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 Modular automatic design of collective behaviors for robots endowed with local communication capabilities

2020 ◽  
Vol 6 ◽  
pp. e291
Author(s):  
Ken Hasselmann ◽  
Mauro Birattari
Keyword(s):  
Design Process ◽  
Swarm Robotics ◽  
Design Method ◽  
A Priori ◽  
Evolutionary Approach ◽  
Automatic Design ◽  
Collective Behaviors ◽  
Local Communication ◽  
Robot Swarms

We investigate the automatic design of communication in swarm robotics through two studies. We first introduce Gianduja an automatic design method that generates collective behaviors for robot swarms in which individuals can locally exchange a message whose semantics is not a priori fixed. It is the automatic design process that, on a per-mission basis, defines the conditions under which the message is sent and the effect that it has on the receiving peers. Then, we extend Gianduja to Gianduja2 and Gianduja 3, which target robots that can exchange multiple distinct messages. Also in this case, the semantics of the messages is automatically defined on a per-mission basis by the design process. Gianduja and its variants are based on Chocolate, which does not provide any support for local communication. In the article, we compare Gianduja and its variants with a standard neuro-evolutionary approach. We consider a total of six different swarm robotics missions. We present results based on simulation and tests performed with 20 e-puck robots. Results show that, typically, Gianduja and its variants are able to associate a meaningful semantics to messages.

scholarly journals Automatic modular design of robot swarms using behavior trees as a control architecture

2020 ◽  
Vol 6 ◽  
pp. e314
Author(s):  
Antoine Ligot ◽  
Jonas Kuckling ◽  
Darko Bozhinoski ◽  
Mauro Birattari
Keyword(s):  
Swarm Robotics ◽  
Modular Design ◽  
Design Method ◽  
Finite State Machines ◽  
Control Architecture ◽  
Automatic Design ◽  
State Machines ◽  
Collective Behaviors ◽  
Behavior Trees ◽  
Finite State

We investigate the possibilities, challenges, and limitations that arise from the use of behavior trees in the context of the automatic modular design of collective behaviors in swarm robotics. To do so, we introduce Maple, an automatic design method that combines predefined modules—low-level behaviors and conditions—into a behavior tree that encodes the individual behavior of each robot of the swarm. We present three empirical studies based on two missions: aggregation and Foraging. To explore the strengths and weaknesses of adopting behavior trees as a control architecture, we compare Maple with Chocolate, a previously proposed automatic design method that uses probabilistic finite state machines instead. In the first study, we assess Maple’s ability to produce control software that crosses the reality gap satisfactorily. In the second study, we investigate Maple’s performance as a function of the design budget, that is, the maximum number of simulation runs that the design process is allowed to perform. In the third study, we explore a number of possible variants of Maple that differ in the constraints imposed on the structure of the behavior trees generated. The results of the three studies indicate that, in the context of swarm robotics, behavior trees might be appealing but in many settings do not produce better solutions than finite state machines.

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 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.

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 Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ken Hasselmann ◽  
Antoine Ligot ◽  
Julian Ruddick ◽  
Mauro Birattari
Keyword(s):  
Neural Networks ◽  
Automatic Design ◽  
Compelling Evidence ◽  
Typical Application ◽  
Collective Behaviors ◽  
Evolutionary Methods ◽  
Robot Swarms ◽  
The Neural Networks ◽  
Real Robot ◽  
Line Design

AbstractNeuro-evolution is an appealing approach to generating collective behaviors for robot swarms. In its typical application, known as off-line automatic design, the neural networks controlling the robots are optimized in simulation. It is understood that the so-called reality gap, the unavoidable differences between simulation and reality, typically causes neural network to be less effective on real robots than what is predicted by simulation. In this paper, we present an empirical study on the extent to which the reality gap impacts the most popular and advanced neuro-evolutionary methods for the off-line design of robot swarms. The results show that the neural networks produced by the methods under analysis performed well in simulation, but not in real-robot experiments. Further, the ranking that could be observed in simulation between the methods eventually disappeared. We find compelling evidence that real-robot experiments are needed to reliably assess the performance of neuro-evolutionary methods and that the robustness to the reality gap is the main issue to be addressed to advance the application of neuro-evolution to robot swarms.

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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