Complete Coverage Path Planning and Obstacle Avoidance Strategy of the Robot

2013 ◽  
Vol 756-759 ◽  
pp. 497-503 ◽  
Author(s):  
Jun Hui Wu ◽  
Tong Di Qin ◽  
Jie Chen ◽  
Hui Ping Si ◽  
Kai Yan Lin ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
High Efficiency ◽  
Control Flow ◽  
Single Chip ◽  
Single Chip Microcomputer ◽  
Complete Coverage ◽  
Coverage Path Planning ◽  
Avoidance Strategy ◽  
Traversal Algorithm

In order to solve the problems of complete coverage path and obstacle avoidance with the mobile robot, the complete coverage planning was described first, and then the algorithm of the complete coverage path planning was analyzed. The complete traversal algorithm and the obstacle avoidance strategy of the robot around the barrier were put forward. Finally, the traversal control flow chart of the traversal robot implemented in Single Chip Microcomputer (SCM) was obtained. After the above analysis, the algorithm was simple, practical, and low repeatability, and high efficiency. The algorithms could effectively solve the difficulty of complete coverage path and obstacle avoidance with the robot.

scholarly journals Complete Coverage Path Planning in an Unknown Underwater Environment Based on D-S Data Fusion Real-Time Map Building

2012 ◽  
Vol 8 (10) ◽  
pp. 567959 ◽  
Author(s):  
Mingzhong Yan ◽  
Daqi Zhu ◽  
Simon X. Yang
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Evidence Theory ◽  
Sensor Data ◽  
Map Building ◽  
Complete Coverage ◽  
Coverage Path Planning ◽  
Underwater Environment ◽  
Time Map

A real-time map-building system is proposed for an autonomous underwater vehicle (AUV) to build a map of an unknown underwater environment. The system, using the AUV's onboard sensor information, includes a neurodynamics model proposed for complete coverage path planning and an evidence theoretic method proposed for map building. The complete coverage of the environment guarantees that the AUV can acquire adequate environment information. The evidence theory is used to handle the noise and uncertainty of the sensor data. The AUV dynamically plans its path with obstacle avoidance through the landscape of neural activity. Concurrently, real-time sensor data are “fused” into a two-dimensional (2D) occupancy grid map of the environment using evidence inference rule based on the Dempster-Shafer theory. Simulation results show a good quality of map-building capabilities and path-planning behaviors of the AUV.

An Algorithm of Complete Coverage Path Planning for Autonomous Underwater Vehicles

2011 ◽  
Vol 467-469 ◽  
pp. 1377-1385 ◽  
Author(s):  
Ming Zhong Yan ◽  
Da Qi Zhu
Keyword(s):  
Path Planning ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Time Varying ◽  
Simulation Studies ◽  
Complete Coverage ◽  
Biologically Inspired ◽  
Coverage Path Planning ◽  
Underwater Environment ◽  
Global Cost

Complete coverage path planning (CCPP) is an essential issue for Autonomous Underwater Vehicles’ (AUV) tasks, such as submarine search operations and complete coverage ocean explorations. A CCPP approach based on biologically inspired neural network is proposed for AUVs in the context of completely unknown environment. The AUV path is autonomously planned without any prior knowledge of the time-varying workspace, without explicitly optimizing any global cost functions, and without any learning procedures. The simulation studies show that the proposed approaches are capable of planning more reasonable collision-free complete coverage paths in unknown underwater environment.

A Novel Full-Automatic Garbage Salvage Ship

2013 ◽  
Vol 791-793 ◽  
pp. 1921-1924
Author(s):  
Ke Ling Luo ◽  
Xu Dong Li ◽  
De Rui Song ◽  
Ping Wang ◽  
Jiao Fu ◽  
...  
Keyword(s):  
Control System ◽  
Obstacle Avoidance ◽  
Single Chip ◽  
Single Chip Microcomputer ◽  
Power Circuit ◽  
The Core ◽  
Photoelectric Sensor

The garbage salvage ship is made from polystyrene plastic as hull framework and STC89C52 single-chip microcomputer to control the core, and consists of DC gear motor, photoelectric sensor, power circuit and other circuits. The system uses STC89C52 to control the boat to move forward, backward or turn through the I/O port. Tracing is finished by infrared obstacle avoidance sensor E18-D80NK. Working manner of the infrared obstacle avoidance sensor E18-D80NK was introduced and its application scheme based on STC89C52 single-chip microcomputer in the control system of garbage salvage ship was put forward.

scholarly journals Obstacle Avoidace Robot Using LabView

2015 ◽  
Vol 4 (3) ◽  
pp. 164
Author(s):  
Tasher Ali Sheikh ◽  
Swacheta Dutta ◽  
Smriti Baruah ◽  
Pooja Sharma ◽  
Sahadev Roy
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Autonomous Robotics ◽  
Continuous Transition ◽  
Heavy Burden ◽  
Avoidance Strategy ◽  
Small Period ◽  
Specific Amount

The concept of path planning and collision avoidance are two of the most common theories applied for designing and developing in advanced autonomous robotics applications. NI LabView makes it possible to implement real-time processor for obstacle avoidance. The obstacle avoidance strategy ensures that the robot whenever senses the obstacle stops without being collided and moves freely when path is free, but sometimes there exists a probability that once the path is found free and the robot starts moving, then within a fraction of milliseconds, the robot again sense the obstacle and it stops. This continuous swing of stop and run within a very small period of time may cause heavy burden on the system leading to malfunctioning of the components of the system. This paper deals with overcoming this drawback in a way that even after the robot calculates the path is free then also it will wait for a specific amount of time before running it. So as to confirm that if again the sensor detects the obstacle within that specified period then robot don’t need to transit its state suddenly thus avoiding continuous transition of run and stop. Thus it reduces the heavy burden on the system.

Sign in / Sign up

Export Citation Format

Share Document