Coordination of Modular Robots by Means of Topology Discovery and Leader Election: Improvement of the Locomotion Case

Springer Tracts in Advanced Robotics - Distributed Autonomous Robotic Systems ◽

10.1007/978-4-431-55879-8_31 ◽

2016 ◽

pp. 447-458 ◽

Cited By ~ 1

Author(s):

José Baca ◽

Bradley Woosley ◽

Prithviraj Dasgupta ◽

Ayan Dutta ◽

Carl Nelson

Keyword(s):

Leader Election ◽

Modular Robots ◽

Topology Discovery

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An Enhanced Protocol for Bluetooth Scatternet Formation and Routing

International Journal of Advanced Pervasive and Ubiquitous Computing ◽

10.4018/ijapuc.2014040102 ◽

2014 ◽

Vol 6 (2) ◽

pp. 14-35

Author(s):

Anandita Sarkar ◽

Minu Shit ◽

Chandreyee Chowdhury ◽

Sarmistha Neogy

Keyword(s):

Routing Protocol ◽

Leader Election ◽

Route Discovery ◽

Topology Discovery ◽

Route Selection ◽

Node Mobility ◽

Hop Count ◽

Bluetooth Scatternet ◽

Simulation Results ◽

Scatternet Formation

Formation of scatternet using Bluetooth devices increases device tractability thereby inviting new networking applications to be designed on it. In this paper we propose Bluetooth Scatternet Formation and Routing Protocol (BSFRP). It is a distributed protocol that handles node mobility and enables multi-hop communication. BSFRP defines rules for topology discovery, scatternet formation and routing. The scatternet phase of the protocol works on the principle of leader election. For routing, AODV is modified to address the constraints of scatternets. It improves the AODV route discovery phase by considering hop count, residual node's power, and route lifetime for best route selection. Simulation results show that the scatternet formed by BSFRP has the following properties: the number of piconets formed is close to the universal lower bound, each device on an average does not assume more than 1.15 roles, and the scatternet does not contain any master-slave bridge.

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Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽

10.3390/sym13030471 ◽

2021 ◽

Vol 13 (3) ◽

pp. 471

Author(s):

Jai Hoon Park ◽

Kang Hoon Lee

Keyword(s):

Genetic Algorithm ◽

Reinforcement Learning ◽

Design Space ◽

Learning Algorithm ◽

Computational Design ◽

Computational Method ◽

Learning Ability ◽

Modular Robots ◽

Control Mechanisms ◽

Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

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