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.

Method and system for providing related navigation features for two or more end users

Abstract: A feature provided by a navigation system or a navigation services provider provides navigation-related services for plural users who have related needs. In one embodiment, a first user specifies a type of place at which to meet a second user who is at a location some distance away from the first user. The navigation feature identifies one or more places of the specified type that are convenient for both users to travel to. The navigation feature may also provide the users with instructions for traveling to a selected place.

Activity classification and dead reckoning for pedestrian navigation with wearable sensors

Abstract: This paper addresses an approach which integrates activity classification and dead reckoning techniques in step-based pedestrian navigation. In the proposed method, the pedestrian is equipped with a prototype wearable sensor module to record accelerations and determine the headings while walking. To improve the step detection accuracy, different types of activities are classified according to extracted features by means of a probabilistic neural network (PNN). The vertical acceleration data, which indicate the periodic vibration during gait cycle are filtered through a wavelet transform before being used to count the steps and assess the step length from which the distance traveled is estimated. By coupling the distance with the azimuth, navigation through pedestrian dead reckoning is implemented. This research provides a possible seamless pedestrian navigation solution which can be applied to a wide range of areas where the global navigation satellite system (GNSS) signal remains vulnerable. Results of two experiments in this paper reveal that the proposed approach is effective in reducing navigation errors and improving accuracy.

Rapid, safe, and incremental learning of navigation strategies

Abstract: In this paper we propose a reinforcement connectionist learning architecture that allows an autonomous robot to acquire efficient navigation strategies in a few trials. Besides rapid learning, the architecture has three further appealing features. First, the robot improves its performance incrementally as it interacts with an initially unknown environment, and it ends up learning to avoid collisions even in those situations in which its sensors cannot detect the obstacles. This is a definite advantage over nonlearning reactive robots. Second, since it learns from basic reflexes, the robot is operational from the very beginning and the learning process is safe. Third, the robot exhibits high tolerance to noisy sensory data and good generalization abilities. All these features make this learning robot's architecture very well suited to real-world applications. We report experimental results obtained with a real mobile robot in an indoor environment that demonstrate the appropriateness of our approach to real autonomous robot control.