A Bayesian framework for optimal motion planning with uncertainty

Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a separate implementation of control, localization and planning. In the end, we reduce the stochastic control problem to path- planning in the extended space of poses x covariances; the transitions between states are modeled through the use of the Fisher information matrix. In this framework, we consider two problems: minimizing the execution time, and minimizing the final covariance, with an upper bound on the execution time. Two correct and complete algorithms are presented. The first is the direct extension of classical graph-search algorithms in the extended space. The second one is a back-projection algorithm: uncertainty constraints are propagated backward from the goal towards the start state

Integrating mapping and navigation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1998.Includes bibliographical references (p. 221-232).by Christopher Michael Smith.Ph.D

Indoor Navigation with Uncertainty using Sensor-Based Motions

: This paper presents an operational framework to bridge the gap between planning with uncertainty and real-time sensor-based motion control. The environment being known, a planner produces a plan composed of free space and sensor-based motion commands. The representations of uncertainty and its evolution, environment landmarks, and actions generated at the planning level are discussed. Sensor-based actions and command definitions for a nonholonomic mobile robot based on a Task-Potential field approach are developed. These various elements are integrated in a system that actually generates the motions of the Hilare2 mobile robot. 1 Introduction Motion planning and control are two important aspects required for mobile robot autonomous navigation. However, they are frequently considered as two separate phases. Many motion planning techniques have been proposed (see [7] for a survey) to produce a collision-free and feasible path avoiding the obstacles of the workspace. Motion control th..

Nanorobotics Control Design: A Practical Approach Tutorial

The authors present a new approach using genetic algorithms, neural networks and nanorobotics concepts applied to the problem of control design for nanoassembly automation and its application in medicine. As a practical approach to validate the proposed design, we have elaborated and simulated a virtual environment focused on control automation for nanorobotics teams that exhibit collective behavior. This collective behavior is a suitable way to perform a large range of tasks and positional assembly manipulation in a complex 3D workspace. We emphasize the application of such techniques as a feasible approach for the investigation of nanorobotics system design in nanomedicine. 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