Mobile robot navigation

About: Mobile robot navigation is a research topic. Over the lifetime, 14713 publications have been published within this topic receiving 263092 citations.

Environment learning using a distributed representation

Abstract: A method for robust mobile robot navigation and environmental learning is presented. It was implemented and tested on a physical robot. The method consists of a collection of simple, incrementally designed robot behaviors. The behaviors receive sonar and compass data which they use to dynamically detect landmarks and construct a distributed map of the environment. The map is represented as a graph in which each node is a collection of augmented finite state machines functioning in parallel. The distributed nature of the map allows for localization in constant time. The method utilizes a modified spreading of activation scheme to accomplish robust linear-time path planning. It is capable of generating both topologically and physically shortest paths to the goal. The method uses local information to achieve the global task without having to replan if the robot becomes lost or strays off the desired path. >

Method, apparatus and system for use in navigation

Abstract: The present embodiments provide methods, processes and systems for use in determining navigation. Some embodiments provide methods that receive a communication at a mobile navigation system from an external device, determine whether the communication comprises navigation information, extract the navigation information when the communication comprises navigation information, identify a navigation location based on the navigation information, retrieve a current location, and generate navigation instructions between the current location and the navigation location.

Visual detection of lintel-occluded doors from a single image

Abstract: Doors are important landmarks for indoor mobile robot navigation. Most existing algorithms for door detection use range sensors or work in limited environments because of restricted assumptions about color, pose, or lighting. We present a vision-based door detection algorithm that achieves robustness by utilizing a variety of features, including color, texture, and intensity edges. We introduce two novel geometric features that increase performance significantly: concavity and bottom-edge intensity profile. The features are combined using Adaboost to ensure optimal linear weighting. On a large database of images collected in a wide variety of conditions, the algorithm achieves more than 90% detection with a low false positive rate. Additional experiments demonstrate the suitability of the algorithm for real-time applications using a mobile robot equipped with an off-the-shelf camera and laptop.

Hybrid Simultaneous Localization and Map Building: Closing the Loop with Multi-Hypotheses Tracking

Abstract: In this paper simultaneous localization and map building is performed with a hybrid, metric - topological, approach. A global topological map connects local metric maps, allowing a compact environment model, which does not require global metric consistency and permits both precision and robustness. However, the most important innovation of the approach is the way how loops in the environment are handled by map building using the information of the multi hypotheses topological localization. The method uses data from a 360° laser scanner to extract corners and openings for the topological approach and infinite lines for the metric method. This hybrid approach has been tested in a 50 x 25 m2 portion of the institute building with a fully autonomous robot. The performances of the whole system are proven empirically by comparing maps generated by independent explorations, testing the localization capabilities, making relocation experiments and showing how the technique for closing the loop works.

Method and system for controlling a mobile robot

Abstract: A method and a system for operating a mobile robot comprise a range finder for collecting range data of one or more objects in an environment around the robot. A discriminator identifies uniquely identifiable ones of the objects as navigation landmarks. A data storage device stores a reference map of the navigation landmarks based on the collected range data. A data processor establishes a list or sequence of way points for the robot to visit. Each way point is defined with reference to one or more landmarks. A reader reads an optical message at or near one or more way points. A task manager manages a task based on the read optical message.