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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.


Papers
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Journal ArticleDOI
TL;DR: A new fuzzy logic algorithm is developed for mobile robot navigation in local environments that resolves the problem of limit cycles in any type of dead-ends encountered on the way to the target.

87 citations

Patent
13 May 2011
TL;DR: In this article, a system and method for robustly calibrating a vision system and a robot is presented, which enables a plurality of cameras to be calibrated into a robot base coordinate system to enable a machine vision/robot control system to accurately identify the location of objects of interest within robot base coordinates.
Abstract: A system and method for robustly calibrating a vision system and a robot is provided. The system and method enables a plurality of cameras to be calibrated into a robot base coordinate system to enable a machine vision/robot control system to accurately identify the location of objects of interest within robot base coordinates.

87 citations

Proceedings ArticleDOI
12 Oct 2005
TL;DR: This paper intends to develop and experiment various task planners and interaction schemes, that will allow the robot to select and perform its tasks while taking into account explicitly the constraints imposed by the presence of humans, their needs and preferences.
Abstract: Human-robot interaction requires explicit reasoning on the human environment and on the robot capacities to achieve its tasks in a collaborative way with a human partner.This paper focuses on organization of the robot decisional abilities and more particularly on the management of human interaction as an integral part of the robot control architecture. Such an architecture should be the framework that will allow the robot to accomplish its tasks but also produce behaviors that support its engagement vis-a-vis its human partner and interpret similar behaviors from him.Together and in coherence with this framework, we intend to develop and experiment various task planners and interaction schemes, that will allow the robot to select and perform its tasks while taking into account explicitly the constraints imposed by the presence of humans, their needs and preferences.We have considered a scheme where the robot plans for itself and for the human in order not only (1) to assess the feasibility of the task (at a certain level) before performing it, but also (2) to share the load between the robot and the human and (3) to explain/illustrate a possible course of action.

87 citations

Proceedings ArticleDOI
01 Oct 2016
TL;DR: The approach presented in this paper is based on a feature-based maximum entropy model and is able to guide a robot in an unstructured, real-world environment and is trained to predict joint behavior of heterogeneous groups of agents from onboard data of a mobile platform.
Abstract: This paper reports on a data-driven motion planning approach for interaction-aware, socially-compliant robot navigation among human agents. Autonomous mobile robots navigating in workspaces shared with human agents require motion planning techniques providing seamless integration and smooth navigation in such. Smooth integration in mixed scenarios calls for two abilities of the robot: predicting actions of others and acting predictably for them. The former requirement requests trainable models of agent behaviors in order to accurately forecast their actions in the future, taking into account their reaction on the robot's decisions. A human-like navigation style of the robot facilitates other agents—most likely not aware of the underlying planning technique applied—to predict the robot motion vice versa, resulting in smoother joint navigation. The approach presented in this paper is based on a feature-based maximum entropy model and is able to guide a robot in an unstructured, real-world environment. The model is trained to predict joint behavior of heterogeneous groups of agents from onboard data of a mobile platform. We evaluate the benefit of interaction-aware motion planning in a realistic public setting with a total distance traveled of over 4 km. Interestingly the motion models learned from human-human interaction did not hold for robot-human interaction, due to the high attention and interest of pedestrians in testing basic braking functionality of the robot.

87 citations

Journal ArticleDOI
TL;DR: It is experimentally verified that a robot safely navigates in dynamic indoor environment by adopting the proposed scheme, which clearly indicates the structural procedure on how to model and to exploit the risk of navigation.
Abstract: We present one approach to achieve safe navigation in an indoor dynamic environment. So far, there have been various useful collision avoidance algorithms and path planning schemes. However, those algorithms possess fundamental limitations in that the robot can avoid only ldquovisiblerdquo ones among surrounded obstacles. In a real environment, it is not possible to detect all the dynamic obstacles around the robot. There are many occluded regions due to the limited field of view. In order to avoid collisions, it is desirable to exploit visibility information. This paper proposes a safe navigation scheme to reduce collision risk considering occluded dynamic obstacles. The robot's motion is controlled by the hybrid control scheme. The possibility of collision is dually reflected to path planning and speed control. The proposed scheme clearly indicates the structural procedure on how to model and to exploit the risk of navigation. The proposed scheme is experimentally tested in a real office building. The experimental results show that the robot moves along the safe path to obtain sufficient field of view. In addition, safe speed constraints are applied in motion control. It is experimentally verified that a robot safely navigates in dynamic indoor environment by adopting the proposed scheme.

87 citations


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