Cooperative Obstacle-Avoidance Pushing Transportation of a Planar Object with One Leader and Two Follower Mobile Robots

2005 ◽  
Vol 17 (1) ◽  
pp. 77-88 ◽  
Author(s):  
Yanqun Le ◽  
◽  
Hiroyuki Kojima ◽  
Kazuhiko Matsuda ◽  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Trajectory Planning ◽  
Communication Systems ◽  
Control Method ◽  
Recognition Algorithm ◽  
Control Phase ◽  
Planning Phase ◽  
Passive Compliance

This paper proposes a cooperative obstacle-avoidance pushing transportation system using one leader and two follower mobile robots. Its usefulness and effectiveness are illustrated and confirmed numerically as well as experimentally. The cooperative obstacle-avoidance pushing transportation control consists of the obstacle configuration measurement phase by the leader mobile robot, the trajectory-planning phase and the pushing transfer control phase by the two follower mobile robots. In the obstacle configuration measurement phase, the leader mobile robot moves by use of the obstacle-avoidance vehicle control method constructed with six infrared sensors and the pattern recognition algorithm, and three waypoints for the trajectory planning of the follower mobile robots are extracted. In the trajectory-planning phase, the two follower mobile robots receive the three modified waypoints from the leader mobile robot through wireless communication systems, and the obstacle-avoidance trajectories by use of cubic spiral and straight-line segments are generated. Then, in the pushing transfer control phase, a planar object is transported with the pushing and constraining forces resulting from the passive compliance mechanisms attached to the follower mobile robots, and the shock is effectively reduced by the passive compliance mechanisms. From the numerical simulation and experimental results using autonomous mobile robots (MK-01X developed by Fuji Heavy Industries Ltd.), it is confirmed that the planar object can be successfully transported by pushing from the start configuration to the goal in spite of the existence of the obstacle.

Obstacle Avoidance Control Method of Mobile Robot Motion

2013 ◽  
Vol 443 ◽  
pp. 119-122
Author(s):  
Bin Zhou ◽  
Jin Fa Qian
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Control Method ◽  
Intelligent System ◽  
Development Trend ◽  
Working Environment ◽  
Intelligent Algorithm ◽  
Control Field ◽  
Avoidance Control ◽  
The Development Trend

Mobile robot is an intelligent system which can move freely and is scheduled to complete the task in the working environment. Obstacle avoidance of mobile robot is the research hotspot in the control field of the mobile robot. The mobile robot obstacle avoidance methods are classified, including the traditional algorithms and the intelligent algorithms. This paper summarizes the intelligent algorithm in the mobile robot obstacle avoidance technique in the present situation, and the intelligent algorithm which is the most researched in the current. Finally, this paper prospects the development trend of intelligent obstacle avoidance of the robot.

scholarly journals Modeling and Analysis of the Obstacle-Avoidance Strategies for a Mobile Robot in a Dynamic Environment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Rui Wang ◽  
Ming Wang ◽  
Yong Guan ◽  
Xiaojuan Li
Keyword(s):  
Energy Consumption ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Decision Process ◽  
Dynamic Environment ◽  
Formal Models ◽  
Modeling And Analysis ◽  
Avoidance Strategies ◽  
Markov Decision

Obstacle avoidance is a key performance of mobile robots. However, its experimental verification is rather difficult, due to the probabilistic behaviors of both the robots and the obstacles. This paper presents the Markov Decision Process based probabilistic formal models for three obstacle-avoidance strategies of a mobile robot in an uncertain dynamic environment. The models are employed to make analyses in PRISM, and the correctness of the analysis results is verified by MATLAB simulations. Finally, the minimum time and the energy consumption are determined by further analyses in PRISM, which prove to be useful in finding the optimal strategy. The present work provides a foundation for the probabilistic formal verification of more complicated obstacle-avoidance strategies.

scholarly journals First results in the development of a mobile robot with trajectory planning and object recognition capabilities

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Talgat Islamgozhayev ◽  
Maksat Kalimoldayev ◽  
Arman Eleusinov ◽  
Shokan Mazhitov ◽  
Orken Mamyrbayev
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Educational Research ◽  
Trajectory Planning ◽  
Template Matching ◽  
Processing Unit ◽  
First Results ◽  
Recognition Systems

Abstract The use of mobile robots is becoming popular in many areas of service because they ensure safety and good performance while working in dangerous or unreachable locations. Areas of application of mobile robots differ from educational research to detection of bombs and their disposal. Based on the mission of the robot they have different configurations and abilities – some of them have additional arms, cranes and other tools, others use sensors and built-in image processing and object recognition systems to perform their missions. The robot that is described in this paper is mobile robot with a turret mounted on top of it. Different approaches have been tested while searching for best method suitable for image processing and template matching goals. Based on the information from image processing unit the system executes appropriate actions for planning motions and trajectory of the mobile robot.

Neural Control System for Autonomous Vehicles

2011 ◽  
pp. 1197-1204
Author(s):  
Francisco García-Córdova ◽  
Antonio Guerrero-González ◽  
Fulgencio Marín-García
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Neural Control ◽  
Learning Rule ◽  
Nonholonomic Mobile Robots ◽  
Neural Architecture ◽  
Neuro Controller

Neural networks have been used in a number of robotic applications (Das & Kar, 2006; Fierro & Lewis, 1998), including both manipulators and mobile robots. A typical approach is to use neural networks for nonlinear system modelling, including for instance the learning of forward and inverse models of a plant, noise cancellation, and other forms of nonlinear control (Fierro & Lewis, 1998). An alternative approach is to solve a particular problem by designing a specialized neural network architecture and/or learning rule (Sutton & Barto, 1981). It is clear that biological brains, though exhibiting a certain degree of homogeneity, rely on many specialized circuits designed to solve particular problems. We are interested in understanding how animals are able to solve complex problems such as learning to navigate in an unknown environment, with the aim of applying what is learned of biology to the control of robots (Chang & Gaudiano, 1998; Martínez-Marín, 2007; Montes-González, Santos-Reyes & Ríos- Figueroa, 2006). In particular, this article presents a neural architecture that makes possible the integration of a kinematical adaptive neuro-controller for trajectory tracking and an obstacle avoidance adaptive neuro-controller for nonholonomic mobile robots. The kinematical adaptive neuro-controller is a real-time, unsupervised neural network that learns to control a nonholonomic mobile robot in a nonstationary environment, which is termed Self-Organization Direction Mapping Network (SODMN), and combines associative learning and Vector Associative Map (VAM) learning to generate transformations between spatial and velocity coordinates (García-Córdova, Guerrero-González & García-Marín, 2007). The transformations are learned in an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The obstacle avoidance adaptive neurocontroller is a neural network that learns to control avoidance behaviours in a mobile robot based on a form of animal learning known as operant conditioning. Learning, which requires no supervision, takes place as the robot moves around a cluttered environment with obstacles. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot’s sensors. The efficacy of the proposed neural architecture is tested experimentally by a differentially driven mobile robot.

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