Neuronal path planning and motion control of mobile robots

1988 ◽  
Vol 1 ◽  
pp. 460
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Motion Control

Ultrasonic sensor system for the uses in intelligent motion control system of a mobile robots group

2010 ◽  
Vol 7 ◽  
pp. 109-117
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov ◽  
B.S. Yudintsev
Keyword(s):  
Control System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Motion Control ◽  
High Speed ◽  
Ultrasonic Sensor ◽  
Sensor System ◽  
Motion Control System ◽  
Speed Sensor ◽  
Working Space

The article deals with the development of a high-speed sensor system for a mobile robot, used in conjunction with an intelligent method of planning trajectories in conditions of high dynamism of the working space.

scholarly journals Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4156
Author(s):  
Luís B. P. Nascimento ◽  
Dennis Barrios-Aranibar ◽  
Vitor G. Santos ◽  
Diego S. Pereira ◽  
William C. Ribeiro ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Configuration Space ◽  
Autonomous Robot ◽  
Breadth First Search ◽  
Propagation Process ◽  
Robot Systems ◽  
And Performance ◽  
Safe Path ◽  
High Clearance

The planning of safe paths is an important issue for autonomous robot systems. The Probabilistic Foam method (PFM) is a planner that guarantees safe paths bounded by a sequence of structures called bubbles that provides safe regions. This method performs the planning by covering the free configuration space with bubbles, an approach analogous to a breadth-first search. To improve the propagation process and keep the safety, we present three algorithms based on Probabilistic Foam: Goal-biased Probabilistic Foam (GBPF), Radius-biased Probabilistic Foam (RBPF), and Heuristic-guided Probabilistic Foam (HPF); the last two are proposed in this work. The variant GBPF is fast, HPF finds short paths, and RBPF finds high-clearance paths. Some simulations were performed using four different maps to analyze the behavior and performance of the methods. Besides, the safety was analyzed considering the new propagation strategies.

State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots

2013 ◽  
Vol 14 (3) ◽  
pp. 167-178 ◽  
Author(s):  
Xin Ma ◽  
Ya Xu ◽  
Guo-qiang Sun ◽  
Li-xia Deng ◽  
Yi-bin Li
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Autonomous Mobile Robots

Path planning for mobile robots based on visibility graphs and A* algorithm

2015 ◽  
Author(s):  
Juan D. Contreras ◽  
Fernando Martínez S. ◽  
Fredy H. Martínez S.
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
A Algorithm ◽  
Visibility Graphs
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