Automatic Modular Design of Behavior Trees for Robot Swarms with Communication Capabilites

2021 ◽  
pp. 130-145
Author(s):  
Jonas Kuckling ◽  
Vincent van Pelt ◽  
Mauro Birattari
Keyword(s):  
Modular Design ◽  
Robot Swarms ◽  
Behavior Trees

Behavior Trees as a Control Architecture in the Automatic Modular Design of Robot Swarms

2018 ◽  
pp. 30-43 ◽  
Author(s):  
Jonas Kuckling ◽  
Antoine Ligot ◽  
Darko Bozhinoski ◽  
Mauro Birattari
Keyword(s):  
Modular Design ◽  
Control Architecture ◽  
Robot Swarms ◽  
Behavior Trees

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.

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.

Unified behavior framework in discrete event simulation systems

2017 ◽  
Vol 14 (4) ◽  
pp. 471-481 ◽  
Author(s):  
Alexander J Kamrud ◽  
Douglas D Hodson ◽  
Gilbert L Peterson ◽  
Brian G Woolley
Keyword(s):  
Decision Making ◽  
Intelligent Agents ◽  
Modular Design ◽  
Discrete Event ◽  
Agent Model ◽  
Air Defense ◽  
Event Simulation ◽  
Behavior Trees ◽  
Code Complexity ◽  
Simulation Systems

Intelligent agents provide simulations a means to add lifelike behavior in place of manned entities. When implemented, typically a single intelligent agent model (or approach to defining decision making), such as rule-based, behavior trees, neural networks, etc., is selected. This choice introduces restrictions into what behaviors agents can manifest, and can require significant testing in edge cases. This paper presents the incorporation and application of the Unified Behavior Framework (UBF) into the Advanced Framework for Simulation, Integration, and Modeling environment. The UBF provides the flexibility to implement any and all behavior-based systems, allowing the developer to rapidly assemble a decision making agent that leverages multiple paradigms or approaches. The UBF achieves this by leveraging several key software engineering principles: modular design, scalability through reduced code complexity, simplified development and testing through abstraction, and the promotion of code reuse. The use of UBF to define intelligent agents within a 2v2 Integrated Air Defense System is demonstrated.

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