scholarly journals Concurrent design of control software and configuration of hardware for robot swarms under economic constraints

2019 ◽  
Vol 5 ◽  
pp. e221 ◽  
Author(s):  
Muhammad Salman ◽  
Antoine Ligot ◽  
Mauro Birattari
Keyword(s):  
Automatic Design ◽  
Control Software ◽  
Experimental Conditions ◽  
Robot Swarm ◽  
Software Configuration ◽  
Robot Swarms ◽  
Battery Capacity ◽  
Finite State ◽  
The Individual ◽  
Economic Constraints

Designing a robot swarm is challenging due to its self-organized and distributed nature: complex relations exist between the behavior of the individual robots and the collective behavior that results from their interactions. In this paper, we study the concurrent automatic design of control software and the automatic configuration of the hardware of robot swarms. We introduce Waffle, a new instance of the AutoMoDe family of automatic design methods that produces control software in the form of a probabilistic finite state machine, configures the robot hardware, and selects the number of robots in the swarm. We test Waffle under economic constraints on the total monetary budget available and on the battery capacity of each individual robot comprised in the swarm. Experimental results obtained via realistic computer-based simulation on three collective missions indicate that different missions require different hardware and software configuration, and that Waffle is able to produce effective and meaningful solutions under all the experimental conditions considered.

scholarly journals Towards an integrated automatic design process for robot swarms

2021 ◽  
Vol 1 ◽  
pp. 112
Author(s):  
Darko Bozhinoski ◽  
Mauro Birattari
Keyword(s):  
Design Process ◽  
Design Method ◽  
Integrated Design ◽  
Automatic Design ◽  
Control Software ◽  
Fully Integrated ◽  
Robot Swarm ◽  
Robot Swarms ◽  
Integrated Design Process ◽  
Optimization Based Design

Background: The specification of missions to be accomplished by a robot swarm has been rarely discussed in the literature: designers do not follow any standardized processes or use any tool to precisely define a mission that must be accomplished. Methods: In this paper, we introduce a fully integrated design process that starts with the specification of a mission to be accomplished and terminates with the deployment of the robots in the target environment. We introduce Swarm Mission Language (SML), a textual language that allows swarm designers to specify missions. Using model-driven engineering techniques, we define a process that automatically transforms a mission specified in SML into a configuration setup for an optimization-based design method.  Upon completion, the output of the optimization-based design method is an instance of control software that is eventually deployed on real robots. Results: We demonstrate the fully integrated process we propose on three different missions. Conclusions: We aim to show that in order to create reliable, maintainable and verifiable robot swarms,  swarm designers need to follow standardised automatic design processes that will facilitate the design of control software in all stages of the development.

scholarly journals When less is more: Robot swarms adapt better to changes with constrained communication

2021 ◽  
Vol 6 (56) ◽  
pp. eabf1416
Author(s):  
Mohamed S. Talamali ◽  
Arindam Saha ◽  
James A. R. Marshall ◽  
Andreagiovanni Reina
Keyword(s):  
Information Exchange ◽  
Mean Field ◽  
Voter Model ◽  
Communication Range ◽  
Less Is More ◽  
Robot Swarm ◽  
Swarm Dynamics ◽  
Robot Swarms ◽  
Communication Links ◽  
The Individual

To effectively perform collective monitoring of dynamic environments, a robot swarm needs to adapt to changes by processing the latest information and discarding outdated beliefs. We show that in a swarm composed of robots relying on local sensing, adaptation is better achieved if the robots have a shorter rather than longer communication range. This result is in contrast with the widespread belief that more communication links always improve the information exchange on a network. We tasked robots with reaching agreement on the best option currently available in their operating environment. We propose a variety of behaviors composed of reactive rules to process environmental and social information. Our study focuses on simple behaviors based on the voter model—a well-known minimal protocol to regulate social interactions—that can be implemented in minimalistic machines. Although different from each other, all behaviors confirm the general result: The ability of the swarm to adapt improves when robots have fewer communication links. The average number of links per robot reduces when the individual communication range or the robot density decreases. The analysis of the swarm dynamics via mean-field models suggests that our results generalize to other systems based on the voter model. Model predictions are confirmed by results of multiagent simulations and experiments with 50 Kilobot robots. Limiting the communication to a local neighborhood is a cheap decentralized solution to allow robot swarms to adapt to previously unknown information that is locally observed by a minority of the robots.

AutoMoDe: A novel approach to the automatic design of control software for robot swarms

2014 ◽  
Vol 8 (2) ◽  
pp. 89-112 ◽  
Author(s):  
Gianpiero Francesca ◽  
Manuele Brambilla ◽  
Arne Brutschy ◽  
Vito Trianni ◽  
Mauro Birattari
Keyword(s):  
Automatic Design ◽  
Control Software ◽  
Robot Swarms ◽  
Novel Approach

scholarly journals Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors

2020 ◽  
Vol 10 (13) ◽  
pp. 4654 ◽  
Author(s):  
David Garzón Ramos ◽  
Mauro Birattari
Keyword(s):  
Collective Behavior ◽  
Swarm Robotics ◽  
Design Method ◽  
Fine Tuning ◽  
Automatic Design ◽  
Control Software ◽  
Collective Behaviors ◽  
Robot Swarms ◽  
Software Modules ◽  
Automatic Design Methods

Research in swarm robotics has shown that automatic design is an effective approach to realize robot swarms. In automatic design methods, the collective behavior of a swarm is obtained by automatically configuring and fine-tuning the control software of individual robots. In this paper, we present TuttiFrutti: an automatic design method for robot swarms that belongs to AutoMoDe—a family of methods that produce control software by assembling preexisting software modules via optimization. The peculiarity of TuttiFrutti is that it designs control software for e-puck robots that can display and perceive colors using their RGB LEDs and omnidirectional camera. Studies with AutoMoDe have been so far restricted by the limited capabilities of the e-pucks. By enabling the use of colors, we significantly enlarge the variety of collective behaviors they can produce. We assess TuttiFrutti with swarms of e-pucks that perform missions in which they should react to colored light. Results show that TuttiFrutti designs collective behaviors in which the robots identify the colored light displayed in the environment and act accordingly. The control software designed by TuttiFrutti endowed the swarms of e-pucks with the ability to use color-based information for handling events, communicating, and navigating.

scholarly journals Off-Policy Evaluation of the Performance of a Robot Swarm: Importance Sampling to Assess Potential Modifications to the Finite-State Machine That Controls the Robots

2021 ◽  
Vol 8 ◽  
Author(s):  
Federico Pagnozzi ◽  
Mauro Birattari
Keyword(s):  
Policy Evaluation ◽  
Finite State Machine ◽  
State Machine ◽  
Control Software ◽  
Loosely Coupled ◽  
Robot Swarm ◽  
Current Implementation ◽  
Finite State ◽  
Automatic Design Methods ◽  
The Impact

Due to the decentralized, loosely coupled nature of a swarm and to the lack of a general design methodology, the development of control software for robot swarms is typically an iterative process. Control software is generally modified and refined repeatedly, either manually or automatically, until satisfactory results are obtained. In this paper, we propose a technique based on off-policy evaluation to estimate how the performance of an instance of control software—implemented as a probabilistic finite-state machine—would be impacted by modifying the structure and the value of the parameters. The proposed technique is particularly appealing when coupled with automatic design methods belonging to the AutoMoDe family, as it can exploit the data generated during the design process. The technique can be used either to reduce the complexity of the control software generated, improving therefore its readability, or to evaluate perturbations of the parameters, which could help in prioritizing the exploration of the neighborhood of the current solution within an iterative improvement algorithm. To evaluate the technique, we apply it to control software generated with an AutoMoDe method, Chocolate−6S . In a first experiment, we use the proposed technique to estimate the impact of removing a state from a probabilistic finite-state machine. In a second experiment, we use it to predict the impact of changing the value of the parameters. The results show that the technique is promising and significantly better than a naive estimation. We discuss the limitations of the current implementation of the technique, and we sketch possible improvements, extensions, and generalizations.

scholarly journals Iterative improvement in the automatic modular design of robot swarms

2020 ◽  
Vol 6 ◽  
pp. e322
Author(s):  
Jonas Kuckling ◽  
Thomas Stützle ◽  
Mauro Birattari
Keyword(s):  
Swarm Robotics ◽  
Modular Design ◽  
Finite State Machines ◽  
Optimization Techniques ◽  
State Machines ◽  
Control Software ◽  
Robot Swarms ◽  
Behavior Trees ◽  
Finite State ◽  
Iterative Improvement

Iterative improvement is an optimization technique that finds frequent application in heuristic optimization, but, to the best of our knowledge, has not yet been adopted in the automatic design of control software for robots. In this work, we investigate iterative improvement in the context of the automatic modular design of control software for robot swarms. In particular, we investigate the optimization of two control architectures: finite-state machines and behavior trees. Finite state machines are a common choice for the control architecture in swarm robotics whereas behavior trees have received less attention so far. We compare three different optimization techniques: iterative improvement, Iterated F-race, and a hybridization of Iterated F-race and iterative improvement. For reference, we include in our study also (i) a design method in which behavior trees are optimized via genetic programming and (ii) EvoStick, a yardstick implementation of the neuro-evolutionary swarm robotics approach. The results indicate that iterative improvement is a viable optimization algorithm in the automatic modular design of control software for robot swarms.

AutoMoDe-Chocolate: automatic design of control software for robot swarms

2015 ◽  
Vol 9 (2-3) ◽  
pp. 125-152 ◽  
Author(s):  
Gianpiero Francesca ◽  
Manuele Brambilla ◽  
Arne Brutschy ◽  
Lorenzo Garattoni ◽  
Roman Miletitch ◽  
...  
Keyword(s):  
Automatic Design ◽  
Control Software ◽  
Robot Swarms
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