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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.


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Proceedings ArticleDOI
06 Jul 2004
TL;DR: The multi-sensor camera + navigation framework is shown to have compelling advantages over a camera-only based approach by improving the robustness of pairwise image registration, setting the free gauge scale, and allowing for a unconnected camera graph topology.
Abstract: This work describes a framework for sensor fusion of navigation data with camera-based 5 DOF relative pose measurements for 6 DOF vehicle motion in an unstructured 3D underwater environment. The fundamental goal of this work is to concurrently estimate online current vehicle position and its past trajectory. This goal is framed within the context of improving mobile robot navigation to support sub-sea science and exploration. Vehicle trajectory is represented by a history of poses in an augmented state Kalman filter. Camera spatial constraints from overlapping imagery provide partial observation of these poses and are used to enforce consistency and provide a mechanism for loop-closure. The multi-sensor camera + navigation framework is shown to have compelling advantages over a camera-only based approach by: 1) improving the robustness of pairwise image registration, 2) setting the free gauge scale, and 3) allowing for a unconnected camera graph topology. Results are shown for a real world data set collected by an autonomous underwater vehicle in an unstructured undersea environment.

93 citations

Proceedings ArticleDOI
18 Apr 2005
TL;DR: The method uses a novel combination of a 3D occupancy grid for robust sensor data interpretation and a 2.5D height map for fine resolution floor values for humanoid robot QRIO to generate detailed maps for autonomous navigation.
Abstract: With the development of biped robots, systems became able to navigate in a 3 dimensional world, walking up and down stairs, or climbing over small obstacles. We present a method for obtaining a labeled 2.5D grid map of the robot's surroundings. Each cell is marked either as floor or obstacle and contains a value telling the height of the floor or obstacle. Such height maps are useful for path planning and collision avoidance. The method uses a novel combination of a 3D occupancy grid for robust sensor data interpretation and a 2.5D height map for fine resolution floor values. We evaluate our approach using stereo vision on the humanoid robot QRIO and show the advantages over previous methods. Experimental results from navigation runs on an obstacle course demonstrate the ability of the method to generate detailed maps for autonomous navigation.

93 citations

Book ChapterDOI
01 Jan 2010
TL;DR: A technique for mobile robot model predictive control that utilizes the structure of a regionalmotion plan to effectively search the local continuum for an improved solution to solve the problem of path following and obstacle avoidance through geometric singularities and discontinuities.
Abstract: As mobile robots venture into more difficult environments, more complex state-space paths are required to move safely and efficiently. The difference between mission success and failure can be determined by a mobile robots capacity to effectively navigate such paths in the presence of disturbances. This paper describes a technique for mobile robot model predictive control that utilizes the structure of a regionalmotion plan to effectively search the local continuum for an improved solution. The contribution, a receding horizon model-predictive control (RHMPC) technique, specifically addresses the problem of path following and obstacle avoidance through geometric singularities and discontinuities such as cusps, turn-in-place, and multi-point turn maneuvers in environments where terrain shape and vehicle mobility effects are non-negligible. The technique is formulated as an optimal controller that utilizes a model-predictive trajectory generator to relax parameterized control inputs initialized from a regional motion planner to navigate safely through the environment. Experimental results are presented for a six-wheeled skid-steered field robot in natural terrain.

93 citations

Journal ArticleDOI
TL;DR: This paper presents a unique and elegant architecture of an indoor navigation system as an integrated and tested system in real time that enables the localization of the user, as well as the navigation considering scenarios that the user might follow and suggests guidelines for self- navigation.

93 citations

Proceedings ArticleDOI
01 Feb 2000
TL;DR: A probabilistic model of human motion is proposed which is based on motion probability grids generated from observed motion and integrated with an autonomous mobile robot system, with a laser range sensor detecting humans moving within the environment, and a path planning system.
Abstract: In order to effectively plan paths in environments inhabited by humans, robots must accurately predict human motion. Typical approaches to human prediction simply assume a constant velocity which is not always valid. This paper proposes to determine the likely navigation intent of humans and use that to predict human motion. Navigation intent is determined by the function and structure of the environment. Manually assigned functional places are combined with automatically extracted navigation way-points to define a number of likely navigation targets within the environment. To predict human motion toward these targets, a probabilistic model of human motion is proposed which is based on motion probability grids generated from observed motion. The models of human navigation intent and motion are integrated with an autonomous mobile robot system, with a laser range sensor detecting humans moving within the environment, and a path planning system. The models of human navigation intent and motion are verified using real captured human motion data from an office environment. Examples of human motion prediction are also presented.

92 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202358
2022179
202194
2020125
2019146
2018129