Image-based motion planning for two mobile robots in an environment with obstacles by using cellular neural networks

2011 ◽  
Vol 2 (1) ◽  
pp. 45-50
Author(s):  
L. Ţepelea ◽  
I. Gavriluţ ◽  
A. Gacsádi ◽  
V. Tiponuţ
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Parallel Computing ◽  
Motion Planning ◽  
Mobile Robots ◽  
Cellular Neural Networks ◽  
Autonomous Mobile Robots ◽  
Planning Algorithm ◽  
Images Processing ◽  
Path Planning Algorithm

Abstract The paper presents an image-based algorithm for motion-planning of two mobile robots moving to the same target in an environment with obstacles. Due to parallel computing, the Cellular Neural Networks (CNN) techniques ensure the images processing in real-time and represent an advantageous solution for autonomous mobile robots guidance. The path planning algorithm can be improved increasing the speed of image processing, using advanced type of the CNN implementation and it can be extended for three or more robots.

scholarly journals Development and Implementation of Spline-based Path Planning Algorithm in ROS/Gazebo Environment

2019 ◽  
Vol 18 (1) ◽  
pp. 57-84 ◽  
Author(s):  
Lavrenov Lavrenov ◽  
Evgeni Magid ◽  
Matsuno Fumitoshi ◽  
Mikhail Svinin ◽  
Jackrit Suthakorn
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Cost Function ◽  
Working Environment ◽  
Autonomous Mobile Robots ◽  
Dynamic Tuning ◽  
Starting Point ◽  
Geometric Point ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Path planning for autonomous mobile robots is an important task within robotics field. It is common to use one of the two classical approaches in path planning: a global approach when an entire map of a working environment is available for a robot or local methods, which require the robot to detect obstacles with a variety of onboard sensors as the robot traverses the environment. In our previous work, a multi-criteria spline algorithm prototype for a global path construction was developed and tested in Matlab environment. The algorithm used the Voronoi graph for computing an initial path that serves as a starting point of the iterative method. This approach allowed finding a path in all map configurations whenever the path existed. During the iterative search, a cost function with a number of different criteria and associated weights was guiding further path optimization. A potential field method was used to implement some of the criteria. This paper describes an implementation of a modified spline-based algorithm that could be used with real autonomous mobile robots. Equations of the characteristic criteria of a path optimality were further modified. The obstacle map was previously presented as intersections of a finite number of circles with various radii. However, in real world environments, obstacles’ data is a dynamically changing probability map that could be based on an occupancy grid. Moreover, the robot is no longer a geometric point. To implement the spline algorithm and further use it with real robots, the source code of the Matlab environment prototype was transferred into C++ programming language. The testing of the method and the multi criteria cost function optimality was carried out in ROS/Gazebo environment, which recently has become a standard for programming and modeling robotic devices and algorithms. The resulting spline-based path planning algorithm could be used on any real robot, which is equipped with a laser rangefinder. The algorithm operates in real time and the influence of the objective function criteria parameters are available for dynamic tuning during a robot motion.

scholarly journals A new method on motion planning for mobile robots using jump point search and Bezier curves

2021 ◽  
Vol 18 (4) ◽  
pp. 172988142110192
Author(s):  
Ben Zhang ◽  
Denglin Zhu
Keyword(s):  
Motion Planning ◽  
Initial Point ◽  
Target Point ◽  
Jump Point ◽  
Bezier Curves ◽  
Bézier Curves ◽  
Front End ◽  
Planning Algorithm ◽  
Point Search ◽  
Path Planning Algorithm

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on motion planning systems that meet the shortest path and obstacle avoidance requirements. This article proposes a novel path planning algorithm based on jump point search and Bezier curves. The proposed algorithm consists of two main steps. In the front end, the improved heuristic function based on distance and direction is used to reduce the cost, and the redundant turning points are trimmed. In the back end, a novel trajectory generation method based on Bezier curves and a straight line is proposed. Our experimental results indicate that the proposed algorithm provides a complete motion planning solution from the front end to the back end, which can realize an optimal trajectory from the initial point to the target point used for robot navigation.

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