Topic
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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TL;DR: This survey concentrates on heuristic-based algorithms in robot path planning which are comprised of neural network, fuzzy logic, nature inspired algorithms and hybrid algorithms.
450 citations
TL;DR: It has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches and are used to improve the performance of the classical approaches as a hybrid algorithm.
450 citations
01 Jan 1991
TL;DR: HIMM has been implemented and tested on a mobile robot and its dual functionality was demonstrated through numerous tests in which maps of unknown obstacle courses were created, while the robot simultaneously performed real-time obstacle avoidance maneuvers at speeds of up to 0.78 m/s.
Abstract: Histogramic in-motion mapping (HIMM) is introduced as a new method for real-time map building with a mobile robot motion. HIMM represents data in a two-dimensional array, called a histogram grid, that is updated through rapid in-motion sampling of on-board range sensors. Rapid in-motion sampling results in a map representation that is well-suited to modeling inaccurate and noisy range-sensor data, such as those produced by ultrasonic sensors, and requires minimal computational overhead. Fast map building allows the robot to use immediately the mapped information in real-time obstacle-avoidance algorithms. The benefits of this integrated approach are quick, accurate mapping and safe navigation of the robot toward a given target. HIMM has been implemented and tested on a mobile robot. Its dual functionality was demonstrated through numerous tests in which maps of unknown obstacle courses were created, while the robot simultaneously performed real-time obstacle avoidance maneuvers at speeds of up to 0.78 m/s. >
449 citations
28 Oct 1993
TL;DR: The interaction of an autonomous mobile robot with the real world critically depends on the robots morphology and on its environment Building a model of these aspects is extremely complex, making simulation insufficient for accurate validation of control algorithms as mentioned in this paper.
Abstract: The interaction of an autonomous mobile robot with the real world critically depends on the robots morphology and on its environment Building a model of these aspects is extremely complex, making simulation insufficient for accurate validation of control algorithms
446 citations
TL;DR: A technique for learning collections of trajectories that characterize typical motion patterns of persons and how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process of a mobile robot is proposed.
Abstract: Whenever people move through their environments they do not move randomly. Instead, they usually follow specific trajectories or motion patterns corresponding to their intentions. Knowledge about such patterns enables a mobile robot to robustly keep track of persons in its environment and to improve its behavior. In this paper we propose a technique for learning collections of trajectories that characterize typical motion patterns of persons. Data recorded with laser-range finders are clustered using the expectation maximization algorithm. Based on the result of the clustering process, we derive a hidden Markov model that is applied to estimate the current and future positions of persons based on sensory input. We also describe how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process of a mobile robot. We present several experiments carried out in different environments with a mobile robot equipped with a laser-range scanner and a camera system. The results demonstrate that our approach can reliably learn motion patterns of persons, can robustly estimate and predict positions of persons, and can be used to improve the navigation behavior of a mobile robot.
430 citations