A: The 9 m 2 arena comprising a nest site, and the foraging space with a randomly placed resource site. B: Finite state machine diagram controlling each and every robot of the swarm. In our multirobot simulator, faults are simulated directly in the robots sensors and actuators. Consequently, the resulting faulty behavior of the robot correspond to the actions performed by its controller, that is either provided with input from faulty sensors, or whose commands to actuators are not executed correctly by the underlying hardware.
The faults simulated in our experiments are representative of seven different scenarios involving permanent failure in the e-puck robots sensor and actuator devices fault types detailed in Table 3. For the infrared proximity sensors, the faults represent scenarios involving disconnected proximity sensors fault type PMIN , obstructions e. The fault on the range-and-bearing sensors fault type PRAB assumes that all the range-and-bearing receivers of the robot are identically affected by the fault.
Consequent to this fault, the strength of the received signal on the range-and-bearing receivers is impeded so that objects that are near to the faulty robot appear to be far. In this section, we assess the performance of our fault-detection algorithm in different scenarios. We first evaluate the capacity of a multirobot system to detect robots behaving faulty for different combinations of normal homogeneous and heterogeneous behaviors, and faulty behaviors.
We then evaluate how frequently normally behaving robots are misclassified as behaving faulty, and the influence of transitions in normal behavior on performance. Finally, we assess the importance of the different components of our algorithm for successful fault detection. We ran experiments with a swarm of 20 e-puck robots. In the swarm, 19 of the 20 robots performed one of the normal behaviors, that is, the aggregation, dispersion, flocking, or homing homogeneous behaviors, or the foraging heterogeneous behavior.
We ran 20 replicates for each of the 35 combinations of 5 normal and 7 faulty behaviors. Each replicate lasted 6, cycles corresponding to s , and we recorded the proportion of time during which the swarm formed a majority coalition identifying the faulty robot. The results are presented in the box-plots in Fig 4 for homogeneous swarm behaviors, and Fig 5 for heterogeneous swarm behaviors, with one box for each combination of normal and faulty behavior.
On each box, the mid-line marks the median, and the box extends from the lower to upper quartile below and above the median. Whisker outside the box indicate the maximum and minimum values, except in case of outliers, which are shown as crosses. Outliers are data points outside of 1. The results show that the median proportion of the time that the faulty robot was detected is above 0. In Table 4 , we explain the situations that caused the poor fault detection performance, in the context of specific normal swarm behaviors. The performance of our fault-detection algorithm was not affected by the robot swarms performing composite swarm behaviors Fig 5.
However, for the fault type ROFS, the multirobot system suffered a poor fault-detection performance, detecting the abnormally behaving robot only a proportion of 0. The range-and-bearing sensors are not being used by the foraging robots during their search for resource sites, and in their return to the nest explore and return to nest states in Fig 4a. Consequently, malfunctions in the range-and-bearing sensors do not affect the behavior of the faulty robot, and the faulty robot behavior is indistinguishable by the swarm, from explorer robots and robots returning to the nest.
The success of a fault detection system depends as much on its capacity to avoid false positives and correctly classify normal swarm behaviors, as its capacity to detect the faulty robots. Consequently, we evaluated our fault detection algorithm in a series of experiments in which all 20 robots behaved normally, performing homogeneous and heterogeneous swarm behaviors. We measured the proportion of time the robots were correctly classified by the multirobot system as behaving normally tolerated. In Fig 6 , we have plotted the mean proportion of time that robots are tolerated in each of 20 replicates of the five normal swarm behaviors horizontal axis , and the variation between the 20 robots observed in each replicate calculated as the difference between the maximum and minimum time tolerated vertical axis.
Mean and variation in proportion of time robots tolerated, across the 20 robots of the multirobot system, in each of 20 replicates, and five normal behaviors: A aggregation circles , B dispersion crosses , C homing towards stationary landmark pluses , D flocking squares , and E cooperative foraging asterisks. The mean proportion of time that normally behaving robots were correctly classified was high across all experiments, at 0.
The variation in time tolerated between the robots of the swarm, in individual replicates, is low less than 0. In these two replications, the false positives were consequent to one to five robots taking longer to join the flock the particular robots random walked between 40 s and s before they encountered the flock; all the other robots had initiated flocking by 30 s into the experiment.
The detected robots thus were, in fact, behaving abnormally, consequent to their initial positions in the swarm, and not because of any faults on the robots. Therefore, our fault-detection system has the capacity to adapt to changes in the normal robot swarm behaviors, while avoiding the misclassification of normal behaviors as faulty. The minimization of false positive incidents is essential, as the subsequent fault diagnosis and fault accommodation procedures may not only be costly, but could lead to the exclusion of capable robots from the multirobot system.
We conducted 20 replicates in which all robots switched behavior. Each experimental replicate had a duration of 1, s. In each replicate, the robots started out by performing a particular normal behavior for the first s, then gradually switched to a second behavior within the next s, and finally reverted back to their original behavior within the remaining s of the experiment.
The fault-detection system was evaluated with three different combinations of behaviors: i aggregation to dispersion to aggregation, ii dispersion to flocking to dispersion, and iii flocking to homing to flocking. Robots were selected at random following a uniform distribution to switch their behavior, and the time between robots switching behavior was 0 s behavior transitioned instantaneously across swarm , The results, in terms of the proportion false-positive incidents, for the different behavior combinations and transition periods are shown in Fig 7.
Proportion of false-positive incidents across the 20 robots of the multirobot system, in each of 20 replicates, and two transitions in normal behavior: A aggregation to dispersion to aggregation, B dispersion to flocking to dispersion, and C flocking to homing to flocking. Robots transitioned between the swarm behaviors in intervals of 0 s instantaneously across the swarm , Our fault-detection system achieved a low number of false-positive incidents for all three behavior transition combinations evaluated.
The mean proportion of reported false-positive incidents sustained by the multirobot was no more than 0. The foraging case study Fig 3 was selected for these experiments, as the robots of the swarm are required to adapt to changes in their environment. We conducted 20 replicates in which the foraging environment was perturbed. Each experimental replicate had a duration of 3, s, and comprised of three stages. Subsequently, in Stage 2, the resource site was removed from the environment, to represent a scarcity of resources. In Stage 3 of the experiment, the resource site was quadrupled in size, and placed no more than 10 cm from the nest site, to represent an abundance of resources.
Each of the three stages lasted for s, to make sure all the robots of the swarm had adapted their behavior to the perturbed foraging environment. The number of robots allocated to different foraging behaviors in each of the three foraging environments, and the proportion false-positive incidents, are shown in Fig 8. The transition to an environment with an absence of resources Stage 1 to Stage 2 resulted on average in a The subsequent perturbation to an environment with an abundance of resources Stage 2 to Stage 3 translated into a Furthermore, in this environment, In summary, the robots of the swarm allocated themselves to different behavior roles in consequence to the nature of the perturbation in their environment.
Our fault-detection algorithm achieved a low number of false-positive incidents in all three sequentially evaluated foraging environments in Stage 1, Stage 2, and Stage 3, mean proportion of false positives no more than 0. In perturbing the foraging environment by removing resources Stage 2 , the robots accumulated a slightly higher proportion of false-positive incidents at 0.
However, environment perturbations did not always translate to an increase in false-positive incidents. The subsequent perturbation to an abundant resource environment Stage 3 registered a negligible proportion of false-positive incidents at 0. In summary, our fault-detection system is largely resilient to environment changes. While some environment perturbations translate to an increase in false-positives, the number of such incidents remains minuscule—even during such perturbations. We assessed the importance of each of these three phase on the fault-detection performance. Our results, detailed in Section B in S1 File , reveal that all three phases of our implemented fault-detection system are necessary for the swarm to achieve good performance.
In this study, we presented a decentralized exogenous fault-detection system for robot swarms. Abnormalities are a symptom of faults in the observed robot; and iii coalition formation, robot consolidate their individual decisions on the classified abnormal behaviors into a swarm-level decision on the identity of the faulty robots. Obtaining reliable inter-robot behavior observations in a distributed manner, the first phase of our fault-detection process, is challenging in large-scale swarms, but crucial for accurate fault detection.
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Robots observe the behaviors of their neighbors, using noisy sensors with limited range, essentially constraining the observations to be brief and sporadic. In our experiments, the range-and-bearing sensors onboard the e-puck robot are employed for behavior observation. The range-and-bearing sensors are readily available on many swarm robot platforms e. In the absence of a dedicated range-and-bearing sensor, time-division-multiplexed IR proximity sensors may provide the robots with equivalent sensor readings [ 40 ]. Alternate observation sensors such as popular onboard camera sensors may also be used for behavior observations, assisted by fiducial markers [ 41 , 42 ] tagged on the robots of the swarm [ 43 ].
In the second phase of our fault-detection process, the classification output of the CRM abnormality detector is accumulated over a series of consecutive control cycles, prior to consensus formation on the detection of the faulty robot. Our results revealed that an increase in the length of the accumulation time window always translated into a lower number of false-positive incidents, accompanied however by a higher latency in the detection of faulty robots. The cost of accommodating a faulty robot is an important consideration in deciding the length of this accumulation time window. By contrast, in critical tasks where faulty robot behavior may be catastrophic, the length of the accumulation time-window may be set to a relatively low value.
In our simulations, every robot of the swarm is synchronized in their fault-detection cycles. However, as local synchronization between robots in close proximity is sufficient for our distributed approach to fault detection, synchronization may easily be enforced in experiments with real robots e. In preliminary experiments detailed in Table A in S1 File , a random perturbation of every robots internal clock following N 5 s , 2.
The impact of a more severe desynchronization of the robots on the performance of the swarm in fault detection is to be studied further. A multi-layered approach to fault detection [ 7 ] integrates both endogenous [ 3 — 6 ] and exogenous fault detection [ 8 — 10 , 12 — 14 , 18 , 51 ] in their design, consequently benefiting from both approaches.
With a robot behavior observation model already in place, our exogenous fault-detection system may be easily extended with the inclusion of an endogenous fault-detection component. Such a component would allow robots to detect faults endogenously by comparing proprioceptively computed behavioral feature vectors, with behavioral feature vectors communicated by observing neighbors.
In utilizing such an approach, false-negatives incidents due to the robot swarm compensating for abnormally behaving robots, would be avoided. Robot fault detection and fault tolerance represent two of the most important problems in the field of multirobot systems. Robot swarms in many real world scenarios, operating in unstructured environments for instance, require a fault-detection system that can adapt to temporal variations in the robots behavior and perturbations to the environment [ 52 , 53 ]. The fault-detection system presented in this study demonstrates these capabilities.
Namely, our fault detection system provides the following significant contributions for robot swarms: Distributed behavior observation model, which is resilient to sensory noise and intermittent and sporadic robot observations. Unlike previous work e. Such a robust collective decision is critical to extend our algorithms to provide fault-recovery strategies for a robot swarm.
Our resulting behavior-driven fault detection system operates independently of the controller architecture employed to execute the swarm behavior. While our experiments have been performed in simulation, largely due to the high cost of individual e-puck robots, noise has been added to simulate the degree of stochasticity inherently associated with real robots. In ongoing work, we are investigating the deployment of our fault-detection system on a relatively small swarm of real mobile robots, and the potential for using our fault-detection system in more challenging outdoor scenarios.
Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. Introduction Robot swarms have the potential to take on numerous real-world tasks [ 1 ]. Materials and methods In this section, we describe our exogenous fault-detection approach for large-scale robot swarms physical parameters of robot swarm in Table 1.
Download: PPT. Robot behavior characterization In the first phase of fault detection, every robot of the swarm executes Algorithm 1 Phase A at the start of each control cycle to observe the behavior of its neighbors over a period of time, and characterize their behaviors. Voting to improve estimation of behavioral features. Crossregulation model. Functioning of the model. CRM in a multirobot system. Detection of abnormally behaving robots. Experimental setup We use a physics-based, discrete-time multirobot simulator named ARGoS [ 30 ], designed to realistically simulate complex experiments involving large swarms of robots.
Homogeneous swarm behaviors In the homogeneous swarm behaviors, all the robots of the swarm execute an identical swarm behavior during the entire duration of the simulation. Heterogeneous swarm behaviors The heterogeneous swarm behaviors require the robots of the swarm to execute complex or composite behaviors, thus entailing the robots of the swarm to often exhibit distinct behaviors at any given time of the simulation.
Fig 3. Foraging experiment setup for heterogeneously behaving swarm of e-puck robots. Faulty behaviors In our multirobot simulator, faults are simulated directly in the robots sensors and actuators. Table 3.
Types of faults, simulated on one robot, selected at random from the swarm. Infrared proximity sensors. Range-and-bearing sensors.
Results In this section, we assess the performance of our fault-detection algorithm in different scenarios. Robot swarm detects almost all injected faults We ran experiments with a swarm of 20 e-puck robots. Fig 4. Fault detection for homogeneously behaving robot swarm. Fig 5. Fault detection for heterogeneously behaving robot swarm. Detection of faults injected in homogeneously behaving swarms. Table 4. Experiment conditions resulting in false-negative incidents in the detection of faulty robots. Detection of faults injected in heterogeneously behaving swarms.
Robot swarm correctly tolerates normal swarm behaviors The success of a fault detection system depends as much on its capacity to avoid false positives and correctly classify normal swarm behaviors, as its capacity to detect the faulty robots. Fig 8. Tolerance to perturbations in the foraging environment. Discussion In this study, we presented a decentralized exogenous fault-detection system for robot swarms. In addition, it has been suggested that adaptation is easier to be threatened by random changes in organisms or systems with higher complexity Orr, In our current research, we assume that considering evolution at different levels and considering interactions between these multiple levels of organization i.
To set up a computational framework to study multilevel evolutionary processes and adaptation in complex systems, and to gain further insights into how adaptation in a changing environment might evolve, we have developed a robot controller that combines an artificial genome with an agent-based system that represents the active Gene Regulatory Network s or GRN s. The full regulatory network is encoded in the genome, consisting of both regulatory and structural genes.
Depending on the environmental signals or cues, part of the encoded network is activated following the rules of transcriptional regulation. The activated part, modelled by an agent-based system, is responsible for sensing the environmental signals, transducing these signals through the network and translating them into the proper behaviour. Whereas the genome represents the encoding of the transcriptional network, the agents can be seen as the functional gene products i.
In all these studies, swarm robots could achieve complex structures or developed complex behavioural strategies using comparatively simple rules of interaction. Furthermore, in some of these recent studies, swarm robots did not only solve complex tasks, but also unveiled some interesting evolutionary patterns and adaptations. In our implementation, each individual swarm robot is equipped with a multiple agent controller representing an active GRN, rather than with an Artificial Neural Network ANN —based system.
In a classic ANN, the architecture of the controller usually allows little interaction with its genetic encoding the genome. To overcome these shortcomings, also others have previously been inspired to use GRNs as controller systems for robots e. Furthermore, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present in the genome.
The blue area represents the genome with genes indicated as red circles and active expressed genes indicated in yellow. Yellow genes connected by white arrows represent the currently active GRN, invoked by environmental inputs. Dark coloured agents represented by small robot figures represent active agents, while grey agents represent agents that will be removed at subsequent steps in the simulation. Genes and GRNs that are no longer active but reside in the system are denoted by green circles.
In general, the higher the concentration value of an agent, the higher the influence of the agent on the final output. The concentration of the agent decays with time, mimicking protein degradation. If the concentration of the agent drops below a pre-set minimal level for instance because of bad performance , the agent will be deleted e. The change in concentration of an agent is determined by a default decay rate and its effect on the fitness of the robot.
In conclusion, we here want to investigate the potential of a novel computational framework that combines a bio-inspired gene regulatory network with an agent-based control system. Through simulations in an A-life environment we show that a swarm of robots running on our bio-inspired controller can indeed evolve complex behaviour and adaptation in a dynamically changing environment.
The main motivation for the particular design of our framework was to allow artificial evolution at multiple levels thereby mimicking biological evolution more closely. In biological systems, the actual phenotype is not only determined by the GRNs encoded by the genome but also depends on the interacting environment, epigenetic effects, chemical reactions, etc. Simulating such complex biological evolution with artificial genomes and GRNs is difficult and usually limited by the underlying network structure and the way this is implemented for instance on an ANN. To encode, represent and maintain a complex evolvable network requires considerable resources which requires sometimes extreme simplification of gene regulation and gene regulation modelling.
Our agent-based GRN aims to allow adaptation at different levels so not only at the GRN level while keeping the system structure as simple as possible. As a result, in our approach, for every gene, we only specified the models about how the particular gene product agent interacts with genes, gene products and environmental conditions.
Based on a limited knowledge base of interaction rules which are encoded in the genome , agents are used to maximize the potential of the GRN in a variable evolutionary context. The experiments are designed such as to test the advantage of integrating evolutionary context at multiple levels the genome, GRN, organisms, population, ….
By comparing with an ANN network based framework for which the evolutionary context at the lower level is limited the ANN controller has a much simpler implementation of interaction, i. Through our experimental setup, we hope to demonstrate that adding evolutionary context at the lower level the genome, the GRN can accelerate the evolution of complex behaviour at the higher level population level while also improving overall adaption in a changing environment Walker et al. Our GRN-based controller actually consists of two separate layers: a bio-inspired artificial genome AG , based on the model of Reil , and an agent-based layer.
For the genome, in short, we distinguish between signalling, regulatory and structural genes, which all have the same basic structure but different functionalities see The Artificial Genome, Supplemental Information 1. The AG thus encodes the full regulatory network or entire collection of genes and all its possible interactions, but which specific GRN part of the AG is active at a certain time depends on the environmental cues and thus is context-dependent see Fig. The AG, which consists of 10 chromosomes of 10, randomly generated characters, changes over time by evolutionary forces such as mutations and duplications see Mutational Operators Acting on the Genome, Supplemental Information 1.
The second layer consists of an agent-based system that represents the context-dependent instantiation of the GRN Fig. Three types of agents have been defined, each corresponding to a specific gene type. Agents can be seen as the translation product of genes. When a new robot is initialised, based on the current sensor inputs, signalling genes will be activated.
These genes will produce a number of signalling agents that will produce a signal value. Next, the signalling agents will search the genome to activate those genes for which the signal value matches the binding site. This will lead to the activation of regulatory genes, again via the translation to agents, which are initialized with certain concentration values, mimicking dosage values of proteins or enzymes. Binding of a target gene by a regulator can have different outcomes and will either activate expression of the target gene or block expression of the target gene.
If the regulatory gene binds to a structural gene end product , structural agents will send output values to the actuators, leading to certain behaviour of the simulated robot, increasing or decreasing its fitness Fig. It should be noted that usually, each actuator receives many parameter values from different structural agents and will average these into one final value that will then be used as the control parameter output value for this particular actuator. In summary, in our approach, the different components of a GRN, such as regulatory and structural genes, active in a single simulated robot, are simulated by different agents.
Contrary to fixed nodes in a neural network, these agents can dynamically alter their status and functionality in response to changes in the environment and the feedback they receive based on performance Fig. As previously described, every simulated robot has different functionalities, each of which comes with a different energy cost and energy consumption style see Robot Functionalities, Supplemental Information 1.
The total energy consumption for one robot during one time step depends on some basic energy consumption plus some energy consumption for performing certain functionalities.
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The robots live in a two-dimensional 90 by 90 matrix or grid in which a number of energy food sources are randomly distributed. Several types of food sources exist that differ from each other in the minimal amount of energy required to access the food source. If a robot does not have enough energy to cover its basic living energy consumption, it will be regarded as dead and removed from the simulation.
During every time step, every robot will sense the number of surrounding robots and food sources. This information will be returned to the controller of the robot as environmental input Fig. Except for such external input, the robot will also keep track of its own energy level and energy consumption and of certain other actions such as the number of successful attacks, defences, replications, and so on as internal input. Both external and internal inputs will be regarded as sensor inputs by the controller. Selection and fitness of the robots are all based on energy.
The energy consumption of the robot is based on its behaviour. For example, movement, replication, attack and defence all have different costs see Robot Functionalities, Supplemental Information 1. During runtime, the robot population will be regarded as gone extinct or better, not being able to survive when the population size becomes smaller than robots. Indeed, we observed that the robot population will quickly die off when the population size is close to and food has become scarce. Although the energy level in the robot population is an indicator of the fitness of the population, there is no explicit fitness function.
The energy level of each individual robot is the only critical factor for its survival or reproduction but is not directly responsible for any particular trait to evolve or for showing some behavioural pattern. Indeed, robots can have various strategies to obtain or save energy during the simulation, so the energy level of robots is always a combination of multiple strategies and responses to various environmental conditions. However, there are important differences with our approach. For instance, during simulation, at certain times e. The values of these extra energy costs differ at different times during the simulation e.
Every food source is capable to replicate itself grow with a certain rate at every time step. The growth rate depends on the surrounding food density the higher the density, the lower the replication rate. As a result, adaptation of the robots actually comes down to reaching and maintaining a dynamic equilibrium between finding and consuming food and energy balance.
Trying to adapt to a changing environment means that it is difficult to define an explicit fitness function. Every robot needs to adjust its strategy based on the current situation. For example, in our simulations, the energy level of a robot is the key to robot survival and replication but this does not necessarily mean that robots with higher energy levels are the most adaptive.
Having more energy to consume more food may undermine new food growth in an environment where food is already scarce. The whole population will face extinction when all food sources are being consumed and therefore, the robots need to evolve more complex behaviour in order to survive for extended periods. During our simulations, we specifically analysed the adaptive process regarding the collective behaviour of organisms, based on the interaction between individuals. For instance, aggregation, by which energy is shared, can be beneficial for defence against preying.
When it comes to preying behaviour, it should be noted that before the predator can actually prey, it needs to perform an attack. An attack action does not always result in preying: if predators are comparatively weaker, an attack action will only cost more energy and will not occur see Robot Functionalities, Supplemental Information 1. Maybe a bit unexpectedly, in all simulations GRN and ANN , cooperation behaviour in the form of aggregation does not look very pronounced. We assume that this can be explained by the fact that aggregation requires extra cooperation between multiple robots and such cooperation needs longer to evolve than other kinds of behaviours.
This will be investigated in future simulations with longer run times. Indeed, individual members have evolved their own strategies, but after aggregating, all members of the robotic organism have to use the same strategy. However, a compromise inferred from all individual strategies can be deleterious to most members. To really to be able to evolve interesting aggregation patterns in our evolutionary scenarios and simulations, apart from longer running times, we probably also need to simplify integration and cooperation in the robotic organism.
A shows the result of GRN robots in the simulation. B shows the typical result for ANN robots in the simulation evaluations have been done every 10 time steps. See text for details. For prey behaviour the situation is clearly different. In the GRN robot simulations, but not in the ANN robot simulations, we often observe that the occurrence of prey behaviour increases in the population during the last stages of the simulation, when food becomes scarce see Figs.
For the ANN robot simulations, prey frequencies are generally higher than in GRN robot populations, but do not seem to be a specific adaptation when food becomes scarcer not shown. To see whether the increased prey behaviour in GRN robots are indeed a specific adaptation when food becomes scarce, we estimated the Pearson correlation between the prey frequency and the current food number recorded every 10 steps during our simulation.
Details on the statistical validation can be found in Supplemental Information 1 Statistical evidence of preying as specific adaptation. For most of the runs here shown, an increase in the number of preying actions can be observed at the end of the simulation at time step 3, The increase of preying behaviour, as seen in a large number of our simulations see Fig.
Prey behaviour reduces the population size but increases the energy of the remaining robots, so at least part of the population can survive longer. On the other hand, prey behaviour also comes with risk and costs energy to every individual robot in the population defence and attacks both cost extra energy.
However, for such complex adaptive behaviour to emerge, a number of conditions need to be fulfilled. Only the combination of a limited number of food sources and large enough population sizes make prey action a trait for selection and therefore an advantageous adaptation. If food sources maintain to be abundant to the population, simply searching for food is obviously an easier and more efficient option than preying.
Therefore, we do not observe the combination of prey and flocking together behaviour evolving at the early stages of our simulations when food resources are still abundant. Once preying has evolved, it usually continues to be a useful strategy for the population to survive, as shown in Fig. However, unlike as in the experiments shown in Figs. As can be observed, once evolved, prey behaviour continues to be a strategy used by part of the population to survive. It should be noted that, in general, even without showing prey behaviour, GRN robots survive much longer with fewer food sources than the ANN robots.
While on average, the ANN robot population dies when no more than food sources are available, the GRN robot population can survive until the number of food sources has been reduced to, on average, 80 or less Fig. The fact that the GRN robots can, on average, survive much longer, is, most likely due to the fact that they are more efficient in finding the food sources exploring the grid , also when these become rarer.
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The population is supposed to be extinct when the number of robots drops below Offspring will be created in the same cells as their parents, which will initially increase the number of neighbours. The study of the chemical basis of processes occurring in living organisms, including the processes by which these substances enter into, or are formed in, the organisms and react with each other and the environment.
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These studies can include scaffolds for recruiting or supporting regenerative cells or tissues or the engineering designs for creating the correct environment for regenerative growth. It includes the study of motion, material deformation, flow within the body and in devices, and transport of chemical constituents across biological and synthetic media and membranes. Such studies include biological circuit design, genetic circuits, protein engineering, nucleic acid engineering, rational design, directed evolution and metabolic engineering. This is an interdisciplinary field that studies the structure, function, intracellular pathways, and formation of cells.
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