Special Issue "Recent Advances in Swarm Robotics"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (1 March 2021).

Special Issue Editor

Prof. Dr. Álvaro Gutiérrez
E-Mail Website
Guest Editor
E.T.S. Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: control systems; smart houses; zero energy buildings; demand-side management; swarm intelligence; autonomous systems; sensor networks; cooperative systems; data analysis
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Special Issue Information

Dear Colleagues,

Swarm robotics research has been present for some decades, providing nature-inspired algorithms in swarms of robots. During this time, many different algorithms have focused on specific tasks, such as coordination, cooperation, organization, division of labor, or task allocation among others. However, not many of those works have addressed the problem of unifying base algorithms that could lay the groundwork of high-level behaviors.

This special issue aims to provide research work on base algorithms for the emergence of complex swarm robotics behaviors, providing novel solutions and discussions for future trends in the field. Local, situated, and embodied communication, as well as stigmergy, have a special role in laying the foundations of these algorithms.

The topics of interest of the special issue include, but are not limited to the following:

  • Distributed communication
  • Dynamic task allocation
  • Aggregation
  • Pattern formation
  • Collective movement
  • Division of labor
  • Leadership paradigms
  • Clustering
  • Self-organization
  • Cooperative control
  • Distributed sensing
  • Distributed action
  • Distributed localization
  • Object localization
  • Swarm synchronization
  • Fault tolerance

Prof. Álvaro Gutiérrez
Guest Editor

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Published Papers (9 papers)

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Research

Article
Evolution of Situated and Abstract Communication in Leader Selection and Borderline Identification Swarm Robotics Problems
Appl. Sci. 2021, 11(8), 3516; https://doi.org/10.3390/app11083516 - 14 Apr 2021
Viewed by 459
Abstract
The design of robust yet simple communication mechanisms, that allow the cooperation through direct interaction among robots, is an important aspect of swarm robotics systems. In this paper, we analyze how an identical continuous-time recurrent neural network (CTRNN) controller can lead to the [...] Read more.
The design of robust yet simple communication mechanisms, that allow the cooperation through direct interaction among robots, is an important aspect of swarm robotics systems. In this paper, we analyze how an identical continuous-time recurrent neural network (CTRNN) controller can lead to the emergence of different kinds of communications within the swarm, either abstract or situated, depending on the problem to be faced. More precisely, we address two swarm robotics tasks that require, at some extent, communication to be solved: leader selection and borderline identification. The parameters of the CTRNN are evolved using separable natural evolution strategies. It is shown that, using the same starting conditions and robots’ controllers, the evolution process leads to the emergence of utterly diverging communications. Firstly, an abstract communication, in which the message carries all the information, results from evolution in the leader selection task. Alternatively, a purely situated communication, meaning that only the context is communicative, emerges when dealing with the borderline identification problem. Nonetheless, scalability and robustness properties are successfully validated. Full article
(This article belongs to the Special Issue Recent Advances in Swarm Robotics)
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Article
Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization
Appl. Sci. 2021, 11(8), 3417; https://doi.org/10.3390/app11083417 - 10 Apr 2021
Cited by 1 | Viewed by 627
Abstract
Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area [...] Read more.
Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest. Full article
(This article belongs to the Special Issue Recent Advances in Swarm Robotics)
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Article
Control of a Robotic Swarm Formation to Track a Dynamic Target with Communication Constraints: Analysis and Simulation
Appl. Sci. 2021, 11(7), 3179; https://doi.org/10.3390/app11073179 - 02 Apr 2021
Cited by 2 | Viewed by 415
Abstract
We describe and analyze the Local Charged Particle Swarm Optimization (LCPSO) algorithm, that we designed to solve the problem of tracking a moving target releasing scalar information in a constrained environment using a swarm of agents. This method is inspired by flocking algorithms [...] Read more.
We describe and analyze the Local Charged Particle Swarm Optimization (LCPSO) algorithm, that we designed to solve the problem of tracking a moving target releasing scalar information in a constrained environment using a swarm of agents. This method is inspired by flocking algorithms and the Particle Swarm Optimization (PSO) algorithm for function optimization. Four parameters drive LCPSO—the number of agents; the inertia weight; the attraction/repulsion weight; and the inter-agent distance. Using APF (Artificial Potential Field), we provide a mathematical analysis of the LCPSO algorithm under some simplifying assumptions. First, the swarm will aggregate and attain a stable formation, whatever the initial conditions. Second, the swarm moves thanks to an attractor in the swarm, which serves as a guide for the other agents to head for the target. By focusing on a simple application of target tracking with communication constraints, we then remove those assumptions one by one. We show the algorithm is resilient to constraints on the communication range and the behavior of the target. Results on simulation confirm our theoretical analysis. This provides useful guidelines to understand and control the LCPSO algorithm as a function of swarm characteristics as well as the nature of the target. Full article
(This article belongs to the Special Issue Recent Advances in Swarm Robotics)
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Article
Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
Appl. Sci. 2021, 11(7), 3115; https://doi.org/10.3390/app11073115 - 31 Mar 2021
Viewed by 681
Abstract
Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be [...] Read more.
Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures. Full article
(This article belongs to the Special Issue Recent Advances in Swarm Robotics)
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Article
Learning a Swarm Foraging Behavior with Microscopic Fuzzy Controllers Using Deep Reinforcement Learning
Appl. Sci. 2021, 11(6), 2856; https://doi.org/10.3390/app11062856 - 23 Mar 2021
Viewed by 406
Abstract
This article presents a macroscopic swarm foraging behavior obtained using deep reinforcement learning. The selected behavior is a complex task in which a group of simple agents must be directed towards an object to move it to a target position without the use [...] Read more.
This article presents a macroscopic swarm foraging behavior obtained using deep reinforcement learning. The selected behavior is a complex task in which a group of simple agents must be directed towards an object to move it to a target position without the use of special gripping mechanisms, using only their own bodies. Our system has been designed to use and combine basic fuzzy behaviors to control obstacle avoidance and the low-level rendezvous processes needed for the foraging task. We use a realistically modeled swarm based on differential robots equipped with light detection and ranging (LiDAR) sensors. It is important to highlight that the obtained macroscopic behavior, in contrast to that of end-to-end systems, combines existing microscopic tasks, which allows us to apply these learning techniques even with the dimensionality and complexity of the problem in a realistic robotic swarm system. The presented behavior is capable of correctly developing the macroscopic foraging task in a robust and scalable way, even in situations that have not been seen in the training phase. An exhaustive analysis of the obtained behavior is carried out, where both the movement of the swarm while performing the task and the swarm scalability are analyzed. Full article
(This article belongs to the Special Issue Recent Advances in Swarm Robotics)
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Article
Collective Memory: Transposing Pavlov’s Experiment to Robot Swarms
Appl. Sci. 2021, 11(6), 2632; https://doi.org/10.3390/app11062632 - 16 Mar 2021
Viewed by 522
Abstract
Remembering information is a fundamental aspect of cognition present in numerous natural systems. It allows adaptation of the behavior as a function of previously encountered situations. For instance, many living organisms use memory to recall if a given situation incurred a penalty or [...] Read more.
Remembering information is a fundamental aspect of cognition present in numerous natural systems. It allows adaptation of the behavior as a function of previously encountered situations. For instance, many living organisms use memory to recall if a given situation incurred a penalty or a reward and rely on that information to avoid or reproduce that situation. In groups, memory is commonly studied in the case where individual members are themselves capable of learning and a few of them hold pieces of information that can be later retrieved for the benefits of the group. Here, we investigate how a group may display memory when the individual members have reactive behaviors and can not learn any information. The well known conditioning experiments of Pavlov illustrate how single animals can memorize stimuli associated with a reward and later trigger a related behavioral response even in the absence of reward. To study and demonstrate collective memory in artificial systems, we get inspiration from the Pavlov experiments and propose a setup tailored for testing our robotic swarm. We devised a novel behavior based on the fundamental process of aggregation with which robots exhibit collective memory. We show that the group is capable of encoding, storing, and retrieving information that is not present at the level of the individuals. Full article
(This article belongs to the Special Issue Recent Advances in Swarm Robotics)
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Article
Software Architecture for Autonomous and Coordinated Navigation of UAV Swarms in Forest and Urban Firefighting
Appl. Sci. 2021, 11(3), 1258; https://doi.org/10.3390/app11031258 - 29 Jan 2021
Cited by 1 | Viewed by 877
Abstract
Advances in the field of unmanned aerial vehicles (UAVs) have led to an exponential increase in their market, thanks to the development of innovative technological solutions aimed at a wide range of applications and services, such as emergencies and those related to fires. [...] Read more.
Advances in the field of unmanned aerial vehicles (UAVs) have led to an exponential increase in their market, thanks to the development of innovative technological solutions aimed at a wide range of applications and services, such as emergencies and those related to fires. In addition, the expansion of this market has been accompanied by the birth and growth of the so-called UAV swarms. Currently, the expansion of these systems is due to their properties in terms of robustness, versatility, and efficiency. Along with these properties there is an aspect, which is still a field of study, such as autonomous and cooperative navigation of these swarms. In this paper we present an architecture that includes a set of complementary methods that allow the establishment of different control layers to enable the autonomous and cooperative navigation of a swarm of UAVs. Among the different layers, there are a global trajectory planner based on sampling, algorithms for obstacle detection and avoidance, and methods for autonomous decision making based on deep reinforcement learning. The paper shows satisfactory results for a line-of-sight based algorithm for global path planner trajectory smoothing in 2D and 3D. In addition, a novel method for autonomous navigation of UAVs based on deep reinforcement learning is shown, which has been tested in 2 different simulation environments with promising results about the use of these techniques to achieve autonomous navigation of UAVs. Full article
(This article belongs to the Special Issue Recent Advances in Swarm Robotics)
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Article
Accelerating Emergence of Aerial Swarm
Appl. Sci. 2020, 10(22), 7986; https://doi.org/10.3390/app10227986 - 11 Nov 2020
Viewed by 614
Abstract
Herein, we present a methodology and framework for exploiting certain interdisciplinary studies that can particularly benefit from integration. In this paper, rigorous derivation of control theory and statistical analysis of simulation results are organically unified for testifying and optimizing the emergence of order [...] Read more.
Herein, we present a methodology and framework for exploiting certain interdisciplinary studies that can particularly benefit from integration. In this paper, rigorous derivation of control theory and statistical analysis of simulation results are organically unified for testifying and optimizing the emergence of order in aerial swarming scenarios under free boundary conditions. Each Unmanned Aerial Vehicle (UAV) is regulated by a simplified mathematical model, based on which a distributed flocking protocol is proposed as a feasible solution for aerial swarms. On condition that the initial interaction network is connected, the LaSalle–Krasovskii invariance principle is implemented to verify the effectiveness of the above algorithm. However, most existing results on flocking are far from being engineering applications. A basic challenge is how to present a low-cost energy and time saving solution on account of the limited flight capability of these UAVs and real-time operational requirements. As is well known, energy consumption can be reduced if unnecessary interactions among individuals are eliminated. Therefore, another contribution of this paper is to propose a precise optimization of an existing flocking algorithm for UAVs with respect to interaction requirements. Energy and time measurements, as well as scalability effects, are assessed in terms of statistical significance and strength. The results indicate that the flocking control protocol adopting the minimal interaction is the most promising swarm. Full article
(This article belongs to the Special Issue Recent Advances in Swarm Robotics)
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Article
UAV Deployment Using Two Levels of Stigmergy for Unstructured Environments
Appl. Sci. 2020, 10(21), 7696; https://doi.org/10.3390/app10217696 - 30 Oct 2020
Viewed by 439
Abstract
This article will present two swarming behaviors for deployment in unstructured environments using unmanned aerial vehicles (UAVs). These behaviors will use stigmergy for communication. We found that there are currently few realistic deployment approaches that use stigmergy, due mainly to the difficulty of [...] Read more.
This article will present two swarming behaviors for deployment in unstructured environments using unmanned aerial vehicles (UAVs). These behaviors will use stigmergy for communication. We found that there are currently few realistic deployment approaches that use stigmergy, due mainly to the difficulty of building transmitters and receivers for this type of communication. In this paper, we will provide the microscopic design of two behaviors with different technological and information requirements. We will compare them and also investigate how the number of agents influences the deployment. In this work, these behaviors will be exhaustively analyzed, taking into account different take-off time interval strategies, the number of collisions, and the time and energy required by the swarm. Numerous simulations will be conducted using unstructured maps generated at random, which will enable the establishment of the general functioning of the behaviors independently of the map used. Finally, we will show how both behaviors are capable of achieving the required deployment task in terms of covering time and energy consumed by the swarm. We will discuss how, depending on the type of map used, this task can be performed at a lower cost without using a more informed (but expensive) robotic swarm. Full article
(This article belongs to the Special Issue Recent Advances in Swarm Robotics)
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