Mobile robot

About: Mobile robot is a research topic. Over the lifetime, 66744 publications have been published within this topic receiving 1100037 citations.

Using occupancy grids for mobile robot perception and navigation

Abstract: An approach to robot perception and world modeling that uses a probabilistic tesselated representation of spatial information called the occupancy grid is reviewed. The occupancy grid is a multidimensional random field that maintains stochastic estimates of the occupancy state of the cells in a spatial lattice. To construct a sensor-derived map of the robot's world, the cell state estimates are obtained by interpreting the incoming range readings using probabilistic sensor models. Bayesian estimation procedures allow the incremental updating of the occupancy grid, using readings taken from several sensors over multiple points of view. The use of occupancy grids from mapping and for navigation is examined. Operations on occupancy grids and extensions of the occupancy grid framework are briefly considered. >

Robotics: Modelling, Planning and Control

Abstract: The classic text on robot manipulators now covers visual control, motion planning and mobile robots too! Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control. The text develops around a core of consistent and rigorous formalism with fundamental and technological material giving rise naturally and with gradually increasing difficulty to more advanced considerations. The theory of manipulator structures presented in the early part of the book encompasses: the fundamentals: kinematics, statics and trajectory planning; and the technology of actuators, sensors and control units. Subsequently, more advanced instruction is given in: dynamics and motion control of robot manipulators; mobile robots; motion planning; and interaction with the environment using exteroceptive sensory data (force and vision). Appendices ensure that students will have access to a consistent level of background in basic areas such as rigid-body mechanics, feedback control, and others. Problems are raised and the proper tools established to find engineering-oriented solutions rather than to focus on abstruse theoretical methodology. To impart practical skill, more than 60 examples and case studies are carefully worked out and interwoven through the text, with frequent resort to simulation. In addition, nearly 150 end-of-chapter problems are proposed, and the book is accompanied by a solutions manual (downloadable from www.springer.com/978-1-84628-641-4) containing the MATLAB code for computer problems; this is available free of charge to those adopting Robotics as a textbook for courses. This text is suitable for use in senior undergraduate and graduate courses in automation and computer, electrical, electronic and mechanical engineering courses with strong robotics content.

Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters

Abstract: Recently, Rao-Blackwellized particle filters (RBPF) have been introduced as an effective means to solve the simultaneous localization and mapping problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper, we present adaptive techniques for reducing this number in a RBPF for learning grid maps. We propose an approach to compute an accurate proposal distribution, taking into account not only the movement of the robot, but also the most recent observation. This drastically decreases the uncertainty about the robot's pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out resampling operations, which seriously reduces the problem of particle depletion. Experimental results carried out with real mobile robots in large-scale indoor, as well as outdoor, environments illustrate the advantages of our methods over previous approaches

Exact robot navigation using artificial potential functions

Abstract: A methodology for exact robot motion planning and control that unifies the purely kinematic path planning problem with the lower level feedback controller design is presented. Complete information about a freespace and goal is encoded in the form of a special artificial potential function, called a navigation function, that connects the kinematic planning problem with the dynamic execution problem in a provably correct fashion. The navigation function automatically gives rise to a bounded-torque feedback controller for the robot's actuators that guarantees collision-free motion and convergence to the destination from almost all initial free configurations. A formula for navigation functions that guide a point-mass robot in a generalized sphere world is developed. The simplest member of this family is a space obtained by puncturing a disk by an arbitrary number of smaller disjoint disks representing obstacles. The other spaces are obtained from this model by a suitable coordinate transformation. Simulation results for planar scenarios are provided. >