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.

Autonomous multi-platform robot system

Abstract: An autonomous mobile robot system allocates mapping, localization, planning and control functions to at least one navigator robot and allocates task performance functions to one or more functional robots. The at least one navigator robot maps the work environment, localizes itself and the functional robots within the map, plans the tasks to be performed by the at least one functional robot, and controls and tracks the at least one functional robot during task performance. The at least one navigator robot performs substantially all calculations for mapping, localization, planning and control for both itself and the functional robots. In one implementation, the at least one navigator robot remains stationary while controlling and moving the at least one functional robot in order to simplify localization calculations. In one embodiment, the at least one navigator robot is equipped with sensors and sensor processing hardware required for these tasks, while the at least one functional robot is not equipped with sensors or hardware employed for these purposes.

Experiences with a mobile robotic guide for the elderly

Abstract: This paper describes an implemented robot system, which relies heavily on probabilistic AI techniques for acting under uncertainty. The robot Pearl and its predecessor Flo have been developed by a multi-disciplinary team of researchers over the past three years. The goal of this research is to investigate the feasibility of assisting elderly people with cognitive and physical activity limitations through interactive robotic devices, thereby improving their quality of life. The robot's task involves escorting people in an assisted living facility-a time-consuming task currently carried out by nurses. Its software architecture employs probabilistic techniques at virtually all levels of perception and decision making. During the course of experiments conducted in an assisted living facility, the robot successfully demonstrated that it could autonomously provide guidance for elderly residents. While previous experiments with fielded robot systems have provided evidence that probabilistic techniques work well in the context of navigation, we found the same to be true of human robot interaction with elderly people.

A sensor-based navigation for a mobile robot using fuzzy logic and reinforcement learning

Abstract: The proposed navigator consists of an avoidance behavior and goal-seeking behavior. Two behaviors are independently designed at the design stage and then combined them by a behavior selector at the running stage. A behavior selector using a bistable switching function chooses a behavior at each action step so that the mobile robot can go for the goal position without colliding with obstacles. Fuzzy logic maps the input fuzzy sets representing the mobile robot's state space determined by sensor readings to the output fuzzy sets representing the mobile robot's action space. Fuzzy rule bases are built through the reinforcement learning which requires simple evaluation data rather than thousands of input-output training data. Since the fuzzy rules for each behavior are learned through a reinforcement learning method, the fuzzy rule bases can be easily constructed for more complex environments. In order to find the mobile robot's present state, ultrasonic sensors mounted at the mobile robot are used. The effectiveness of the proposed method is verified by a series of simulations. >

Visual navigation and obstacle avoidance structured light system

Abstract: A vision system for a vehicle, such as a mobile robot (10) includes at least one radiation projector (14, 16) which projects a structured beam of radiation into the robot's environment. The structured beam of radiation (14a, 16a) preferably has a substantially planar pattern of sufficient width to encompass the immediate forward path of the robot and also to encompass laterally disposed areas in order to permit turning adjustments. The vision system further includes an imaging (12) sensor such as a CCD imaging device having a two-dimensional field of view which encompasses the immediate forward path of the robot. An image sensor processor (18) includes an image memory (18A) coupled to a device (18D) which is operable for accessing the image memory. Image processing is accomplished in part by triangulating the stored image of the structured beam pattern to derive range and bearing, relative to the robot, of an object being illuminated. A navigation control system (20) of the robot inputs data from at least the vision system and infers therefrom data relating to the configuration of the environment which lies in front of the robot. The navigation control system generates control signals which drive propulsion and steering motors in order to navigate the robot through the perceived environment.