This paper presents a metric for finding optimal sensor and target geometries that provide accurate estimates of bias during target tracking with a single sensor taking measurements of bearing. Since the bias cannot be measured directly, it is shown how to manipulate the equations of a Kalman filter to produce a pseudo measurement of bias and its associated measurement error covariance. These measurement error covariances are used to form a Cramer-Rao lower bound (CRLB) on the bias estimation variance as a function of sensor and target geometries. It is shown that highly accurate estimates of bias can be produced using a single sensor, even if the kinematic state estimate of the target is poor.

Formation flying (FF) is a critical element in NASA's future deep-space missions. Terrestrial Planet Finder (TPF), NASA's first space-based mission to directly observe planets outside our own solar system, will rely on FF to achieve the functionality and benefits of a large instrument using multiple lower cost smaller spacecraft. Many key network design problems for such FF missions can be formulated as optimization problems with local and global constraints. We develop a topology control algorithm that can be used for many network problems in the presence of local constraints, such as collision avoidance, and global constraints, such as network connectivity. The presence of contradictory objectives in topology control problems motivated a game-theoretic approach. We demonstrated that a game-theoretic technique could provide a framework for design and analysis of many topology control problems in dynamic networks. In particular, the problem of motion planning for formation reconfiguration in the presence of constraints on network connectivity and inter-spacecraft collisions is studied.

In this paper, we study the problem of distributed motion coordination among a group of nonholonomic ground robots. We develop vision-based control laws for parallel and balanced circular formations using a consensus approach. The proposed control laws are distributed in the sense that they require information only from neighboring robots. Furthermore, the control laws are coordinate-free and do not rely on measurement or communication of heading information among neighbors but instead require measurements of bearing, optical flow, and time to collision, all of which can be measured using visual sensors. Collision-avoidance capabilities are added to the team members, and the effectiveness of the control laws are demonstrated on a group of mobile robots.

We study the problem of vision-based flocking and coordination of a group of kinematic agents in 2 and 3 dimensions. It is shown that in the absence of communication among agents, and by using only visual information, a group of mobile agents can align their velocity vectors and move in a formation. A coordinate-free control law is used to develop a vision-based input for each nonholonomic agent. The vision-based input does not rely on heading measurements, but only requires measurements of bearing, optical flow and time-to-collision, all of which can be efficiently measured