To improve the accuracy in capturing the motion of deformable objects, a reconfigurable multi-camera active-vision system which can dynamically reposition its cameras online is proposed, and a design for such a system, along with a methodology to select the near-optimal positions and orientations of the set of cameras, is presented. The active-vision system accounts for the deformation of the object-of-interest by tracking triangulated vertices in order to predict the shape of the object at subsequent demand instants. It then selects a system configuration that minimizes the expected error in the recovered position of each of these vertices. Extensive simulations and experiments have verified that using the proposed reconfigurable system to both translate and rotate cameras to near-optimal poses is tangibly superior to using cameras which are either static, or can only rotate, in minimizing the error in recovered vertex positions.

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This thesis investigates cooperative and intelligent control of autonomous multi-robot systems in a dynamic, unstructured and unknown environment and makes significant original contributions with regard to self-deterministic learning for robot cooperation, evolutionary optimization of robotic actions, improvement of system robustness, vision-based object tracking, and real-time performance. A distributed multi-robot architecture is developed which will facilitate operation of a cooperative multi-robot system in a dynamic and unknown environment in a self-improving, robust, and real-time manner. It is a fully distributed and hierarchical architecture with three levels. By combining several popular AI, soft computing, and control techniques such as learning, planning, reactive paradigm, optimization, and hybrid control, the developed architecture is expected to facilitate effective autonomous operation of cooperative multi-robot systems in a dynamically changing, unknown, and unstructured environment. A machine learning technique is incorporated into the developed multi-robot system for self-deterministic and self-improving cooperation and coping with uncertainties in the environment. A modified Q-learning algorithm termed Sequential Q-learning with Kalman Filtering (SQKF) is developed in the thesis, which can provide fast multi-robot learning. By arranging the robots to learn according to a predefined sequence, modeling the effect of the actions of other robots in the work environment as Gaussian white noise and estimating this noise online with a Kalman filter, the SQKF algorithm seeks to solve several key problems in multi-robot learning. As a part of low-level sensing and control in the proposed multi-robot architecture, a fast computer vision algorithm for color-blob tracking is developed to track multiple moving objects in the environment. By removing the brightness and saturation information in an image and filtering unrelated information based on statistical features and domain knowledge, the algorithm solves the problems of uneven illumination in the environment and improves real-time performance.

Online computation is a model for formulating decision making under uncertainty. In an online problem, the algorithm does not know the entire input from the beginning; the input is revealed in a sequence of steps. At each step, the algorithm should make its decisions based on the past and without any knowledge about the future. Many important real-life problems such as robot navigation are intrinsically online and thus the design and analysis of online algorithms is one of the main research areas in theoretical computer science. Competitive analysis is the standard measure for analysis of online algorithms. It has been applied to many online problems in diverse areas ranging from robot navigation, to network routing, to scheduling, to online graph coloring. In this thesis, we first survey three classic online problems, namely the cow-path problem, the Processor-Allocation problem and the Robots-Search-Rays problem and highlight connections between them. Second, the main result is for the One-Robot-Searches-Two-Rays problem for which we consider the weighted scenario, in which the robot is located on a ray with a preferential probability p. We term the One-Robot-Searches-Two-Rays-And-Weighted problem as 1-STRAW (and in general k-STRAW for k searchers). In the 1-STRAW problem, we propose a search strategy which is optimal among weighted geometric states. In addition, we prove a tight lower bound of the worst case competitive ratio and conjecture a lower bound of the average case competitive ratio for the 1-STRAW problem. Additionally, we compare our search strategy and its performance with the doubling strategy and the SmartCow algorithm.

One of the applications where the use of robots can be beneficial is Wilderness Search and Rescue (WiSAR), which involves the search for a possibly mobile but non-trackable lost person (i.e., the target) in wilderness environments. A mobile target implies that the search area grows continuously and potentially without bound. This fact, combined with the presence of typically rugged, varying terrain and the possibility of inclement weather, poses a considerable challenge to human Search and Rescue (SAR) personnel with respect to the time and effort required to perform the search and the danger entailed to the searchers. Mobile robots can be advantageous in WiSAR due to their ability to provide consistent performance without getting tired and their lower susceptibility to harsh weather conditions compared to humans. Thus, a coordinated team of robots that can assist human SAR personnel by autonomously performing searches in WiSAR scenarios would be of great value. However, to date, a suitable multi-robot coordination methodology for autonomous search that can satisfactorily address the issues relevant to WiSAR is lacking. The objective of this Dissertation is, thus, to develop a methodology that can autonomously coordinate the search strategy of a multi-robot team in wilderness environments to locate a moving target that is neither continuously nor intermittently observed during the search process. Three issues in particular are addressed: (i) target-location prediction, (ii) robot deployment, and (iii) robot-path planning. The corresponding solution approaches devised to address these issues incorporate the influence of varying terrain that may contain a priori known and unknown obstacles, and deal with unique target physiology and psychology as well as found clues left behind by the target. The solution methods for these three tasks work seamlessly together resulting in a tractable MRC methodology for autonomous robotic WiSAR. Comprehensive simulations have been performed that validate the overall proposed methodology. Moreover, the tangible benefits provided by this methodology were further revealed through its comparison with an alternative search method.

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People and animals use various kinds of motion in a multitude of ways to communicate their ideas and affective states, such as their moods or emotions. Further, people attribute affect and personalities to movements of even abstract entities based solely on the style of their motions, e.g., movement of a geometric shape (how it moves about) can be interpreted as being shy, aggressive, etc. In this thesis, we investigated how flying robots can leverage this locomotion-style communication channel for communicating their states to people. One problem in leveraging this style of communication in robot design is that there are no guidelines, or tools that Human-Robot Interaction (HRI) designers can leverage to author affect communicating locomotion paths for flying robots. Therefore, we propose to adapt the Laban Effort System (LES), a standard method for interpreting human motion commonly used in the performing arts, to develop a set of guidelines that can be leveraged by HRI designers to author affective locomotion paths for flying robots. We further validate our proposed approach by conducting a small design workshop with a group of interaction designers, where they were asked to design robotic behaviors using our design method. We conclude this thesis with an original adaption of LES to the locomotion path of a flying robot, and a set of design guidelines that can be leveraged by interaction designers for building affective locomotion path for a flying robot.

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A map is important for autonomous mobile robots to traverse an environment safely and efficiently through highly competent abilities in path planning, navigation and localization. Maps are generated from sensors data. However, sensor uncertainties affect the mapping process and thus influence the performance of path planning, navigation and localization capabilities. This thesis proposes to incorporate sensor uncertainty information in robot environmental map using Fuzzy Boundary Representation (B-rep). Fuzzy B-rep map is generated by first converting measured range data into scan polygons, then combining scan polygons into resultant robot B-rep map by union operation and finally fuzzifying the B-rep map by sweeping sensor uncertainty membership function along generated B-rep map. A map of the fifth floor of E1 building is generated using the proposed method to demonstrate the alleviation in computational and memory load for robot environment mapping using Fuzzy B-rep, in contrast to the conventional grid based mapping methods.