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

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.

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

Applications of Evolutionary Computation - Lecture Notes in Computer Science ◽

10.1007/978-3-030-72699-7_9 ◽

2021 ◽

pp. 130-145

Author(s):

Jonas Kuckling ◽

Vincent van Pelt ◽

Mauro Birattari

Keyword(s):

Modular Design ◽

Robot Swarms ◽

Behavior Trees

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Iterative improvement in the automatic modular design of robot swarms

PeerJ Computer Science ◽

10.7717/peerj-cs.322 ◽

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.

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AutoMoDe-IcePop: Automatic Modular Design of Control Software for Robot Swarms Using Simulated Annealing

Communications in Computer and Information Science - Artificial Intelligence and Machine Learning ◽

10.1007/978-3-030-65154-1_1 ◽

2020 ◽

pp. 3-17

Author(s):

Jonas Kuckling ◽

Keneth Ubeda Arriaza ◽

Mauro Birattari

Keyword(s):

Simulated Annealing ◽

Modular Design ◽

Control Software ◽

Robot Swarms

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Inverse Kinematic and Control Architecture of a Slave Manipulator for MR-Guided Brain Biopsy

Volume 2: Biomedical and Biotechnology Engineering ◽

10.1115/imece2008-68302 ◽

2008 ◽

Author(s):

C. Raoufi ◽

A. A. Goldenberg ◽

W. Kucharczyk ◽

H. Hadian

Keyword(s):

Modular Design ◽

Brain Biopsy ◽

Inverse Kinematic ◽

Robotic System ◽

Control Architecture ◽

Neurosurgical Procedures ◽

Biopsy Needle ◽

Mri Guided ◽

Position And Orientation ◽

And Control

In this paper, the inverse kinematic and control paradigm of a novel tele-robotic system for MRI-guided interventions for closed-bore MRI-guided brain biopsy is presented. Other candidate neurosurgical procedures enabled by this system would include thermal ablation, radiofrequency ablation, deep brain stimulators, and targeted drug delivery. The control architecture is also reported. The design paradigm is fundamentally based on a modular design configuration of the slave manipulator that is performing tasks inside MR scanner. The tele-robotic system is a master-slave system. The master manipulator consists of three units including: (i) the navigation module; (ii) the biopsy module; and (iii) the surgical arm. Navigation and biopsy modules were designed to undertake the alignment and advancement of the surgical needle respectively. The biopsy needle is held and advanced by the biopsy module. The biopsy module is attached to the navigation module. All three units are held by a surgical arm. The main challenge in the control of the biopsy needle using the proposed navigation module is to adjust a surgical tool from its initial position and orientation to a final position and orientation. In a typical brain biopsy operation, the desired task is to align the biopsy needle with a target knowing the positions of both the target in the patient’s skull and the entry point on the surface of the skull. In this paper, the mechanical design, control paradigms, and inverse kinematics model of the robot are reported.

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