Swarm Robotics Obstacle Avoidance: A Progressive Minimal Criteria Novelty Search-Based Approach

2015 ◽  
pp. 493-498
Author(s):  
Nesma M. Rezk ◽  
Yousra Alkabani ◽  
Hassan Bedour ◽  
Sherif Hammad
Keyword(s):  
Obstacle Avoidance ◽  
Swarm Robotics ◽  
Novelty Search

scholarly journals Evolution of swarm robotics systems with novelty search

2013 ◽  
Vol 7 (2-3) ◽  
pp. 115-144 ◽  
Author(s):  
Jorge Gomes ◽  
Paulo Urbano ◽  
Anders Lyhne Christensen
Keyword(s):  
Swarm Robotics ◽  
Novelty Search

Introducing Novelty Search in Evolutionary Swarm Robotics

2012 ◽  
pp. 85-96 ◽  
Author(s):  
Jorge Gomes ◽  
Paulo Urbano ◽  
Anders Lyhne Christensen
Keyword(s):  
Swarm Robotics ◽  
Novelty Search ◽  
Evolutionary Swarm Robotics

scholarly journals On the Performance Analyses of a Modified Force Field Algorithm and Neural Network Approach for Obstacle Avoidance in Swarm Robotics

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Girish Balasubramanian ◽  
Senthil Arumugam Muthukumaraswamy ◽  
Xianwen Kong
Keyword(s):  
Neural Network ◽  
Force Field ◽  
Obstacle Avoidance ◽  
Swarm Robotics ◽  
Field Method ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Swarm Robots ◽  
Swarm Robot

AbstractObstacle avoidance is a major hurdle when implementing mobile robots and swarm robots. Swarm robots work in groups and therefore require an efficient and functional obstacle avoidance algorithm to stay collision free between themselves and their surroundings. This paper reviews previous research in obstacle avoidance implementation using the force field method (FFM), also known as potential field method (PFM) and a neutral network approach. Moreover, this paper aims to execute simulations using a modified force field algorithm and a neural network approach and compare them. The obtained results are analyzed to identify the performance characteristics and the time taken to perform tasks using a singular mobile robot against a swarm robot environment consisting of four and ten robots, respectively, in both simulation cases. Simulations showed that the algorithm was successful in navigating obstacles for both single and swarm robot environments. A single robot was found to take up to 340% longer to arrive at the required location compared to the first robot in the experiment. Moreover, it was found that the neural network approach showed ~ 27% improvement over the modified force field algorithm when it comes to cases where more than four robots are being used.

Autonomous Navigation and Obstacle Avoidance

2009 ◽  
Author(s):  
Jesse Berger ◽  
Cory Carson ◽  
Massood Towhidnejad ◽  
Richard Stansbury
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Navigation
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