The paper addresses the problem of steering a fleet of wheeled robots along a set of given spatial paths, while keeping a desired inter-vehicle formation pattern. This problem arises for example when multiple vehicles...
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The paper addresses the problem of steering a fleet of wheeled robots along a set of given spatial paths, while keeping a desired inter-vehicle formation pattern. This problem arises for example when multiple vehicles are required to scan a given area in cooperation. In a possible mission scenario, one of the vehicles acts as a leader and follows a path accurately, while the other vehicles follow paths that are naturally determined by the formation pattern imposed. The solution adopted for coordinated path following builds on Lyapunov-based techniques and addresses explicitly the constraints imposed by the topology of the inter-vehicle communications network, which is captured in the framework of directed graph theory. With this set-up, path following (in space) and inter-vehicle coordination (in time) are essentially decoupled. Path following for each vehicle amounts to reducing a conveniently defined error variable to zero. Vehicle coordination is achieved by adjusting the speed of each of the vehicles along its path, according to information on the position of the other vehicles, as determined by the communications topology adopted. Simulations illustrate the efficacy of the solution proposed.
The clinical management of skeletal trauma and disease relies on radiographic imaging to infer bone quality. However, bone strength does not necessarily correlate well with image intensity. There is a need for a safe ...
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Building cooperatively 3-D maps of unknown environments is one of the application fields of multi-robot systems. This article introduces a distributed architecture, within a probabilistic framework for vision-based 3-...
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Building cooperatively 3-D maps of unknown environments is one of the application fields of multi-robot systems. This article introduces a distributed architecture, within a probabilistic framework for vision-based 3-D mapping, whereby each robot is committed to cooperate with other robots through information sharing. An entropy-based measure of information utility is defined, which a robot uses for communicating to its teammates the most useful measurements, thus preventing the robot to overwhelm communication resources with redundant information. Experiments with real robots, equipped with stereo-vision, yielded important conclusions about the way robots should cooperate on sharing information.
This article presents an innovative probabilistic approach for building volumetric maps of unknown environments with autonomous mobile robots, which is based on information theory. Each mobile robot uses an entropy gr...
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This article presents an innovative probabilistic approach for building volumetric maps of unknown environments with autonomous mobile robots, which is based on information theory. Each mobile robot uses an entropy gradient-based exploration strategy, with the aim of maximizing information gain when building and improving a 3D map upon measurements yielded by an on-board stereo-vision sensor. The proposed framework was validated through experiments with a real mobile robot equipped with stereo-vision, in order to be further used on cooperative volumetric mapping with teams of mobile robots.
Consequent to previously published theoretical work by Marshall, Broucke, and Francis, this paper summarizes the apparatus and results of multi-vehicle coordination experiments conducted at the University of Toronto I...
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The paper describes the formulation of multi-contact compliant motion control. It extends our previous work to non-rigid environments. The contact forces are controlled through active observers (AOB), based on the Kal...
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The paper describes the formulation of multi-contact compliant motion control. It extends our previous work to non-rigid environments. The contact forces are controlled through active observers (AOB), based on the Kalman filter theory. Noise characteristics enter in the control design and are estimated on-line. Experimental results are provided.
The paper presents control architecture for steer-by-wire (SBW) vehicles based on conventional ones. Estimation techniques linked to state space design enter in the control loop. The active observer (AOB) is extended ...
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The paper presents control architecture for steer-by-wire (SBW) vehicles based on conventional ones. Estimation techniques linked to state space design enter in the control loop. The active observer (AOB) is extended to under-actuated multiple-input-multiple-output (MIMO) systems. Stochastic strategies are presented. Simulation results for the model-matching problem are presented. In the presence of road disturbances (e.g. lateral wind), SBW vehicles have superior performance.
The paper addresses the problem of autonomous underwater vehicle (AUV) modeling and parameter estimation as a means to predict the expected dynamic performance of underwater vehicles and thus provide solid guidelines ...
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The paper addresses the problem of autonomous underwater vehicle (AUV) modeling and parameter estimation as a means to predict the expected dynamic performance of underwater vehicles and thus provide solid guidelines during their design phase. The use of analytical and semi-empirical (ASE) methods to predict the hydrodynamic derivatives of a large class of AUVs with conventional, streamlined bodies is discussed. An application is made to the estimation of the hydrodynamic derivatives of the MAYA AUV, an autonomous vehicle that is being developed under a joint Indian-Portuguese project. The estimates are used to predict the behavior of the vehicle in the vertical plane and to assess the impact of stern plane size on its expected performance.
Mobile robots must be able to build their own maps to navigate in unknown worlds. Expanding a previously proposed method [Rui Araujo et al., April 1999], based on the fuzzy ART neural architecture (FARTNA), this paper...
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Mobile robots must be able to build their own maps to navigate in unknown worlds. Expanding a previously proposed method [Rui Araujo et al., April 1999], based on the fuzzy ART neural architecture (FARTNA), this paper introduces a new on-line method for learning maps of dynamic worlds. For this purpose the Prune-Able fuzzy ART neural architecture (PAFARTNA) is introduced. It extends the FARTNA self-organizing neural network to include the ability to selectively perform the following additional operation on recognition categories: remove, directly update spatial span, or forced create. A method is proposed for the perception of object removals, and then integrated with PAFARTNA. Experimental results obtained with a Nomad 200 robot are presented demonstrating the feasibility and effectiveness of the proposed methods.
The paper discusses compliant motion control using Active Observers (AOBs) applied in robotic manipulators. Stochastic estimation strategies for haptic manipulation are introduced. Stability and robustness analysis is...
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The paper discusses compliant motion control using Active Observers (AOBs) applied in robotic manipulators. Stochastic estimation strategies for haptic manipulation are introduced. Stability and robustness analysis is made as a function of stiffness mismatches. Real time adaptation is discussed.
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