Experimental study on autonomous mobile robot acquiring optimal action to avoid moving obstacles

1997 ◽  
Vol 1 (4) ◽  
pp. 205-210
Author(s):  
Takeshi Aoki ◽  
Toshiaki Oka ◽  
Soichiro Hayakawa ◽  
Tatsuya Suzuki ◽  
Shigeru Okuma
Keyword(s):  
Experimental Study ◽  
Mobile Robot ◽  
Autonomous Mobile Robot ◽  
Moving Obstacles ◽  
Optimal Action

scholarly journals Differential Steering Control for an Autonomous Mobile Robot: A Preliminary Experimental Study

2012 ◽  
Vol 41 ◽  
pp. 189-195 ◽  
Author(s):  
M. Nor Rudzuan ◽  
D. Hazry ◽  
W.A.N. Khairunizam ◽  
A.B. Shahriman ◽  
M. Saifizi ◽  
...  
Keyword(s):  
Experimental Study ◽  
Mobile Robot ◽  
Autonomous Mobile Robot ◽  
Steering Control ◽  
Differential Steering

615 Moving Obstacles Avoidance of Autonomous Mobile Robot Considering Load Swing

2001 ◽  
Vol 2001 (0) ◽  
pp. 219-220
Author(s):  
Nobuhito SHIRAKAMI ◽  
Masafumi HAMAGUCHI ◽  
Gen ONISHI ◽  
Hideki YAMADA
Keyword(s):  
Mobile Robot ◽  
Autonomous Mobile Robot ◽  
Moving Obstacles ◽  
Load Swing

scholarly journals OperativeCriticalPointBug algorithm-local path planning of mobile robot avoiding obstacles

2020 ◽  
Vol 18 (3) ◽  
pp. 1646
Author(s):  
Subir Kumar Das ◽  
Ajoy Kumar Dutta ◽  
Subir Kumar Debnath
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Autonomous Mobile Robot ◽  
Moving Obstacles ◽  
Local Path Planning ◽  
Local Environments ◽  
Run Time ◽  
Local Path

<span>For Autonomous Mobile Robot one of the biggest and interesting issues is path planning. An autonomous mobile robot should be able determine its own path to reach destination. This paper offers a new algorithm for mobile robot to plan a path in local environments with stationary as well as moving obstacles. For movable robots’ path planning OperativeCriticalPointBug (OCPB) algorithm, is a new Bug algorithm. This algorithm is carried out by the robot throughout the movement from source to goal, hence allowing the robot to rectify its way if a new obstacle comes into the route or any existing obstacle changes its route. According as, not only the robot tries to avoid clash with other obstacle but also tries a series of run time adjustment in its way to produce roughly a best possible path. During journey the robot is believed to be capable to act in an unknown location by acquiring information perceived locally. Using this algorithm the robot can avoid obstacle by considering its own as well as the obstacle’s dimension. The obstacle may be static or dynamic. The algorithm belongs to bug family.</span>

scholarly journals A Method of Piloting an Autonomous Mobile Robot in Dynamically Changing Environment Including Moving Obstacles

1996 ◽  
Vol 8 (1) ◽  
pp. 81-92
Author(s):  
Shigeki Ishikawa ◽  
◽  
Shun’ichi Asaka
Keyword(s):  
Mobile Robot ◽  
Sensory Processing ◽  
Sensory Information ◽  
Range Data ◽  
Autonomous Mobile Robot ◽  
Steering Angle ◽  
Changing Environment ◽  
Moving Obstacles ◽  
Locomotion System ◽  
Navigation Method

In this paper, we present a sensor-based navigation method for an autonomous mobile robot in a dynamically changing environment with stationary and moving obstacles. A navigation task needs to navigate a robot to a destination along a predefined path, and to avoid obstacles, if necessary. However, the environment is always changing, and sensory information is quite limited (actually, we have many limitations on sensory processing in connection with a dynamically changing environment). We have to describe an efficient navigation manner onto the robot using simple sensory information. The first step in our approach was to use fuzzy control. Based on results of experiments, we propose a new navigation method for an autonomous mobile robot in a dynamically changing environment. The method determines a steering angle and a velocity to be sent to a low-level locomotion system. Two major parts are involved in this operation: one is the task of tracing a planned path; and the other is the task of avoiding obstacles. The distribution weight between their outputs is determined with a range data and differential range data between samplings.

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