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One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, 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. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment. ; The authors acknowledge the support of the European Commission within the Work Program ''Future and Emergent Technologies Proactive'' under the grant agreement no. 216342 (Symbrion) and Ghent University (Multidisciplinary Research Partnership 'Bioinformatics: from nucleotides to networks'. YVdP acknowledges support from the European Research Council under the European Union's Seventh Framework Programme ...

Swarm robotic systems, unlike traditional multi-robotic systems, deploy number of cost effective robots which can co-operate, aggregate to form patterns/formations and accomplish missions beyond the capabilities of individual robot. In the event of fire, mine collapse or disasters like earthquake, swarm of robots can enter the area, conduct rescue operations, collect images and convey locations of interest to the rescue team and enable them to plan their approach in advance. Task allocation among members of the swarm is a critical and challenging problem to be addressed. DTTA- a distributed, Time-division multiple access (TDMA) based task allocation framework is proposed for swarm of robots which can be utilised to solve any of the 8 different types of task allocation problem identified by Gerkey and Mataric´. DTTA is reactive and supports task migration via extended task assignments to complete the mission in case of failure of the assigned robot to complete the task. DTTA can be utilised for any kind of robot in land or for co-operative systems comprising of land robots and air-borne drones. Dependencies with other layers of the protocol stack were identified and a quantitative analysis of communication and computational complexity is provided. To our knowledge this is the first work to be reported on task allocation for clustered scalable networks suitable for handling all 8 types of multi-robot task allocation problem. Effectiveness and feasibility of deploying DTTA in real world scenarios is demonstrated by testing the framework for two diverse application scenarios.

We present a method that distributes a swarm of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type) is defined by the traits (capabilities) that it owns. Our method is based on a continuous abstraction of the swarm at a macroscopic level as we model robots switching between tasks. We formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution is reached as quickly as possible. Since our method is based on the derivation of an analytical gradient, it is very efficient with respect to state-of-the-art methods. Building on this result, we propose a real-time optimization method that enables an online adaptation of transition rates. Our approach is well-suited for real-time applications that rely on online redistribution of large-scale robotic systems.

Blockchain technology has the potential to disrupt applications beyond cryptocurrencies. This work applies the concepts of blockchain technology to swarm robotics applications. Swarm robots typically operate in a distributed fashion, wherein the collaboration and coordination between the robots are essential to accomplishing the application goals. However, robot swarms may experience network partitions either due to navigational and communication challenges or in order to perform certain tasks efficiently. We propose a novel protocol, SwarmDAG, that enables the maintenance of a distributed ledger based on the concept of extended virtual synchrony while managing and tolerating network partitions.

In this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule. We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments. We find that (1) if the number of zealots for the lowest quality option is not too high, the decision-making process is driven toward the highest quality option; (2) this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots; (3) when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents.

International audience ; Drones (aerial, terrestrial, marine, underwater, etc.) are more and more widely used in both civilian and military scenario. Still, they remain complex systems for which training, opera- tion preparation and execution of e ective operations require adapted tools and support. In this paper we propose such a tool, that we call Thunderbird, based on a shadow drone that is used to control and to get feedback form a drone on an e ective eld of operation. In this position paper we detail a number of issues that we have identi ed in the design of such a tool and we describe additional problems that arise when considering not only a single drone but a swarm of possibly heterogeneous drones. We also suggest some possible ways to cope with the identi ed issues. We eventually present a rst prototype/proof of concept that we have developed.

International audience ; Drones (aerial, terrestrial, marine, underwater, etc.) are more and more widely used in both civilian and military scenario. Still, they remain complex systems for which training, opera- tion preparation and execution of e ective operations require adapted tools and support. In this paper we propose such a tool, that we call Thunderbird, based on a shadow drone that is used to control and to get feedback form a drone on an e ective eld of operation. In this position paper we detail a number of issues that we have identi ed in the design of such a tool and we describe additional problems that arise when considering not only a single drone but a swarm of possibly heterogeneous drones. We also suggest some possible ways to cope with the identi ed issues. We eventually present a rst prototype/proof of concept that we have developed.

Different geopolitical conflicts of recent years have led to mass migration of several civilian populations. These migrations take place in militarized zones, indicating real danger contexts for the populations. Indeed, civilians are increasingly targeted during military assaults. Defense and security needs have increased; therefore, there is a need to prioritize the protection of migrants. Very few or no arrangements are available to manage the scale of displacement and the protection of civilians during migration. In order to increase their security during mass migration in an inhospitable territory, this article proposes an assistive system using a team of mobile robots, labeled a rover swarm that is able to provide safety area around the migrants. We suggest a coordination algorithm including CNN and fuzzy logic that allows the swarm to synchronize their movements and provide better sensor coverage of the environment. Implementation is carried out using on a reduced scale rover to enable evaluation of the functionalities of the suggested software architecture and algorithms. Results bring new perspectives to helping and protecting migrants with a swarm that evolves in a complex and dynamic environment.

In this paper, we propose a new method for exploring an unknown environment with a team of homogeneous mobile robots. The goal of our approach is to minimize the exploration time. The challenge in multi-robot exploration is how to develop distributed algorithm to govern the colony of robots while choosing its new direction so that they simultaneously explore different regions. In this paper we use the extended version of Particle Swarm Optimization (PSO) to robotic applications, which is referred to as Robotic Particle Swarm Optimization (RPSO), a technique to compute robots new location. To better adapt this technique to the collective exploration problem, and maximize the exploring area, we propose a new method for computing PSO's global best parameter. Experiment results obtained in a simulated environment show that our new method of computing PSO's global best parameter increases the explored area with a shorter time convergence.

The aim of robot path planning is to search for a safe path for the mobile robot. Even though there exist various path planning algorithms for mobile robots, yet only a few are optimized. The optimized algorithms include the Particle Swarm Optimization (PSO) that finds the optimal path with respect to avoiding the obstacles while ensuring safety. In PSO, the sub-optimal solution takes place frequently while finding a solution to the optimal path problem. This paper proposes an enhanced PSO algorithm that contains an improved particle velocity. Experimental results show that the proposed Enhanced PSO performs better than the standard PSO in terms of solution's quality. Hence, a mobile robot implementing the proposed algorithm operates better and is more secure.

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population. ; Multidisciplinary Research Partnership: 01MR0310W and European Union Seventh Framework Programme (FP7/2007-2013): 322739 DOUBLEUP. ; https://peerj.com ; am2017 ; Genetics

Abstract: In this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule. We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments. We find that (1) if the number of zealots for the lowest quality option is not too high, the decision-making process is driven toward the highest quality option; (2) this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots; (3) when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents.