International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / This paper describes the experiences and final design of one team in a senior design competition to build a soccer-playing robot. Each robot was required to operate autonomously under the remote control of a dedicated host computer via a wireless link. Each team designed and constructed a robot and wrote its control software. Certain components were made available to all teams. These components included wireless transmitters and receivers, microcontrollers, overhead cameras, image processing boards, and desktop computers. This paper describes the team’s hardware and software designs, problems they encountered, and lessons learned.

Les singularités sont des poses particulières où le robot ne peut être contrôlé et ou les efforts subis par les articulations du mécanisme peuvent tendre vers l'infini ce qui entraine alors une détérioration du mécanisme. Bien que ces dernières années de larges avancées dans la classification et la détection des singularités aient eu lieu la question de la proximité d'une pose à une pose singulière reste ouverte. Dans cette thèse nous tentons d'y répondre avec une approche basée sur la statique. En effet si pour une pose donnée les efforts subis par les articulation restent inférieur à un certain seuil, on pourra affirmer que le robot ne subira pas de détérioration et nous le considérons comme assez éloigné d'une pose singulière. Dans un premier temps, nous proposons un algorithme qui calcule, pour un robot plan dont l'orientation est fixée et dont la plate-forme supporte une charge connue, la frontière de la région pour laquelle on assure l'intégrité physique du manipulateur. Cet algorithme étant difficile à étendre pour des robots à plus de 3 degrés de libertés, nous en proposons un second basé sur l'analyse par intervalles.

In this thesis, we examine algorithms for the division of labour in a group of robot. The algorithms make no use of direct communication. Instead, they are based only on the interactions among the robots and between the group and the environment. Division of labour is the mechanism that decides how many robots shall be used to perform a task. The efficiency of the group of robots depends in fact on the number of robots involved in a task. If too few robots are used to achieve a task, they might not be successful or might perform poorly. If too many robots are used, it might be a waste of resources. The number of robots to use might be decided a priori by the system designer. More interestingly, the group of robots might autonomously select how many and which robots to use. In this thesis, we study algorithms of the latter type. The robotic literature offers already some solutions, but most of them use a form of direct communication between agents. Direct, or explicit, communication between the robots is usually considered a necessary condition for co-ordination. Recent studies have questioned this assumption. The claim is based on observations of animal colonies, e.g., ants and termites. They can effectively co-operate without directly communicating, but using indirect forms of communication like stigmergy. Because they do not rely on communication, such colonies show robust behaviours at group level, a condition that one wishes also for groups of robots. Algorithms for robot co-ordination without direct communication have been proposed in the last few years. They are interesting not only because they are a stimulating intellectual challenge, but also because they address a situation that might likely occur when using robots for real-world out-door applications. Unfortunately, they are still poorly studied. This thesis helps the understanding and the development of such algorithms. We start from a specific case to learn its characteristics. Then we improve our understandings through comparisons with other solutions, and finally we port everything into another domain. We first study an algorithm for division of labour that was inspired by ants' foraging. We test the algorithm in an application similar to ants' foraging: prey retrieval. We prove that the model used for ants' foraging can be effective also in real conditions. Our analysis allows us to understand the underlying mechanisms of the division of labour and to define some way of measuring it. Using this knowledge, we continue by comparing the ant-inspired algorithm with similar solutions that can be found in the literature and by assessing their differences. In performing these comparisons, we take care of using a formal methodology that allows us to spare resources. Namely, we use concepts of experiment design to reduce the number of experiments with real robots, without losing significance in the results. Finally, we apply and port what we previously learnt into another application: Sensor/Actor Networks (SANETs). We develop an architecture for division of labour that is based on the same mechanisms as the ants' foraging model. Although the individuals in the SANET can communicate, the communication channel might be overloaded. Therefore, the agents of a SANET shall be able to co-ordinate without accessing the communication channel.

The aim of this thesis is the stiffness analysis of cable-driven parallel robots. Cable-driven parallel robots have drawn considerable attention because of their unique abilities and advantages such as the large workspace, light weight of cable actuators, easy disassembly and transportation of the robot. The mobile platform of a cable-driven parallel robot is attached to the base with multiple cables. One of the parameters that should be studied to make sure a robot is able to execute a task accurately is stiffness of the robot. In order to investigate the stiffness behaviour of a robot, the stiffness matrix can be calculated as the first step. Because cables act in tension, keeping the positive tension in cables becomes a challenge. In order to have a fully controllable robot, an actuation redundancy is needed. These complexities are addressed in the thesis and simulations. In this thesis, the complete form of the stiffness matrix is considered without neglecting any terms in calculation of the stiffness. Some stiffness indices such as single-dimensional stiffness based on stiffness ellipse, directional stiffness and condition number of the stiffness matrix are introduced and calculated and stiffness maps of the robot are developed. In addition, the issue of unit inconsistency in calculating the stiffness index is addressed. One of the areas which is also addressed in this thesis is failure analysis based on the stiffness of robot. The effect of the failure in one or more cables or motors is modelled and stiffness maps are developed for the failure situation. It is shown that by changing the anchor position and mobile platform orientation, the lost stiffness after failure of a cable or motor can be retrieved partially. Optimum anchor position and mobile platform orientation are identified to maximize the area of the stiffness map. Condition number of the stiffness matrix while robot is following a trajectory is optimized. In addition, when one cable fails during the path planning, the recovery of the robot is studied. Finally, these analyses on stiffness and failure provide the designer with the necessary and valuable information about the anchor positions and actuator toques. / Thesis (Ph.D, Mechanical and Materials Engineering) -- Queen's University, 2013-04-27 08:47:26.297

Autonomous robots deployed in complex, natural human environments such as homes and offices need to manipulate numerous objects throughout their deployment. For an autonomous robot to operate effectively in such a setting and not require excessive training from a human operator, it should be capable of discovering how to reliably manipulate novel objects it encounters. We characterize the possible methods by which a robot can act on an object using the concept of affordances. We define affordance-based behaviors as object manipulation strategies available to a robot, which correspond to specific semantic actions over which a task-level planner or end user of the robot can operate. This thesis concerns itself with developing the representation of these affordance- based behaviors along with associated learning algorithms. We identify three specific learning problems. The first asks which affordance-based behaviors a robot can successfully apply to a given object, including ones seen for the first time. Second, we examine how a robot can learn to best apply a specific behavior as a function of an object’s shape. Third, we investigate how learned affordance knowledge can be transferred between different objects and different behaviors. We claim that decomposing affordance-based behaviors into three separate factors— a control policy, a perceptual proxy, and a behavior primitive—aids an autonomous robot in learning to manipulate. Having a varied set of affordance-based behaviors available allows a robot to learn which behaviors perform most effectively as a function of an object’s identity or pose in the workspace. For a specific behavior a robot can use interactions with previously encountered objects to learn to robustly manipulate a novel object when first encountered. Finally, our factored representation allows a robot to transfer knowledge learned with one behavior to effectively manipulate an object in a qualitatively different manner by using a distinct controller or behavior primitive. We evaluate all work on a bimanual, mobile-manipulator robot. In all experiments the robot interacts with real-world objects sensed by an RGB-D camera.

This thesis aims to design an appropriate human-following solution for a mobile robot. The research can be characterised as interactive model building for a Human Robot Interaction (HRI) scenario. It studies possible proposals for the robot system that learns to accomplish the task autonomously, based on the human preference about the positions and movements of the robot during the interaction. A multilayered feedforward network framework with backpropagation is the adopted learning strategy. The research breaks the task of following a human into three independent behaviours: social positioning, human avoidance and obstacle avoidance. Social positioning is the behaviour that moves the robot, via reasonable paths, to the most appropriate location to follow the human. Both the location and the paths reflect the preference of the human, which varies by individual. The main body of the research therefore proposes a using-while-learning system for this behaviour such that the robot can adapt to the human’s preference autonomously. This research investigated multilayered feedforward networks with backpropagation learning to fulfil the social learning task. This learning model is less used in HRI because a complete set of correct training data doesn’t exist as the human preference is initially unknown. The research proposes a novel method to generate the training data during the operation of learning and introduces the concept of adaptive and reactive learning. A novel training scheme that combines the two learning threads has been proposed, in which the learning is fast, robust and able to adapt to new features of the human preference online. The system enables the behaviour to be a real using-while-learning system as no pre-training of any form is needed to ensure the successful performance of the behaviour. Extensive simulations and interactive experiments with humans have also been conducted to prove the robustness of the system.

No description available.

Visually guided navigation represents a long standing goal in robotics. Insights may be drawn from various insect species for which visual information has been shown sufficient for navigation in complex environments, however the generality of visual homing abilities across insect species remains unclear. Furthermore variousmodels have been proposed as strategies employed by navigating insects yet comparative studies across models and species are lacking. This work addresses these questions in two insect species not previously studied: the field cricket Gryllus bimaculatus for which almost no navigational data is available; and the European desert ant Cataglyphis velox, a relation of the African desert ant Cataglyphis bicolor which has become a model species for insect navigation studies. The ability of crickets to return to a hidden target using surrounding visual cues was tested using an analogue of the Morris water-maze, a standard paradigm for spatial memory testing in rodents. Crickets learned to re-locate the hidden target using the provided visual cues, with the best performance recorded when a natural image was provided as stimulus rather than clearly identifiable landmarks. The role of vision in navigation was also observed for desert ants within their natural habitat. Foraging ants formed individual, idiosyncratic, visually guided routes through their cluttered surroundings as has been reported in other ant species inhabiting similar environments. In the absence of other cues ants recalled their route even when displaced along their path indicating that ants recall previously visited places rather than a sequence of manoeuvres. Image databases were collected within the environments experienced by the insects using custompanoramic cameras that approximated the insect eye viewof the world. Six biologically plausible visual homing models were implemented and their performance assessed across experimental conditions. The models were first assessed on their ability to replicate the relative performance across the various visual surrounds in which crickets were tested. That is, best performance was sought with the natural scene, followed by blank walls and then the distinct landmarks. Only two models were able to reproduce the pattern of results observed in crickets: pixel-wise image difference with RunDown and the centre of mass average landmark vector. The efficacy of models was then assessed across locations in the ant habitat. A 3D world was generated from the captured images providing noise free and high spatial resolution images asmodel input. Best performancewas found for optic flow and image difference based models. However in many locations the centre of mass average landmark vector failed to provide reliable guidance. This work shows that two previously unstudied insect species can navigate using surrounding visual cues alone. Moreover six biologically plausible models of visual navigation were assessed in the same environments as the insects and only an image difference based model succeeded in all experimental conditions.

This thesis presents a novel distributed control method which allows a collection of independently mobile robotic units, with two or three dimensional movement, to self-assemble into self-repairing hierarchical structures. The proposed method utilises a simple model of the cellular adhesion mechanisms observed in biological cells, allowing the robotic units to form virtually bonded aggregates which behave as predicted by Steinberg’s differential adhesion hypothesis. Simulated robotic units based on the design of the subaquatic HYDRON module are introduced as a possible platform on which the model can be implemented. The units are used to carry out a detailed investigation of the model behaviour and parameter space focusing on the two main tasks of rounding and sorting in both two and three dimensions. These tasks assess the model’s ability to reach a thermodynamically stable configuration when the aggregates consist of either a single population of units or multiple populations of units with differing adhesive properties. The results are analysed in detail with particular attention given to the role of random movements in determining the overall performance, and demonstrate that this model provides a very robust solution to these complex tasks. Finally, a possible extension of this work is presented in which the original model is combined with a genetic regulatory network controller. The performance of this composite is evaluated, and the benefits of this hybrid approach, in which a powerful control system manipulates a robust self-organising behaviour, are discussed.

Recent developments in robot technology have contributed to the advancement of autonomous behaviours in human-robot systems; for example, in following instructions received from an interacting human partner. Nevertheless, increasingly many systems are moving towards more seamless forms of interaction, where factors such as implicit trust and persuasion between humans and robots are brought to the fore. In this context, the problem of attaining, through suitable computational models and algorithms, more complex strategic behaviours that can influence human decisions and actions during an interaction, remains largely open. To address this issue, this thesis introduces the problem of decision shaping in strategic interactions between humans and robots, where a robot seeks to lead, without however forcing, an interacting human partner to a particular state. Our approach to this problem is based on a combination of statistical modeling and synthesis of demonstrated behaviours, which enables robots to efficiently adapt to novel interacting agents. We primarily focus on interactions between autonomous and teleoperated (i.e. human-controlled) NAO humanoid robots, using the adversarial soccer penalty shooting game as an illustrative example. We begin by describing the various challenges that a robot operating in such complex interactive environments is likely to face. Then, we introduce a procedure through which composable strategy templates can be learned from provided human demonstrations of interactive behaviours. We subsequently present our primary contribution to the shaping problem, a Bayesian learning framework that empirically models and predicts the responses of an interacting agent, and computes action strategies that are likely to influence that agent towards a desired goal. We then address the related issue of factors affecting human decisions in these interactive strategic environments, such as the availability of perceptual information for the human operator. Finally, we describe an information processing algorithm, based on the Orient motion capture platform, which serves to facilitate direct (as opposed to teleoperation-mediated) strategic interactions between humans and robots. Our experiments introduce and evaluate a wide range of novel autonomous behaviours, where robots are shown to (learn to) influence a variety of interacting agents, ranging from other simple autonomous agents, to robots controlled by experienced human subjects. These results demonstrate the benefits of strategic reasoning in human-robot interaction, and constitute an important step towards realistic, practical applications, where robots are expected to be not just passive agents, but active, influencing participants.