like range and line of sight. It is assumed that estimates of these variables can be derived using passive, vision-based sensors. The need for explicit communication to maintain formation is minimized and the resulting controller solution is decentralized. We implement a reactive obstacle avoidance controller to navigate in an environment with obstacles. The formation controller and obstacle avoidance controller are outer-loop controllers whose outputs are speed and heading commands. These commands are blended together to generate composite speed and heading commands that are inputs to the inner-loop controller. The weights used for blending the commands depend upon the priority of the task at hand. We illustrate the method with an example involving a team of three aircraft keeping formation in the presence of obstacles.

Nonlinear Formation Guidance Law with Robust Disturbance Observer

“…In Ref. [3], an adaptive approach for vision-based UAV formation control assumes that LOS range can be estimated by the visual sensors. Another possible method for sensing unnecessary data communication is to use the wake produced by the leading aircraft [4].…”

Active control of visual sensor for navigation and guidance

“…The special maneuvers in Betser et al (2004), Johnson et al (2004), Sattigeri et al (2003), Watanabe et al (2004), Wang et al (2005), Stepanyan and Hovakimyan (2005) and Cao and Hovakimyan (2005) required for estimation and range recovery can disturb the normal operation of the aircraft, especially in the aerial-tracking operation mode. Unlike the above approaches which couple estimation and control, we propose a new approach in this paper that does not require special maneuvers for the aircraft to achieve parameter convergence or range recovery.…”

“…However, convergence properties for EKF have been proven only for limited classes of deterministic systems (Ljung 1979). Betser et al (2004), Johnson et al (2004), Sattigeri et al (2003), Watanabe et al (2004), Wang et al (2005) and Stepanyan and Hovakimyan (2005) have explored a variety of methods for visual tracking of moving objects. The main difference in these various approaches is in the class of maneuvers required from the follower to recover the observability.…”

Active Control of Visual Sensor for Aerial Tracking

“…7,8,9,10,11,12,14,13 required for estimation and range recovery can disturb the normal operation of the aircraft, especially in the aerial-tracking operation mode. Unlike the above approaches which couple estimation and control, we propose a new approach in this paper that does not require special maneuvers for the aircraft to achieve parameter convergence or range recovery.…”

“…6 Refs. 7,8,9,10,11,12,13 have explored a variety of methods for visual tracking of moving objects. The main difference in these various approaches is in the class of maneuvers required from the follower to recover the observability.…”