Development of Sensor-Based Navigation for Mobile Robots Using Target Direction Sensor

1999 ◽  
Vol 11 (1) ◽  
pp. 39-44 ◽  
Author(s):  
Motoji Yamamoto ◽  
◽  
Nobuhiro Ushimi ◽  
Akira Mohri
Keyword(s):  
Measurement Error ◽  
Mobile Robot ◽  
Motion Planning ◽  
Mobile Robots ◽  
Navigation System ◽  
Location Information ◽  
Work Space ◽  
Target Direction ◽  
Navigation Algorithm ◽  
Direction Information

Sensor-based navigation used a target direction sensor for mobile robots among unknown obstacles in work space is discussed. The advantage of target direction information is robustness of measurement error for online navigation, compared to robot location information. Convergence of navigation using target direction information is discussed. An actual sensor system using two CdS sensors to measure target direction is proposed. Using target direction information, we present a new sensor based navigation algorithm in unknown obstacle environment. The navigation algorithm is based on target direction information, unlike sensor-based motion planning algorithms based on mobile robot location information. Using a sensor-based navigation system, we conducted a navigation experiment and simulations in unknown obstacle environment.

scholarly journals The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

2020 ◽  
Vol 69 ◽  
pp. 471-500
Author(s):  
Shih-Yun Lo ◽  
Shiqi Zhang ◽  
Peter Stone
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Mobile Robots ◽  
Large Scale ◽  
Accurate Estimate ◽  
Indoor Environments ◽  
Task Planning ◽  
Trade Offs ◽  
High Level ◽  
Task Level

Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.

scholarly journals A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5409
Author(s):  
Gonzalo Farias ◽  
Ernesto Fabregas ◽  
Enrique Torres ◽  
Gaëtan Bricas ◽  
Sebastián Dormido-Canto ◽  
...  
Keyword(s):  
Object Recognition ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Navigation System ◽  
Autonomous System ◽  
Traffic Signals ◽  
Vision Based Navigation ◽  
Vision Algorithm ◽  
Distributed Vision ◽  
Robot Platform

This work presents the development and implementation of a distributed navigation system based on object recognition algorithms. The main goal is to introduce advanced algorithms for image processing and artificial intelligence techniques for teaching control of mobile robots. The autonomous system consists of a wheeled mobile robot with an integrated color camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that performs a computer vision algorithm to recognize the objects. The computer calculates the corresponding speeds of the robot according to the object detected. The speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. Three different algorithms have been tested in simulation and a practical mobile robot laboratory. The results show an average of 84% success rate for object recognition in experiments with the real mobile robot platform.

scholarly journals A topological navigation system for indoor environments based on perception events

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141667813 ◽  
Author(s):  
Clara Gomez ◽  
Alejandra Carolina Hernandez ◽  
Jonathan Crespo ◽  
Ramon Barber
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Navigation System ◽  
Indoor Environment ◽  
Mobile Systems ◽  
Indoor Environments ◽  
Human Navigation ◽  
Multiple Events ◽  
Topological Navigation ◽  
Detection And Recognition

The aim of the work presented in this article is to develop a navigation system that allows a mobile robot to move autonomously in an indoor environment using perceptions of multiple events. A topological navigation system based on events that imitates human navigation using sensorimotor abilities and sensorial events is presented. The increasing interest in building autonomous mobile systems makes the detection and recognition of perceptions a crucial task. The system proposed can be considered a perceptive navigation system as the navigation process is based on perception and recognition of natural and artificial landmarks, among others. The innovation of this work resides in the use of an integration interface to handle multiple events concurrently, leading to a more complete and advanced navigation system. The developed architecture enhances the integration of new elements due to its modularity and the decoupling between modules. Finally, experiments have been carried out in several mobile robots, and their results show the feasibility of the navigation system proposed and the effectiveness of the sensorial data integration managed as events.

Two-level artificial intelligent system of mobile robot motion planning

2014 ◽  
Vol 10 ◽  
pp. 50-54
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Mobile Robots ◽  
Control Systems ◽  
Intelligent System ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Vision Systems ◽  
Artificial Intelligent ◽  
Upper Level

We consider a two-level intelligent system for planning the movements of mobile robots, in which the search for the trajectory is carried out on two levels — a rough and precise planning subsystems. Insufficient resolution of vision systems at the upper level is compensated by sensor systems placed on board robots. The proposed approach reduces the resources required on-board control systems (are based on computer or controller) and optimization of traffic routes of all members of the group to achieve group goals.

AN INTEGERATED NAVIGATION SYSTEM FOR AUTONOMOUS MOBILE ROBOT IN DYNAMIC ENVIROMENTS

2020 ◽  
pp. 32-46
Author(s):  
Nguyen Lan Anh
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Navigation System ◽  
Tracking System ◽  
Dynamic Environment ◽  
Planning System ◽  
Autonomous Mobile Robot ◽  
Integrated Navigation ◽  
Elastic Band ◽  
Integrated Navigation System

To enable an autonomous mobile robot to navigate safely in a dy- namic environment, the mobile robot must address four typical functional blocks of the navigation system including perception, localization, motion planning, and motor control. In this study, we present an integrated navigation system for the autonomous mobile robot in the dynamic environment by incorporating the techniques proposed in our previous studies, including object detection and tracking system, localization system and motion planning system, into a completed navigation system. In addition, we propose an extended timed elastic band (ETEB) technique for online trajectory planning, which allows the mobile robot to proactively avoid obstacles in the surrounding environment. We validate the effectiveness of the proposed model through a series of experiments in both simulated and real-world environments. The experimental results show that our proposed motion model is capable of driving the mobile robots to proactively avoid dynamic obstacles, providing safe navigation for the robots.

scholarly journals Navigation Based on Vision and DGPS Information for Mobile Robots

1999 ◽  
Vol 11 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Shinji Kotani ◽  
◽  
Ken’ichi Kaneko ◽  
Tatsuya Shinoda ◽  
Hideo Mori ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Navigation System ◽  
Vision System ◽  
Dead Reckoning ◽  
Initial Position ◽  
Map Matching ◽  
Autonomous Mobile Robot ◽  
Environmental Model ◽  
Position And Orientation

This paper describes a navigation system for an autonomous mobile robot in outdoors. The robot uses vision to detect landmarks and DGPS information to determine its initial position and orientation. The vision system detects landmarks in the environment by referring to an environmental model. As the robot moves, it calculates its position by conventional dead reckoning, and matches landmarks to the environmental model to reduce error in position calculation. The robot's initial position and orientation are calculated from coordinates of the first and second locations acquired by DGPS. Subsequent orientations and positions are derived by map matching. We implemented the system on a mobile robot, Harunobu 6. Experiments in real environments verified the effectiveness of our proposed navigation.

Special Issue on Modern Trends in Mobile Robotics

2002 ◽  
Vol 14 (4) ◽  
pp. 323-323
Author(s):  
Takashi Tsubouchi ◽  
◽  
Keiji Nagatani ◽  
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Mobile Robots ◽  
High Speed ◽  
Mobile Robotics ◽  
Low Cost ◽  
Electronic Devices ◽  
Mobile Robot Navigation ◽  
Map Building ◽  
Special Issue

Since the dawning of the Robotics age, mobile robots have been important objectives of research and development. Working from such aspects as locomotion mechanisms, path and motion planning algorithms, navigation, map building and localization, and system architecture, researchers are working long and hard. Despite the fact that mobile robotics has a shorter history than conventional mechanical engineering, it has already accumulated a major, innovative, and rich body of R&D work. Rapid progress in modern scientific technology had advanced to where down-sized low-cost electronic devices, especially highperformance computers, can now be built into such mobile robots. Recent trends in ever higher performance and increased downsizing have enabled those working in the field of mobile robotics to make their models increasingly intelligent, versatile, and dexterous. The down-sized computer systems implemented in mobile robots must provide high-speed calculation for complicated motion planning, real-time image processing in image recognition, and sufficient memory for storing the huge amounts of data required for environment mapping. Given the swift progress in electronic devices, new trends are now emerging in mobile robotics. This special issue on ""Modern Trends in Mobile Robotics"" provides a diverse collection of distinguished papers on modern mobile robotics research. In the area of locomotion mechanisms, Huang et al. provide an informative paper on control of a 6-legged walking robot and Fujiwara et al. contribute progressive work on the development of a practical omnidirectional cart. Given the importance of vision systems enabling robots to survey their environments, Doi et al., Tang et al., and Shimizu present papers on cutting-edge vision-based navigation. On the crucial subject of how to equip robots with intelligence, Hashimoto et al. present the latest on sensor fault detection in dead-reckoning, Miura et al. detail the probabilistic modeling of obstacle motion during mobile robot navigation, Hada et al. treat long-term mobile robot activity, and Lee et al. explore mobile robot control in intelligent space. As guest editors, we are sure readers will find these articles both informative and interesting concerning current issues and new perspectives in modern trends in mobile robotics.

Simulation of Accurate Navigation Technology for Aircraft Landing Approach

2013 ◽  
Vol 329 ◽  
pp. 406-410
Author(s):  
Ang Tai Li ◽  
Xiao Jiao Ma
Keyword(s):  
Information Fusion ◽  
Navigation System ◽  
Location Information ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
System Models ◽  
Navigation Algorithm ◽  
Aircraft Landing ◽  
Simulation Results

The purpose of this paper is to find a solid application and improve the integrated navigation system of aircraft independent landing. The main researches are focused on the issue how to improve the accuracy and reliability of the integrated navigation system, by means of the detailed analyzing about information fusion, and investigating accurate navigation technology for independent landing approach of aircraft. Next, the integrated navigation system models SINS/DGPS/TAN/ILS based on Federated filter are established, and the accurate navigation algorithm of landing is also researched. At the end, the paper gives its simulation results for independent lading approach, which show this algorithm is able to greatly improve the accuracy of speed and location information for aircraft accurate landing.

scholarly journals Motion Planning of Multiple Mobile Robots Using Virtual Impedance

1996 ◽  
Vol 8 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Tamio Arai ◽  
◽  
Jun Ota
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Mobile Robots ◽  
Lower Layer ◽  
Local Equilibrium ◽  
Current Position ◽  
Cooperative Motion ◽  
Robot Systems ◽  
Simulation Results ◽  
Virtual Impedance

This paper proposes a planning method for multiple mobile robot systems. It has two characteristics: (1) Each robot plans a path on its own, without any supervisor; (2) The concept of cooperative motion can be implemented. A two-layered hierarchy is defined for a scheme of individual path planning. The higher layer generates a trajectory from the current position to a goal. The lower layer called“Virtual Impedance Metho” makes a real-time plan to follow the generated trajectory while avoiding obstacles and avoiding or cooperating with other robots. This layer is composed of four modules called, “watchdog”, “deadlock solver”,“blockade solver”, and “pilot”. The local equilibrium is detected by the watchdog and cancelled by the deadlock solver or the blockade solver. Simulation results indicate the effectiveness of the proposed method.

scholarly journals A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots

2020 ◽  
Author(s):  
Gonzalo Farias ◽  
Ernesto Fabregas ◽  
Enrique Torres ◽  
Gaetan Bricas ◽  
Sebastián Dormido-Canto ◽  
...  
Keyword(s):  
Computer Vision ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Navigation System ◽  
Autonomous System ◽  
Traffic Signals ◽  
Training Stage ◽  
Negative Images ◽  
Vision Based Navigation ◽  
Distributed Vision

This work presents the development and implementation of a distributed navigation system based on computer vision. The autonomous system consists of a wheeled mobile robot with an integrated colour camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that processes them and calculates the corresponding speeds of the robot using a cascade of trained classifiers. These speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. The classifier cascade should be trained before experimentation with two sets of positive and negative images. The number of images in these sets should be considered to limit the training stage time and avoid overtraining the system.

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