Selection et controle de modes de deplacement pour un robot mobile autonome en environnements naturels

Autonomous navigation and locomotion of a mobile robot in natural environments remain a rather open issue. Several functionalities are required to complete the usual perception/decision/action cycle. They can be divided in two main categories : navigation (perception and decision about the movement) and locomotion (movement execution). In order to be able to face the large range of possible situations in natural environments, it is essential to make use of various kinds of complementary functionalities, defining various navigation and locomotion modes. Indeed, a number of navigation and locomotion approaches have been proposed in the literature for the last years, but none can pretend being able to achieve autonomous navigation and locomotion in every situation. Thus, it seems relevant to endow an outdoor mobile robot with several complementary navigation and locomotion modes. Accordingly, the robot must also have means to select the most appropriate mode to apply. This thesis proposes the development of such a navigation/locomotion mode selection system, based on two types of data: an observation of the context to determine in what kind of situation the robot has to achieve its movement and an evaluation of the behavior of the current mode, made by monitors which influence the transitions towards other modes when the behavior of the current one is considered as non satisfying. Hence, this document introduces a probabilistic framework for the estimation of the mode to be applied, some navigation and locomotion modes used, a qualitative terrain representation method (based on the evaluation of a difficulty computed from the placement of the robot's structure on a digital elevation map), and monitors that check the behavior of the modes used (evaluation of rolling locomotion efficiency, robot's attitude and configuration watching. . .). Some experimental results obtained with those elements integrated on board two different outdoor robots are presented and discussed.

A probabilistic framework to monitor a multi-mode outdoor robot

This paper presents an approach to autonomously monitor the behavior of a robot endowed with several navigation and locomotion modes, adapted to the terrain to traverse. The mode selection process is done in two steps: the best suited mode is firstly selected on the basis of initial information or a qualitative map built on-line by the robot. Then, the motions of the robot are monitored by various processes that update mode transition probabilities in a Markov system. The paper focuses on this latter selection process: the overall approach is depicted, and preliminary experimental results are presented

Acoustic Masking of a Stealthy Outdoor Robot Tracking a Dynamic Target

This work is motivated by the desire to covertly track mobile targets, either animal or human, in previously unmapped outdoor natural environments using off-road robotic platforms with a non-negligible acoustic signature. The use of robots for stealthy surveillance is not new. Many studies exist but only consider the navigation problem to maintain visual covertness. However, robotic systems also have a significant acoustic footprint from the onboard sensors, motors, computers and cooling systems, and also from the wheels interacting with the terrain during motion. All these can jepordise any visual covertness. In this work, we experimentally explore the concepts of opportunistically utilizing naturally occurring sounds within outdoor environments to mask the motion of a robot, and being visually covert whilst maintaining constant observation of the target. Our experiments in a constrained outdoor built environment demonstrate the effectiveness of the concept by showing a reduced acoustic signature as perceived by a mobile target allowing the robot to covertly navigate to opportunistic vantage points for observation.

Developing grounded representations for robots through the principles of sensorimotor coordination

Robots currently recognise and use objects through algorithms that are hand-coded or specifically trained. Such robots can operate in known, structured environments but cannot learn to recognise or use novel objects as they appear. This thesis demonstrates that a robot can develop meaningful object representations by learning the fundamental relationship between action and change in sensory state; the robot learns sensorimotor coordination. Methods based on Markov Decision Processes are experimentally validated on a mobile robot capable of gripping objects, and it is found that object recognition and manipulation can be learnt as an emergent property of sensorimotor coordination.

Vision based guidance for robot navigation in agriculture

This paper describes a novel vision based texture tracking method to guide autonomous vehicles in agricultural fields where the crop rows are challenging to detect. Existing methods require sufficient visual difference between the crop and soil for segmentation, or explicit knowledge of the structure of the crop rows. This method works by extracting and tracking the direction and lateral offset of the dominant parallel texture in a simulated overhead view of the scene and hence abstracts away crop-specific details such as colour, spacing and periodicity. The results demonstrate that the method is able to track crop rows across fields with extremely varied appearance during day and night. We demonstrate this method can autonomously guide a robot along the crop rows.

Development of a 3500-tonne field robot

The mining industry is highly suitable for the application of robotics and automation technology, since the work is arduous, dangerous, and often repetitive. This paper presents a broad overview of the issues involved in the development of a physically large and complex field robotic system—a 3500-tonne mining machine (dragline). Draglines are “walking cranes” used in open-pit coal mining to remove the material covering a coal seam. The critical issues of robust load position sensing, modeling of the dynamics of the electrical drive system and the swinging load, control strategies, the operator interface, and automation system architecture are addressed. An important aspect of this system is that it must work cooperatively with a human operator, seamlessly passing control back and forth in order to achieve the main aim—increased productivity.

Connecting the person with dementia and family : a feasibility study of a telepresence robot

Background Maintenance of communication is important for people with dementia living in long-term care. The purpose of this study was to assess the feasibility of using “Giraff”, a telepresence robot to enhance engagement between family and a person with dementia living in long-term care. Methods A mixed-methods approach involving semi-structured interviews, call records and video observational data was used. Five people with dementia and their family member participated in a discussion via the Giraff robot for a minimum of six times over a six-week period. A feasibility framework was used to assess feasibility and included video analysis of emotional response and engagement. Results Twenty-six calls with an average duration of 23 mins took place. Residents showed a general state of positive emotions across the calls with a high level of engagement and a minimal level of negative emotions. Participants enjoyed the experience and families reported that the Giraff robot offered the opportunity to reduce social isolation. A number of software and hardware challenges were encountered. Conclusions Participants perceived this novel approach to engage families and people with dementia as a feasible option. Participants were observed and also reported to enjoy the experience. The technical challenges identified have been improved in a newer version of the robot. Future research should include a feasibility trial of longer duration, with a larger sample and a cost analysis.

A simple vehicle for the transportation of a humanoid Nao robot

Locomotion and autonomy in humanoid robots is of utmost importance in integrating them into social and community service type roles. However, the limited range and speed of these robots severely limits their ability to be deployed in situations where fast response is necessary. While the ability for a humanoid to drive a vehicle would aide in increasing their overall mobility, the ability to mount and dismount a vehicle designed for human occupants is a non-trivial problem. To address this issue, this paper presents an innovative approach to enabling a humanoid robot to mount and dismount a vehicle by proposing a simple mounting bracket involving no moving parts. In conjunction with a purpose built robotic vehicle, the mounting bracket successfully allowed a humanoid Nao robot to mount, dismount and drive the vehicle.

Audio signalling as a backup communication channel for multi-robot systems

This paper presents a low-bandwidth multi-robot communication system designed to serve as a backup communication channel in the event a robot suffers a network device fault. While much research has been performed in the area of distributing network communication across multiple robots within a system, individual robots are still susceptible to hardware failure. In the past, such robots would simply be removed from service, and their tasks re-allocated to other members. However, there are times when a faulty robot might be crucial to a mission, or be able to contribute in a less communication intensive area. By allowing robots to encode and decode messages into unique sequences of DTMF symbols, called words, our system is able to facilitate continued low-bandwidth communication between robots without access to network communication. Our results have shown that the system is capable of permitting robots to negotiate task initiation and termination, and is flexible enough to permit a pair of robots to perform a simple turn taking task.

Principles of goal-directed spatial robot navigation in biomimetic models

Mobile robots and animals alike must effectively navigate their environments in order to achieve their goals. For animals goal-directed navigation facilitates finding food, seeking shelter or migration; similarly robots perform goal-directed navigation to find a charging station, get out of the rain or guide a person to a destination. This similarity in tasks extends to the environment as well; increasingly, mobile robots are operating in the same underwater, ground and aerial environments that animals do. Yet despite these similarities, goal-directed navigation research in robotics and biology has proceeded largely in parallel, linked only by a small amount of interdisciplinary research spanning both areas. Most state-of-the-art robotic navigation systems employ a range of sensors, world representations and navigation algorithms that seem far removed from what we know of how animals navigate; their navigation systems are shaped by key principles of navigation in ‘real-world’ environments including dealing with uncertainty in sensing, landmark observation and world modelling. By contrast, biomimetic animal navigation models produce plausible animal navigation behaviour in a range of laboratory experimental navigation paradigms, typically without addressing many of these robotic navigation principles. In this paper, we attempt to link robotics and biology by reviewing the current state of the art in conventional and biomimetic goal-directed navigation models, focusing on the key principles of goal-oriented robotic navigation and the extent to which these principles have been adapted by biomimetic navigation models and why.

Visual place recognition for persistent robot navigation in changing environments

This thesis demonstrates that robots can learn about how the world changes, and can use this information to recognise where they are, even when the appearance of the environment has changed a great deal. The ability to localise in highly dynamic environments using vision only is a key tool for achieving long-term, autonomous navigation in unstructured outdoor environments. The proposed learning algorithms are designed to be unsupervised, and can be generated by the robot online in response to its observations of the world, without requiring information from a human operator or other external source.

Understanding welding practices on shipyards : an ethnographic study for designing an interactive robot welder

The aim of this ethnographic study was to understand welding practices in shipyard environments with the purpose of designing an interactive welding robot that can help workers with their daily job. The robot is meant to be deployed for automatic welding on jack-up rig structures. The design of the robot turns out to be a challenging task due to several problematic working conditions on the shipyard, such as dust, irregular floor, high temperature, wind variations, elevated working platforms, narrow spaces, and circular welding paths requiring a robotic arm with more than 6 degrees of freedom. Additionally, the environment is very noisy and the workers – mostly foreigners – have a very basic level of English. These two issues need to be taken into account when designing the interactive user interface for the robot. Ideally, the communication flow between the two parties involved should be as frictionless as possible. The paper presents the results of our field observations and welders’ interviews, as well as our robot design recommendation for the next project stage.