In this paper we consider the application of a naïve Bayes model for the evaluation of fraud risk connected with government agencies. This model applies probabilistic classifiers to support a generic risk assessment model, allowing for more efficient and effective use of resources for fraud detection in government transactions, and assisting audit agencies in transitioning from reactive to proactive fraud detection model.

This paper deals with the probabilistic modeling of space, in the context of mobile robot navigation. We define a formalism called the Bayesian map, which allows incremental building of models, thanks to the superposition operator, which is a formally well-defined operator. Firstly, we present a syntactic version of this operator, and secondly, a version where the previously obtained model is enriched by experimental learning. In the resulting map, locations are the conjunction of underlying possible locations, which allows for more precise localization and more complex tasks. A theoretical example validates the concept, and hints at its usefulness for realistic robotic scenarios.

This work deals with the domain of space modeling for mobile robotics. It offers a comparison of probabilistic and biomimetic models of navigation. Both approaches are shown to be quite complementary: while the probabilistic methods exploit sound theoretical grounds, they lack the modularity and, as a consequence, flexibility, of their biomimetic counterparts. We propose a new formalism, called the Bayesian Map formalism, that attempts to bridge the gap between the two domains: it is based on Bayesian modeling and inference for defining the building blocks, and uses operators for building hierarchies of models.

This paper presents a new method for probabilistic modeling of space, called the Bayesian Map formalism. It offers a generalization of some common approaches found in the literature, as it does not constrain the dependency structure of the probabilistic model. The formalism allows incremental building of hierarchies of models, by the use of the Abstraction operator. In the resulting hierarchy, localization in the high level model is based on probabilistic competition of the lower level models. Experimental results validate the concept, and hint at its usefulness for large scale scenarios.

A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced first to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge.

Evolve safely in an unchanged environment and possibly following an optimal trajectory is one big challenge presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a solution based on a probabilistic approach called Bayesian Programming. This approach aims to deal with the uncertainty, imprecision and incompleteness of the information handled. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. Some videos illustrating these experiments can be found at http://www-laplace.imag.fr.

A prerequisite to the design of future advanced driver assistance systems for cars is a sensing system that provides all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It combines different sensors (laser, radar and video) and relies on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced initially to design robot control programs but its scope of application including uncertain or incomplete knowledge handling problems.

We present a Bayesian CAD system for robotic applications. We address the problem of the propagation of geometric uncertainties, and how to take this propagation into account when solving inverse problems. We describe the methodology we use to represent and handle uncertainties using probability distributions of the system's parameters and sensor measurements. It may be seen as a generalization of constraint-based approaches where we express a constraint as a probability distribution instead of a simple equality or inequality. Appropriate numerical algorithms used to apply this methodology are also described. Using an example, we show how to apply our approach by providing simulation results using our CAD system.

This paper presents a new algorithm for computing the minimum distance between convex polyhedras. The algorithm of Gilbert-Johnson-Keerthi (GJK) and the algorithm of Lin-Canny (LC) are well-known fast solutions to the problem. We show how a mix between LC's idea and the GJK's algorithm can be used to solve the problem. In our algorithm, we use local methods to calculate the distance between features and new "updating" conditions to add stability. These new conditions enable one to ensure more stability when compared to GJK. We also modify our terminating conditions to add robustness to our approach. Our experiments also show that the expected running time of our approach is constant, independent of the complexity of the polyhedra. We present some comparisons of our method with GJK.

The repair of nuclear power plants often necessitates the development of special robots which should be operational readily. This requires a software architecture which quickly permits the design of a controller for an entire telerobotic system in a minimum period of time. We have developed such a software architecture to facilitate the design of applications dealing with nonstandard robot systems and complex industrial processes. In this paper we describe the requirements for this architecture and how we address them.

We present a new approach to path planning, called the ``Ariadne's clew algorithm''. It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments --- ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called SEARCH and EXPLORE, applied in an interleaved manner. EXPLORE builds a representation of the accessible space while SEARCH looks for the target. Both are posed as optimization problems. We describe a real implementation of the algorithm to plan paths for a six degrees of freedom arm in a dynamic environment where another six degrees of freedom arm is used as a moving obstacle. Experimental results show that a path is found in about one second without any pre-processing.

In this paper we propose an original modeling method. Our work can be considered as somewhere between classical modeling methods and learning based methods. We show that in the scope of a particular but quite general functional class it is possible to automatically choose the best equation form to represent a physical process, avoiding the hard work of analytical model definition that the designer should do. Our method uses an experimental protocol where the parameters identification is limited to one entry dimension problems, reducing the amount of data required by the model acquisition. The models generated by our method can be easily differentiated, corrected and reused. The method can be particularly useful in robotics where the functional form the method hands can be easily found in many kinds of problems.

A computational approach to direct and generalized inverse model acquisition is presented. The approach is based on a proposed method to direct model acquisition from partial information. The method decomposes a hyper-space function in one variable functions, simplifying the learning problem. The acquired direct model is then implemented in a tree-like structure that can be used in the inverse sense without additional learning effort. The authors' approach is able to acquire complete models in hyper-spaces requiring only selected data focused in one-dimension sub-spaces, strongly reducing the data acquisition effort. The authors' approach is particularly interesting for applications in robotics. The acquisition of direct models in robotics frequently takes place in high dimension phase spaces. When traditional approximation methods are used, enormous data bases, containing the examples to be interpolated, are required.

The goal of the work described is to build a path planner able to drive a robot in a dynamic environment where the obstacles are moving. In order to do so, the authors propose a method, called Ariadne's clew algorithm, to build a global path planner based on the combination of two local planning algorithms: an explore algorithm and a search algorithm. The purpose of the explore algorithm is to collect information about the environment with an increasingly fine resolution by placing landmarks in the searched space. The goal of the search algorithm is to opportunistically check if the target can be easily reached from any given placed landmark. The Ariadne's clew algorithm is shown to be very fast is most cases, allowing planning in dynamic environment. It is shown to be complete, which means that it is sure to find a path when one exists. A massively parallel implementation of this algorithm is described.

The authors address the problem of controlling a remote arm when a large communication delay exists between the control room and the remote workcell. To minimize the volume of information transiting through the communication link and to avoid the problems due to the delay of transmission, the system makes use of two identical world models of the remote workcell: one installed physically close to the operator and the other installed within the controller of the remote site. The communication link only transmits task level robot commands or goals to be achieved. Both sites are equipped with two sets of identical planners, one set is used to test if the generated task level commands can be processed by these planners and the other to plan and execute the actual commands on the remote workcell. After an overview of existing teleprogramming environments, the authors present a general description of the system. Finally they describe the experimental testbed and the results obtained with their method.

The authors describe ACT, a system designed to plan and monitor robot motions using a geometric representation of the environment. The goal in developing ACT was to design a system which could be used in industrial applications as well as a software basis to develop new robot programming techniques in the academic environment. ACT is intended to provide the user with advanced programming tools such as inline path planning and graphic monitoring of robot tasks. The project included developments not directly related to robotics but which were essential to enhance these new programming techniques. For example, a special user interface was developed to specify the robot tasks as well as to interface with existing CAD modelers. The authors present the technical choices made in developing the system and the most technically innovative parts of the system.>

This paper deals with the problem of planning safe trajectories for a manipulator robot moving in an environment including both static obstacles and objects (other robots, conveyors) whose positions may change during the interval of time separating two planning steps. In order to solve this problem for a general manipulator having six degrees of freedom, the authors have developed and implemented a method combining two different path planners: a local planner whose purpose is to take the manipulator away from the constrained initial and final configurations, and a global planner which is in charge of generating a safe trajectory for the first three degrees of freedom of the arm. Both the local and the global planners are based on a fast iterative algorithm for computing the distance between pairs of objects. These planners have been implemented within a robot programming system (called ACT) including a solid modeler dedicated to robotics applications and a connection to a robot controller.>

We describe a robot system capable of locating a part in an unstructured pile of objects, choose a grasp on the part, plan a motion to reach the part safely, and plan a motion to place the part at a commanded position. The system requires as input a polyhedral world model including models of the part to be manipulated, the robot arm, and any other fixed objects in the environment. In addition, the system builds a depth map, using structured light, of the area where the part is to be found initially. Any other objects present in that area do not have to be modeled.

Regrasping must be performed whenever a robot's grasp of an object is not compatible with the task it must perform. This paper presents an approach to the problem of regrasping for a robot arm equipped with parallel-jaw end-effector. The method employs a table surface to place the object in intermediate positions.