Sensor information space for robust mobile robot path planning

Robotica ◽  
2000 ◽  
Vol 18 (4) ◽  
pp. 415-421 ◽  
Author(s):  
A. Pruski ◽  
A. Atassi
Keyword(s):  
Path Planning ◽  
Scanning System ◽  
Robot Path Planning ◽  
Continuous Laser ◽  
New Approach ◽  
Environment Model ◽  
Goal Region ◽  
Planning Algorithm ◽  
Environment Perception ◽  
Robot Path

This paper introduces a new approach to robust path planning for mobile robots entirely based on information from environment perception sensors. This method avoids the use of odometry which leads to the accumulation of errors resulting from the robot's position computing. We proceed as follows: we create regions inside which the robot detects the same obstacle segments. A node graph represents all the regions and their links. Then a planning algorithm is used to find a path which joins a start to a goal region. The final stage consists in applying a robust robot motion control as regards the uncertainties of the environment model. This approach contributes to a control system for indoor robots which is environment referenced. The sensors we deal with are first a continuous laser or ultrasonic scanning system, then a discrete ultrasonic belt whose limits of use we show.

A fast robot path planning algorithm based on bidirectional associative learning

2021 ◽  
Vol 155 ◽  
pp. 107173
Author(s):  
Meng Zhao ◽  
Hui Lu ◽  
Siyi Yang ◽  
Yinan Guo ◽  
Fengjuan Guo
Keyword(s):  
Path Planning ◽  
Associative Learning ◽  
Robot Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path

scholarly journals Multi-Robot Path Planning Method Using Reinforcement Learning

2019 ◽  
Vol 9 (15) ◽  
pp. 3057 ◽  
Author(s):  
Hyansu Bae ◽  
Gidong Kim ◽  
Jonguk Kim ◽  
Dianwei Qian ◽  
Sukgyu Lee
Keyword(s):  
Path Planning ◽  
Learning Algorithm ◽  
Robot Path Planning ◽  
Q Learning ◽  
Efficient Performance ◽  
Planning Algorithm ◽  
Neural Network Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path ◽  
Multi Robot

This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a given environment. Each robot in the multi-robot system is inherently required to navigate independently with collaborating with other robots for efficient performance. In addition, the robot collaboration scheme is highly depends on the conditions of each robot, such as its position and velocity. However, the conventional method does not actively cope with variable situations since each robot has difficulty to recognize the moving robot around it as an obstacle or a cooperative robot. To compensate for these shortcomings, we apply Deep q learning to strengthen the learning algorithm combined with CNN algorithm, which is needed to analyze the situation efficiently. CNN analyzes the exact situation using image information on its environment and the robot navigates based on the situation analyzed through Deep q learning. The simulation results using the proposed algorithm shows the flexible and efficient movement of the robots comparing with conventional methods under various environments.

scholarly journals Robot Path Planning in Uncertain Environments: A Language-Measure-Theoretic Approach

2014 ◽  
Vol 137 (3) ◽  
Author(s):  
Devesh K. Jha ◽  
Yue Li ◽  
Thomas A. Wettergren ◽  
Asok Ray
Keyword(s):  
Path Planning ◽  
Autonomous Underwater Vehicles ◽  
Infinite Horizon ◽  
Theoretic Approach ◽  
Robot Path Planning ◽  
Test Bed ◽  
Finite State ◽  
Planning Algorithm ◽  
Language Measure ◽  
Robot Path

This paper addresses the problem of goal-directed robot path planning in the presence of uncertainties that are induced by bounded environmental disturbances and actuation errors. The offline infinite-horizon optimal plan is locally updated by online finite-horizon adaptive replanning upon observation of unexpected events (e.g., detection of unanticipated obstacles). The underlying theory is developed as an extension of a grid-based path planning algorithm, called ν⋆, which was formulated in the framework of probabilistic finite state automata (PFSA) and language measure from a control-theoretic perspective. The proposed concept has been validated on a simulation test bed that is constructed upon a model of typical autonomous underwater vehicles (AUVs) in the presence of uncertainties.

New Approach for Mobile Robot Path Planning

2013 ◽  
Vol 467 ◽  
pp. 475-478
Author(s):  
Feng Yun Lin
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Dc Motor ◽  
Robot Path Planning ◽  
New Approach ◽  
Kinematic Constraints ◽  
Optimal Path Planning ◽  
Kinematic Limit ◽  
Time Optimal ◽  
Robot Path

This paper presents a method of time optimal path planning under kinematic, limit heat characteristics of DC motor and dynamic constrain for a 2-DOF wheeled. Firstly the shortest path is planned by using the geometric method under kinematic constraints. Then, in order to make full use of motors capacity we have the torque limits under limit heat characteristics of DC motor, finally the velocity limit and the boundary acceleration (deceleration) are determined to generate a time optimal path.

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