This work presents a novel multi-robot coverage scheme for an unknown number of robots; it focuses on optimizing the number of robots and each path cost. Coverage problems traditionally deal with how a given number of robots covers the entire environment. This work, however, presents solutions of not only (i) how to cover the area (locations of interest) within the minimum time, but simultaneously (ii) how to find the optimal number of robots for a given time. Also, we consider the worst but realistic case of all robots starting at the same location instead of assuming randomly initialized positions. The minimum coverage time depends upon the number of robots used. Our research specifies (iii) how to find the minimum coverage time without knowing the number of robots. Finally, we present a deterministic coverage algorithm based on finding the shortest paths in order to optimize the number of robots and corresponding paths.

This paper presents a new vision-based leader-follower formation algorithm where the leader's trajectory is unknown to the robots which are following. Formation schemes in straight lines and diagonal formations are introduced which are both stable and observable in the presence of limited views. The algorithms are novel since they only use local image measurements through a pinhole camera to estimate the leader's position. This approach does not require specialized markings nor extensive robot communications. The algorithms are also decentralized. We apply an input-output feedback linearization for system stability and utilize an Extended Kalman Filter (EKF) for estimation. Simulations illustrate how the proposed formation controls work. Real experiments utilizing multiple miniature robots are also presented and illustrate the challenges associated with noisy images in real-world applications.

The forward moving target segmentation from a moving platform with a pinhole camera is an important and relatively unexplored problem in visual tracking. This paper proposes a novel segmentation algorithm for extracting a moving target when using a moving platform (that follows a similar trajectory and has a camera that is not calibrated for the particular scene). When the target has unpredictable motions, we are unable to model it and the pertinent backgrounds are very different. We introduce a new entropy-based clustering algorithm in order to find a bounding box representing the target. A target model based on graph representation is used for matching the moving target. To demonstrate the robust target segmentation scheme, we apply the method to a team of miniature robots (the Explorers developed at the University of Minnesota) in real tracking missions.