Can a robot understand a human's social behavior? Moreover, how should a robot act in response to a human's behavior? If the goals of artificial intelligence are to understand, imitate, and interact with human level intelligence then researchers must also explore the social underpinnings of this intellect. Our endeavor is buttressed by work in biology, neuroscience, social psychology and sociology. Initially developed by Kelley and Thibaut, social psychology's interdependence theory serves as a conceptual skeleton for the study of social situations, a computational process of social deliberation, and relationships (Kelley&Thibaut, 1978). We extend and expand their original work to explore the challenge of interaction with an embodied, situated robot. This dissertation investigates the use of outcome matrices as a means for computationally representing a robot's interactions. We develop algorithms that allow a robot to create these outcome matrices from perceptual information and then to use them to reason about the characteristics of their interactive partner. This work goes on to introduce algorithms that afford a means for reasoning about a robot's relationships and the trustworthiness of a robot's partners. Overall, this dissertation embodies a general, principled approach to human-robot interaction which results in a novel and scientifically meaningful approach to topics such as trust and relationships.

We design and analyze algorithms for the following two robot navigation problems: 1. TARGET SEARCH. Given a robot located at a point s in the plane, how will a robot navigate to a goal t in the presence of unknown obstacles ? 2. LOCALIZATION. A robot is "lost" in an environment with a map of its surroundings. How will it find its true location by traveling the minimum distance ? Since efficient algorithms for these two problems will make a robot completely autonomous, they have held the interest of both robotics and computer science communities. Previous work has focussed mainly on designing competitive algorithms where the robot's performance is compared to that of an omniscient adversary. For example, a competitive algorithm for target search will compare the distance traveled by the robot with the shortest path from s to t. We analyze these problems from the worst-case perspective, which, in our view, is a more appropriate measure. Our results are : 1. For target search, we analyze an algorithm called Dynamic A*. The robot continuously moves to the goal on the shortest path which it recomputes on the discovery of obstacles. A variant of this algorithm has been employed in Mars Rover prototypes. We show that D* takes O(n log n) time on planar graphs and also show a comparable bound on arbitrary graphs. Thus, our results show that D* combines the optimistic possibility of reaching the goal very soon while competing with depth-first search within a logarithmic factor. 2. For the localization problem, worst-case analysis compares the performance of the robot with the optimal decision tree over the set of possible locations. No approximation algorithm has been known. We give a polylogarithmic approximation algorithm and also show a near-tight lower bound for the grid graphs commonly used in practice. The key idea is to plan travel on a "majority-rule map" which eliminates uncertainty and permits a link to the half-Group Steiner problem. We also extend the problem to polygonal maps by discretizing the domain using novel geometric techniques.

<p>This thesis consists of four independent papers concerningthe control of mobile robots in the context of obstacleavoidance and formation keeping.</p><p>The first paper describes a new theoreticallyv erifiableapproach to obstacle avoidance. It merges the ideas of twoprevious methods, with complementaryprop erties, byusing acombined control Lyapunov function (CLF) and model predictivecontrol (MPC) framework.</p><p>The second paper investigates the problem of moving a fixedformation of vehicles through a partiallykno wn environmentwith obstacles. Using an input to state (ISS) formulation theconcept of configuration space obstacles is generalized toleader follower formations. This generalization then makes itpossible to convert the problem into a standard single vehicleobstacle avoidance problem, such as the one considered in thefirst paper. The properties of goal convergence and safetyth uscarries over to the formation obstacle avoidance case.</p><p>In the third paper, coordination along trajectories of anonhomogenuos set of vehicles is considered. Byusing a controlLyapunov function approach, properties such as boundedformation error and finite completion time is shown.</p><p>Finally, the fourth paper applies a generalized version ofthe control in the third paper to translate,rotate and expanda formation. It is furthermore shown how a partial decouplingof formation keeping and formation mission can be achieved. Theapproach is then applied to a scenario of underwater vehiclesclimbing gradients in search for specific thermal/biologicalregions of interest. The sensor data fusion problem fordifferent formation configurations is investigated and anoptimal formation geometryis proposed.</p><p><b>Keywords:</b>Mobile Robots, Robot Control, ObstacleAvoidance, Multirobot System, Formation Control, NavigationFunction, Lyapunov Function, Model Predictive Control, RecedingHorizon Control, Gradient Climbing, Gradient Estimation.</p>

<p>This thesis deals with various aspects of indoor navigationfor mobile robots. For a system that moves around in ahousehold or office environment,two major problems must betackled. First, an appropriate control scheme has to bedesigned in order to navigate the platform. Second, the form ofrepresentations of the environment must be chosen.</p><p>Behaviour based approaches have become the dominantmethodologies for designing control schemes for robotnavigation. One of them is the dynamical systems approach,which is based on the mathematical theory of nonlineardynamics. It provides a sound theoretical framework for bothbehaviour design and behaviour coordination. In the workpresented in this thesis, the approach has been used for thefirst time to construct a navigation system for realistic tasksin large-scale real-world environments. In particular, thecoordination scheme was exploited in order to combinecontinuous sensory signals and discrete events for decisionmaking processes. In addition, this coordination frameworkassures a continuous control signal at all times and permitsthe robot to deal with unexpected events.</p><p>In order to act in the real world, the control system makesuse of representations of the environment. On the one hand,local geometrical representations parameterise the behaviours.On the other hand, context information and a predefined worldmodel enable the coordination scheme to switchbetweensubtasks. These representations constitute symbols, on thebasis of which the system makes decisions. These symbols mustbe anchored in the real world, requiring the capability ofrelating to sensory data. A general framework for theseanchoring processes in hybrid deliberative architectures isproposed. A distinction of anchoring on two different levels ofabstraction reduces the complexity of the problemsignificantly.</p><p>A topological map was chosen as a world model. Through theadvanced behaviour coordination system and a proper choice ofrepresentations,the complexity of this map can be kept at aminimum. This allows the development of simple algorithms forautomatic map acquisition. When the robot is guided through theenvironment, it creates such a map of the area online. Theresulting map is precise enough for subsequent use innavigation.</p><p>In addition, initial studies on navigation in human-robotinteraction tasks are presented. These kinds of tasks posedifferent constraints on a robotic system than, for example,delivery missions. It is shown that the methods developed inthis thesis can easily be applied to interactive navigation.Results show a personal robot maintaining formations with agroup of persons during social interaction.</p><p><b>Keywords:</b>mobile robots, robot navigation, indoornavigation, behaviour based robotics, hybrid deliberativesystems, dynamical systems approach, topological maps, symbolanchoring, autonomous mapping, human-robot interaction</p>

Human-scale mobile robots that manipulate objects (mobile manipulators) have the potential to perform a variety of useful roles in healthcare. Many promising roles for robots require physical contact with patients and caregivers, which is fraught with both psychological and physical implications. In this thesis, we used a human factors approach to evaluate system performance and participant responses when potential end users performed a healthcare task involving physical contact with a robot. We performed four human-robot interaction studies with 100 people who were not experts in robotics (naive participants). We show that physical contact between naive participants and human-scale mobile manipulators can be acceptable and effective in a variety of healthcare contexts. In this thesis, we investigated two forms of touch-based (haptic) interaction relevant to healthcare. First, we studied how participants responded to physical contact initiated by an autonomous robotic nurse. On average, people responded favorably to robot-initiated touch when the robot indicated that it was a necessary part of a healthcare task. However, their responses strongly depended on what they thought the robot's intentions were, which suggests that this will be an important consideration for future healthcare robots. Second, we investigated the coordination of whole-body motion between human-scale robots and people by the application of forces to the robot's hands and arms. Nurses found this haptic interaction to be intuitive and preferred it over a standard gamepad interface. They also navigated the robot through a cluttered healthcare environment in less time, with fewer collisions, and with less cognitive load via haptic interaction. Through a study with expert dancers, we demonstrated the feasibility of robots as dance-based exercise partners. The experts rated a robot that used only haptic interaction to be a good follower according to subjective measures of dance quality. We also determined that healthy older adults were accepting of using a robot for partner dance-based exercise. On average, they found the robot easy and enjoyable to use and that it performed a partnered stepping task well. The findings in this work make several impacts on the design of robots in healthcare. We found that the perceived intent of robot-initiated touch significantly influenced people's responses. Thus, we determined that autonomous robots that initiate touch with patients can be acceptable in some contexts. This result highlights the importance of considering the psychological responses of users when designing physical human-robot interactions in addition to considering the mechanics of performing tasks. We found that naive users across three user groups could quickly learn how to effectively use physical interaction to lead a robot during navigation, positioning, and partnered stepping tasks. These consistent results underscore the value of using physical interaction to enable users of varying backgrounds to lead a robot during whole-body motion coordination across different healthcare contexts.

Multi-vehicle systems offer many advantages in engineering applications such as increased efficiency and robustness. However, the disadvantage of multi-vehicle systems is that they require a high level of organization and coordination in order to successfully complete a task. Formation control is a field of engineering that addresses this issue, and provides coordination schemes to successfully implement multi-vehicle systems. Two approaches to group coordination were proposed in this work: geometric and self-organizing formations. A geometric reconfiguring formation was developed using the leader-follower method, and the self-organizing formation was developed using the velocity potential equations from fluid flow theory. Both formation controllers were first tested in simulation in MATLAB, and then implemented on the X80 mobile robot units. Various experiments were conducted to test the formations under difficult obstacle scenarios. The robots successfully navigated through the obstacles as a coordinated as a team using the self-organizing and geometric formation control approaches.

The navigation tasks in advanced home robotic applications incorporating reliable revisiting strategies are dependent on very low cost but nevertheless rather accurate localization systems. In this paper a localization system based on the principle of trilateration is described. The proposed system uses only a single small base station, but achieves accuracies comparable to systems using spread beacons and it performs sufficiently for map building. Thus it is a standalone system and needs no odometry or other auxiliary sensors. Furthermore a new approach for the problem of the reliably detection of areas without direct line of sight is presented. The described system is very low cost and it is designed for use in indoor service robotics. The paper gives an overview on the system concept and special design solutions and proves the possible performances with experimental results.

This thesis consists of four independent papers concerningthe control of mobile robots in the context of obstacleavoidance and formation keeping. The first paper describes a new theoreticallyv erifiableapproach to obstacle avoidance. It merges the ideas of twoprevious methods, with complementaryprop erties, byusing acombined control Lyapunov function (CLF) and model predictivecontrol (MPC) framework. The second paper investigates the problem of moving a fixedformation of vehicles through a partiallykno wn environmentwith obstacles. Using an input to state (ISS) formulation theconcept of configuration space obstacles is generalized toleader follower formations. This generalization then makes itpossible to convert the problem into a standard single vehicleobstacle avoidance problem, such as the one considered in thefirst paper. The properties of goal convergence and safetyth uscarries over to the formation obstacle avoidance case. In the third paper, coordination along trajectories of anonhomogenuos set of vehicles is considered. Byusing a controlLyapunov function approach, properties such as boundedformation error and finite completion time is shown. Finally, the fourth paper applies a generalized version ofthe control in the third paper to translate,rotate and expanda formation. It is furthermore shown how a partial decouplingof formation keeping and formation mission can be achieved. Theapproach is then applied to a scenario of underwater vehiclesclimbing gradients in search for specific thermal/biologicalregions of interest. The sensor data fusion problem fordifferent formation configurations is investigated and anoptimal formation geometryis proposed. Keywords:Mobile Robots, Robot Control, ObstacleAvoidance, Multirobot System, Formation Control, NavigationFunction, Lyapunov Function, Model Predictive Control, RecedingHorizon Control, Gradient Climbing, Gradient Estimation. / QC 20111121

This thesis addresses the pose stabilization of a car-like vehicle using omnidirectional visual feedback. The presented method allows a vehicle to servo to a pre-learnt target pose based on feature bearing angle and range discrepancies between the vehicle's current view of the environment and that seen at the learnt location. The best example of such a task is the use of visual feedback for autonomous parallel-parking of an automobile. Much of the existing work in pose stabilization is highly theoretical in nature with few examples of implementations on 'real' vehicles, let alone vehicles representative of those found in industry. The work in this thesis develops a suitable test platform and implements vision-based pose stabilization techniques. Many of the existing techniques were found to fail due to vehicle steering and velocity loop dynamics, and more significantly, with steering input saturation. A technique which does cope with the characteristics of 'real' vehicles is to divide the task into predefined stages, essentially dividing the state space into sub-manifolds. For a car-like vehicle, the strategy used is to stabilize the vehicle to the line which has the correct orientation and contains the target location. Once on the line, the vehicle then servos to the desired pose. This strategy can accommodate velocity and steering loop dynamics, and input saturation. It can also allow the use of linear control techniques for system analysis and tuning of control gains. To perform pose stabilization, good estimates of vehicle pose are required. A simple, yet robust, method derived from the visual homing literature is to sum the range vectors to all the landmarks in the workspace and divide by the total number of landmarks--the Improved Average Landmark Vector. By subtracting the IALV at the target location from the currently calculated IALV, an estimate of vehicle pose is obtained. In this work, views of the world are provided by an omnidirectional camera, while a magnetic compass provides a reference direction. The landmarks used are red road cones which are segmented from the omnidirectional colour images using a pre-learnt, two-dimensional lookup table of their colour profile. Range to each landmark is estimated using a model of the optics of the system, based on a flat-Earth assumption. A linked-list based method is used to filter the landmarks over time. Complementary filtering techniques, which combine the vision data with vehicle odometry, are used to improve the quality of the measurements.

CAPES / Mobile robot applications that involve automated exploration and inspection of environments are often dependant on novelty detection, the ability to differentiate between common and uncommon perceptions. Because novelty can be anything that deviates from the normal context, we argue that in order to implement a novelty filter it is necessary to exploit the robot's sensory data from the ground up, building models of normality rather than abnormality. In this work we use unrestricted colour visual data as perceptual input to on-line incremental learning algorithms. Unlike other sensor modalities, vision can provide a variety of useful information about the environment through massive amounts of data, which often need to be reduced for realtime operation. Here we use mechanisms of visual attention to select candidate image regions to be encoded and fed to higher levels of processing, enabling the localisation of novel features within the input image frame. An extensive series of experiments using visual input, obtained by a real mobile robot interacting with laboratory and medium-scale real world environments, are used to discuss different visual novelty filter configurations. We compare performance and functionality of novelty detection mechanisms based on the Grow-When-Required neural network and incremental Principal Component Analysis. Results are assessed using both qualitative and quantitative methods, demonstrating advantages and disadvantages of each investigated approach.