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BACKGROUND: Automode is a feature on Servo ventilators that automatically switches between mandatory and spontaneous breaths. Spontaneous breaths suppress mandatory breaths until apnea. The period from the last spontaneous breath to the first mandatory breath is automatically adjusted by a calculated apnea time limit based on a maximum apnea time setting, the mandatory breathing frequency setting, and the spontaneous breath count. The purpose of this study was to validate the apnea time algorithm by using simulated mechanical ventilation. METHODS: A Servo-u ventilator was connected to an ASL 5000 breathing simulator. Ventilator settings were the following: Automode (pressure control to pressure support), pressure control = 10 cm H2O; pressure support 5 = cm H2O; PEEP = 10 cm H2O; breathing frequency 5 10, 12, 15, 20 breaths/min; maximum apnea time = 7 and 12 s. Simulator settings were the following: resistance = 10 cm H2O/L/s; compliance = 35 mL/cm H2O; flow trigger model: frequency = 20 breaths/min, trigger flow = 10 L/min, trigger duration 5 800 ms. Flow waveforms were recorded, and the observed apnea time limit was compared with the calculated value. The outcome variable was error, defined as the difference between observed and calculated apnea times expressed as a percentage. RESULTS: The observed apnea time limit ranged from 3 to 12 s, depending on the mandatory frequency and the spontaneous breath count. The average error ranged from 22 to 0%. CONCLUSIONS: The measured apnea time for simulated ventilation settings was within 2% of calculated times. Automode allowed a spontaneous frequency lower than expected based on the mandatory frequency. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

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.

Background: Automode is a feature on Servo ventilators that automatically switches between mandatory and spontaneous breaths. Spontaneous breaths suppress mandatory breaths until apnea. The period from the last spontaneous breath to the first mandatory breath is automatically adjusted by a calculated apnea time limit based on a maximum apnea time setting, the mandatory breathing frequency setting, and the spontaneous breath count. The purpose of this study was to validate the apnea time algorithm by using simulated mechanical ventilation., Methods: A Servo-u ventilator was connected to an ASL 5000 breathing simulator. Ventilator settings were the following: Automode (pressure control to pressure support), pressure control = 10 cm H 2 O; pressure support = 5 cm H 2 O; PEEP = 10 cm H 2 O; breathing frequency = 10, 12, 15, 20 breaths/min; maximum apnea time = 7 and 12 s. Simulator settings were the following: resistance = 10 cm H 2 O/L/s; compliance = 35 mL/cm H 2 O; flow trigger model: frequency = 20 breaths/min, trigger flow = 10 L/min, trigger duration = 800 ms. Flow waveforms were recorded, and the observed apnea time limit was compared with the calculated value. The outcome variable was error, defined as the difference between observed and calculated apnea times expressed as a percentage., Results: The observed apnea time limit ranged from 3 to 12 s, depending on the mandatory frequency and the spontaneous breath count. The average error ranged from -2 to 0%., Conclusions: The measured apnea time for simulated ventilation settings was within 2% of calculated times. Automode allowed a spontaneous frequency lower than expected based on the mandatory frequency., (Copyright © 2023 by Daedalus Enterprises.)

We present two empirical studies on the design of control software for robot swarms. In Study A, Vanilla and EvoStick, two previously published automatic design methods, are compared with human designers. The comparison is performed on five swarm robotics tasks that are different from those on which Vanilla and EvoStick have been previously tested. The results show that, under the experimental conditions considered, Vanilla performs better than EvoStick, but it is not able to outperform human designers. The results indicate that Vanilla 's weak element is the optimization algorithm employed to search the space of candidate designs. To improve over Vanilla and with the final goal of obtaining an automatic design method that performs better than human designers, we introduce Chocolate, which differs from Vanilla only in the fact that it adopts a more powerful optimization algorithm. In Study B, we perform an assessment of Chocolate. The results show that, under the experimental conditions considered, Chocolate outperforms both Vanilla and the human designers. Chocolate is the first automatic design method for robot swarms that, at least under specific experimental conditions, is shown to outperform a human designer. [ABSTRACT FROM AUTHOR]

• Automatic methods can design spatially-organizing behaviors for robot swarms. • AutoMoDe-Mate is a design method specialized in spatial organization problems. • AutoMoDe-Mate includes a module that enables robot pattern formation. • Experiments with physical robots show effectiveness in coverage and networking. • AutoMoDe-Mate outperforms baseline modular and neuro-evolutionary methods. [Display omitted] We present Mate, an automatic off-line design method specialized in the design of spatially-organizing behaviors for robot swarms. Mate belongs to the family of modular methods known as AutoMoDe. We introduce Mate to study the automatic design of collective behaviors for missions in which the swarm is subject to spatial distribution constrains. In this paper, we produce control software for three missions with specifications related to the distribution of the swarm in the environment. We conduct experiments in simulation and with a swarm of 20 e-puck robots. Alongside Mate, we also conduct experiments with two other automatic design methods: Chocolate—a state-of-the-art instance of AutoMoDe; and EvoSpace—a method based on neuro-evolution. Early studies conducted with existing modular design methods have shown their limitations in the design of spatially-organizing behaviors for robots that operate under spatial constrains. By introducing a specialized method like Mate, we expect to overcome these limitations. The aggregate results of our experiments show that Mate performs significantly better than Chocolate and EvoSpace in the missions we consider. [ABSTRACT FROM AUTHOR]

We introduce AutoMoDe: a novel approach to the automatic design of control software for robot swarms. The core idea in AutoMoDe recalls the approach commonly adopted in machine learning for dealing with the bias-variance tradedoff: to obtain suitably general solutions with low variance, an appropriate design bias is injected. AutoMoDe produces robot control software by selecting, instantiating, and combining preexisting parametric modules-the injected bias. The resulting control software is a probabilistic finite state machine in which the topology, the transition rules and the values of the parameters are obtained automatically via an optimization process that maximizes a task-specific objective function. As a proof of concept, we define AutoMoDe- Vanilla, which is a specialization of AutoMoDe for the e-puck robot. We use AutoMoDe- Vanilla to design the robot control software for two different tasks: aggregation and foraging. The results show that the control software produced by AutoMoDe- Vanilla (i) yields good results, (ii) appears to be robust to the so called reality gap, and (iii) is naturally human-readable. [ABSTRACT FROM AUTHOR]

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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., Competing Interests: Thomas Stützle and Mauro Birattari are Academic Editors for PeerJ Computer Science., (© 2020 Kuckling et al.)

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., Competing Interests: Mauro Birattari is an Academic Editor for PeerJ., (© 2020 Ligot et al.)