Comparison of Different Cue based Swarm Aggregation Strategies

2014-10-19
Farshad, Arvin
Turgut, Ali Emre
Nicola, Belletto
Yue, Shigang
In this paper, we compare different aggregation strategies for cue-based aggregation with a mobile robot swarm. We used a sound source as the cue in the environment and performed real robot and simulation based experiments. We compared the performance of two proposed aggregation algorithms we called as the vector averaging and naive with the state-of-the-art cue-based aggregation strategy BEECLUST. We showed that the proposed strategies outperform BEECLUST method. We also illustrated the feasibility of the method in the presence of noise. The results showed that the vector averaging algorithm is more robust to noise when compared to the naive method.
5th International Conference on Swarm Intelligence (October 17-20, 2014)

Suggestions

Optimization of physical parameters of an underactuated quadrupedal robot
Karagoz, Osman Kaan; Ankaralı, Mustafa Mert (2018-01-01)
In this paper, we present the comparison of different optimization algorithms that are used to optimize the parameters of a simulated legged robotic platform. We compare the results obtained by applying different algorithms on the same model and show the relative advantages and disadvantages of these algorithms. The tested algorithms are Particle Swarm Optimization, Binary Coded Genetic Algorithm, Broyden-Fletcher-Goldfrab-Shannon Algorithm and Method of Zoutendijk. We showed that the globally optimal param...
A systematic study of probabilistic aggregation strategies in swarm robotic systems
Soysal, Onur; Şahin, Erol; Department of Computer Engineering (2005)
In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performa...
DEVELOPMENT OF A SOCIAL REINFORCEMENT LEARNING BASED AGGREGATION METHOD WITH A MOBILE ROBOT SWARM
Gür, Emre; Turgut, Ali Emre; Şahin, Erol; Department of Mechanical Engineering (2022-9-09)
In this thesis, the development of a social, reinforcement learning-based aggregation method is covered together with the development of a mobile robot swarm of Kobot- Tracked (Kobot-T) robots. The proposed method is developed to improve efficiency in low robot density swarm environments especially when the aggregated area is difficult to find. The method is called Social Reinforcement Learning, and Landmark-Based Aggregation (SRLA) and it is based on Q learning. In this method, robots share their Q tables ...
On Equivalence Relationships Between Classification and Ranking Algorithms
Ertekin Bolelli, Şeyda (2011-10-01)
We demonstrate that there are machine learning algorithms that can achieve success for two separate tasks simultaneously, namely the tasks of classification and bipartite ranking. This means that advantages gained from solving one task can be carried over to the other task, such as the ability to obtain conditional density estimates, and an order-of-magnitude reduction in computational time for training the algorithm. It also means that some algorithms are robust to the choice of evaluation metric used; the...
Comparison of Facial Alignment Techniques: With Test Results on Gender Classification Task
Kaya, Tunç Güven (2014-08-24)
In this paper, different facial alignment techniques are revised in terms of their effects on machine learning algorithms. This paper, investigates techniques that are widely accepted in literature and measures their effect on gender classification task. There is no special reason on selecting gender classification task, any other task could have been chosen. In audience measurement systems, many important demographics, i.e. gender, age, facial expression, can be measured by using machine learning algorithm...
Citation Formats
A. Farshad, A. E. Turgut, B. Nicola, and S. Yue, “Comparison of Different Cue based Swarm Aggregation Strategies,” Hefei, China, 2014, p. 1, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/71992.