Path planning of mobile robots using potential fields and swarms of Brownian particles

2011 ◽  
Author(s):  
Helbert Eduardo Espitia ◽  
Jorge Ivan Sofrony
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Potential Fields ◽  
Brownian Particles

Application of path planning based on Artificial Potential Fields to mobile robots with distributed processing

2018 ◽  
Author(s):  
Kamilla Pereira Peixoto ◽  
Pedro Henrique Silva Coutinho ◽  
Allan Cavalcante Araújo ◽  
Berguem Paula Santos Almeida ◽  
Thiago Chagas
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Distributed Processing ◽  
Potential Fields ◽  
Artificial Potential Fields

Hybrid Methodology for Path Planning and Computational Vision Applied to Autonomous Mission: A New Approach

Robotica ◽  
2019 ◽  
Vol 38 (6) ◽  
pp. 1000-1018
Author(s):  
Fabrício O. Coelho ◽  
Milena F. Pinto ◽  
João Pedro C. Souza ◽  
André L. M. Marcato
Keyword(s):  
Experimental Design ◽  
Path Planning ◽  
Mobile Robots ◽  
Daily Life ◽  
The Elderly ◽  
Potential Fields ◽  
New Approach ◽  
Artificial Potential Fields ◽  
Extensive Evaluation ◽  
Hybrid Methodology

SUMMARYIn recent years, mobile robots have become increasingly frequent in daily life applications, such as cleaning, surveillance, support for the elderly and people with disabilities, as well as hazardous activities. However, a big challenge arises when the robotic system must perform a fully autonomous mission. The main problems of autonomous missions include path planning, localisation, and mapping. Thus, this research proposes a hybrid methodology for mobile robots on an autonomous mission involving an offline approach that uses the Direct-DRRT* algorithm and the artificial potential fields algorithm as the online planner. The experimental design covers three scenarios with an increasing degree of accuracy in respect of the real world. Additionally, an extensive evaluation of the proposed methodology is reported.

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
Sign in / Sign up

Export Citation Format

Share Document