Motion Planning for a Chain of Mobile Robots Using A* and Potential Field

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

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The WL_PCR: A Planning for Ground-to-Pole Transition of Wheeled-Legged Pole-Climbing Robots

Robotics ◽

10.3390/robotics10030096 ◽

2021 ◽

Vol 10 (3) ◽

pp. 96

Author(s):

Yankai Wang ◽

Qiaoling Du ◽

Tianhe Zhang ◽

Chengze Xue

Keyword(s):

Motion Planning ◽

Mobile Robots ◽

Dynamic Process ◽

Mathematical Expression ◽

Truss Structure ◽

Centre Of Mass ◽

Climbing Robots ◽

Wheeled Vehicle ◽

Motion Modes

Hybrid mobile robots with two motion modes of a wheeled vehicle and truss structure with the ability to climb poles have significant flexibility. The motion planning of this kind of robot on a pole has been widely studied, but few studies have focused on the transition of the robot from the ground to the pole. In this study, a locomotion strategy of wheeled-legged pole-climbing robots (the WL_PCR) is proposed to solve the problem of ground-to-pole transition. By analyzing the force of static and dynamic process in the ground-to-pole transition, the condition of torque provided by the gripper and moving joint is proposed. The mathematical expression of Centre of Mass (CoM) of the wheeled-legged pole-climbing robots is utilized, and the conditions for the robot to smoothly transition from the ground to the vertical pole are proposed. Finally, the feasibility of this method is proved by the simulation and experimentation of a locomotion strategy on wheeled-legged pole-climbing robots.

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Passively safe partial motion planning for mobile robots with limited field-of-views in unknown dynamic environments

2014 IEEE International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra.2014.6907375 ◽

2014 ◽

Cited By ~ 6

Author(s):

S. Bouraine ◽

Th. Fraichard ◽

O. Azouaoui ◽

Hassen Salhi

Keyword(s):

Motion Planning ◽

Mobile Robots ◽

Dynamic Environments ◽

Passively Safe

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