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Performance curve for (left) constant robot size of 1 m as more robots are added, or (right) constant swarm capacity of 50 m, where the capacity can be allocated to either few large robots, or many small robots. The constant size case shows diminishing returns when adding additional robots. The constant capacity case shows a small initial drop in performance as the number of robots increases (and size decreases), but the performance is steady as the number of robots continues to increase
Source publication
Designing a robot swarm requires a swarm designer to understand the trade-offs unique to a swarm. The most basic design decisions are how many robots there should be in the swarm and the individual robot size. These choices in turn impact swarm cost and robot interference, and therefore swarm performance. The underlying physical reasons for why the...
Citations
... In, the influence of drone characteristics on the tasks that can be assigned to the swarm is considered [12]. An example is a swarm of aquatic robots designed to remove harmful algae from the water. ...
Citation: Shyian, A. (2024). Approach to conception and modeling for distributed hierarchical control for autonomous drone swarm. Adv Mach Lear Art Inte, 5(1), 01-08. Annotation Control of a drone swarm as a unity requires decentralization and hierarchy. Decentralizing control of drone swarms is necessary to free the human operator from having to constantly control the behavior of the drones within the swarm. Hierarchical control of a drone swarm is necessary so that human operators can adjust the activity of the swarm as a unit. The following separate roles have been identified for the implementation of decentralized hierarchical control of swarm activity: the activity of a separate drone, the activity of a drone-coordinator, and the activity of a human operator. The control hierarchy consists of a human operator who controls the change in the behavior of the drone coordinator. The drone coordinator controls the changes in the programmed behavior of individual drones. This approach is an analog of the management of human workers who perform assigned work, which opens up several possibilities. First, it is possible to use formal models of performance people's behavior in social teams. Second, formal models can be used for decision-making and optimization for controlling a drone-coordinator in a swarm. Thirdly, computer modeling can be applied to the behavior of a drone swarm, which will allow choosing the optimal behavior of the swarm for different conditions of its activity.
... In [12], the in uence of drone characteristics on the tasks that can be assigned to the swarm is considered. An example is a swarm of aquatic robots designed to remove harmful algae from the water. ...
Control of a drone swarm as a unit requires decentralization and hierarchy. Decentralizing control of the drone swarm is necessary to free the human-operator from having to constantly control the behavior of the drones within the swarm. Hierarchical control of a drone swarm is necessary so that a human-operator can adjust the activity of the swarm as a unit (as a whole). To implement this approach, the control model is proposed. The following separate roles have been identified for the implementation of decentralized hierarchical control of swarm activity: the activity of a separate drone, the activity of a drone- coordinator, and the activity of a human-operator. The control hierarchy consists of a human-operator who controls the change in the behavior of the drone-coordinator. The drone-coordinator controls the changes in the behavior of individual drones in the swarm. Drones in a swarm perform programmed behavior. This approach allows us to consider the control of a drone swarm as an analog of the management of human-workers who perform assigned work. This opens up several possibilities. First, it is possible to use methods of formalizing people’s behavior in social teams. For example, at the level of formal models of performance of their functional duties. Second, formal models can be used for decision-making and optimization for controlling a drone-coordinator in a swarm. Thirdly, computer modeling can be applied to the behavior of a drone swarm, which will allow choosing the optimal behavior of the swarm for different conditions of its activity.
... The relationship between robot size, robot quantity, and congestion was explored recently (Schroeder et al., 2019). Specifically, the balance between the total swarm cost, as a function of robot size and quantity, and interference between vehicles was used to identify the optimal physical size of robots that comprise a swarm. ...
The Defense Advanced Research Projects Agency (DARPA) OFFensive Swarm-Enabled Tactics program's goal of launching 250 unmanned aerial and ground vehicles from a limited sized launch zone was a daunting challenge. The swarm's aerial vehicles were primarily multirotor platforms, which can efficiently be launched en masse. Each field exercise expected the deployment of an even larger swarm. While the launch zone's spatial area increased with each field exercise, the relative space for each vehicle was not necessarily increased, considering the increasing size of the swarm and the vehicles' associated GPS error; however, safe mission deployment and execution were expected. At the same time, achieving the mission goals required maximizing efficiency of the swarm's performance by reducing congestion that blocked vehicles from completing tactic assignments. Congestion analysis conducted before the final field exercise focused on adjusting various constraints to optimize the swarm's deployment without reducing safety. During the field exercise, data was collected that permitted analyzing the number and durations of individual vehicle blockages' impact on the resulting congestion. After the field exercise, additional analyses used the mission plan to validate the use of simulation for analyzing congestion.
... However, a system's collective behavior is not the only consideration when determining a swarm's size. For a given task, several other aspects also influence the selection of the number of swarming agents, such as: (1) system scalability, (2) technical capabilities of the individual agents (e.g., communications range, sensor range, maneuverability, maximum speed, etc.), and (3) financial or logistical constraints (i.e., the number of robots that can be built, stored, and operated given the available resources) (Schroeder et al., 2019). It can therefore be said that selecting the number of robotic units is far from being a trivial task for system designers. ...
... This was done using an exponential function that accounted for both the benefits of inter-agent cooperation and the detriments caused by the additional interference. Subsequently, Schroeder et al. (2019) combined this multi-agent performance strategy with a simple cost model to maximize the performance of their foraging MRS while minimizing the overall system cost by finding the ideal number of robots to be constructed for their system. ...
... Furthermore, as the density increases and we enter the transition phase, the existence of an intricate balance between exploratory and exploitative actions reveals that these emergent properties are strongly dependent on the density. Nonetheless, one should not put aside unavoidable finite-size effects; for instance, those that arise due to the limited number of agents employed within any MRS, usually constrained by financial and logistical challenges (Schroeder et al., 2019;Horsevad et al., 2022a). As such, we must acknowledge the role that a system's size plays, and it appears therefore necessary to complement our density analysis by investigating the effects of swarm size. ...
How does the size of a swarm affect its collective action? Despite being arguably a key parameter, no systematic and satisfactory guiding principles exist to select the number of units required for a given task and environment. Even when limited by practical considerations, system designers should endeavor to identify what a reasonable swarm size should be. Here, we show that this fundamental question is closely linked to that of selecting an appropriate swarm density. Our analysis of the influence of density on the collective performance of a target tracking task reveals different ‘phases’ corresponding to markedly distinct group dynamics. We identify a ‘transition’ phase, in which a complex emergent collective response arises. Interestingly, the collective dynamics within this transition phase exhibit a clear trade-off between exploratory actions and exploitative ones. We show that at any density, the exploration–exploitation balance can be adjusted to maximize the system’s performance through various means, such as by changing the level of connectivity between agents. While the density is the primary factor to be considered, it should not be the sole one to be accounted for when sizing the system. Due to the inherent finite-size effects present in physical systems, we establish that the number of constituents primarily affects system-level properties such as exploitation in the transition phase. These results illustrate that instead of learning and optimizing a swarm’s behavior for a specific set of task parameters, further work should instead concentrate on learning to be adaptive, thereby endowing the swarm with the highly desirable feature of being able to operate effectively over a wide range of circumstances.
... However, a system's collective behavior is not the only consideration when determining a swarm's size. For a given task, several other aspects also influence the selection of the number of swarming agents, such as: (1) system scalability, (2) technical capabilities of the individual agents (e.g., communications range, sensor range, maneuverability, maximum speed, etc.), and (3) financial or logistical constraints (i.e., the number of robots that can be built, stored, and operated given the available resources) (Schroeder et al, 2019). It can therefore be said that selecting the number of robotic units is far from being a trivial task for system designers. ...
... This was done using an exponential function that accounted for both the benefits of inter-agent cooperation and the detriments caused by the additional interference. Subsequently, Schroeder et al (2019) combined this multi-agent performance strategy with a simple cost model to maximize the performance of their foraging MRS while minimizing the overall system cost by finding the ideal number of robots to be constructed for their system. ...
... Furthermore, as the density increases and we enter the transition phase, the existence of an intricate balance between exploratory and exploitative actions reveals that these emergent properties are strongly dependent on the density. Nonetheless, one should not put aside unavoidable finite-size effects; for instance, those that arise due to the limited number of agents employed within any MRS, usually constrained by financial and logistical challenges (Schroeder et al, 2019;Horsevad et al, 2022a). As such, we must acknowledge the role that a system's size plays, and it appears therefore necessary to complement our density analysis by investigating the effects of swarm size. ...
How does the size of a swarm affect its collective action? Despite being arguably a key parameter, no systematic and satisfactory guiding principles exist to select the number of units required for a given task and environment. Even when limited by practical considerations, system designers should endeavor to identify what a reasonable swarm size should be. Here, we show that this fundamental question is closely linked to that of selecting an appropriate swarm density. Our analysis of the influence of density on the collective performance of a target tracking task reveals different `phases' corresponding to markedly distinct group dynamics. We identify a `transition' phase, in which a complex emergent collective response arises. Interestingly, the collective dynamics within this transition phase exhibit a clear trade-off between exploratory actions and exploitative ones. We show that at any density, the exploration-exploitation balance can be adjusted to maximize the system's performance through various means, such as by changing the level of connectivity between agents. While the density is the primary factor to be considered, it should not be the sole one to be accounted for when sizing the system. Due to the inherent finite-size effects present in physical systems, we establish that the number of constituents primarily affects system-level properties such as exploitation in the transition phase. These results illustrate that instead of learning and optimizing a swarm's behavior for a specific set of task parameters, further work should instead concentrate on learning to be adaptive, thereby endowing the swarm with the highly desirable feature of being able to operate effectively over a wide range of circumstances.
... The relationship between robot size, robot quantity, and congestion was explored recently (Schroeder et al., 2019). Specifically, the balance between the total swarm cost, as a function of robot size and quantity, and interference between vehicles was used to identify the optimal physical size of robots that comprise a swarm. ...
The Defense Advanced Research Projects Agency (DARPA) OFFensive Swam-Enabled Tactics program’s goal of launching 250 unmanned aerial and ground vehicles from a limited sized launch zone was a daunting challenge. The swarm’s aerial vehicles were primarily multirotor platforms, which can efficiently be launched en masse. Each field exercise expected the deployment of an even larger swarm. While the launch zone’s spatial area increased with each field exercise, the relative space for each vehicle was not necessarily increased, considering the increasing size of the swarm and the vehicles’ associated GPS error; however, safe mission deployment and execution were expected. At the same time, achieving the mission goals required maximizing the efficiency of the swarm’s performance by reducing congestion that blocked vehicles from completing tactic assignments. Congestion analysis conducted before the final field exercise focused on adjusting various constraints to optimize the swarm’s deployment without reducing safety. During the field exercise, data was collected that permitted analyzing the number and durations of individual vehicle blockages’ impact on the resulting congestion. After the field exercise, additional analyses used the mission plan to validate the use of simulation for analyzing congestion.
... However, a system's collective behavior is not the only consideration when determining its size. For a given task, several other aspects also influence the selection of the number of swarming agents, such as: (1) system scalability (i.e., the capacity of a system to continue functioning properly when the number of its components substantially varies) (Dorigo, Theraulaz, and Trianni, 2021), (2) technical capabilities of the individual agents (e.g., communications range, sensor range, maneuverability, maximum speed, etc.), and (3) financial or logistical constraints (i.e., the number of robots that can be built, stored, and operated given the available resources) (Schroeder, Trease, and Arsie, 2019). ...
... Furthermore, as the density increases, the existence of a complex balance between exploratory and exploitative actions reveals that these emergent properties are strongly dependent on the density. Nonetheless, one should not be put aside unavoidable finite-size effects; for instance, due to the limited number of agents employed within any MRS, usually constrained by financial and logistical challenges (Schroeder, Trease, and Arsie, 2019). As such, the role that a system's size plays must be acknowledged. ...
Can a multi-robot system (MRS) be made to find and track a target that can move faster than any of its component robots? Such tasks have long been considered impossible due to the assumption that the target will always be able to outrun the individual robots. The work done in this thesis shows that this task is in fact, achievable and at its root, boils down to the exploration-exploitation dilemma---the choice a system must make between gathering more information about the environment or making use of the information currently available.
To accomplish the task of tracking a fast-moving non-evasive target, a fully decentralised search and track strategy based on the Particle Swarm Optimisation (PSO) algorithm is used. This strategy is complemented by an adaptive inter-agent repulsion behaviour, used to promote exploration, as well as an adjustable $k$-nearest neighbour communications network, used to tune the system's exploration-exploitation balance. To achieve the more challenging task of tracking an evasive target, the individual agents of the swarming MRS are endowed with a short-term memory, thereby promoting higher levels of exploitation. The two developed strategies are then validated through physical tests using a decentralised swarm of miniature ground robots.
Through both virtual and physical experimentation, an optimum level of connectivity to maximise the MRS's tracking performance is revealed. The origin of this optimum level of connectivity is further traced back to an optimum balance in the amount of exploratory and exploitative actions carried out by the system. The effect of various environmental factors and mission parameters on this optimum, such as the swarm density, number of agents used, and the movement profile of the targets, are also studied. The results presented in this thesis further emphasises the importance of attaining the correct exploration-exploitation balance when developing a swarm strategy. This optimum changes according to the task set for the system and the results presented shed some light on how to tune a swarm's exploration-exploitation dynamics to find this optimum, potentially paving the way for better swarming algorithms to be developed.
... Many FS approaches have been developed with the common aim to find a minimal subset of features that are necessary to describe the IS. Many of these approaches are based on swarm intelligence (SI) Ertenlice and Kalayci (2018), which uses meta-heuristic search algorithms that mimic the behavior and motion of natural objects (Schroeder et al. 2019). These algorithms typically progress in two steps, exploration and exploitation. ...
Inconsistent heterogeneous information systems (IHISs) are predominant nowadays. In the meantime, feature selection (FS) for such systems represents a challenge, requiring more innovative research. In the present article, we introduce a novel FS algorithm, GWNO, to tackle this challenge. The novelty of GWNO stems from combining the powers of both grey wolf optimization (GWO) and rough set theory (RST). GWO is used to search for a minimal feature subset just enough to describe the IHIS, whereas RST is used to design a clever fitness function to guide the search. For validation, GWNO was implemented and heavily tested with a kNN classifier, using seven publicly available IHISs of dimensionalities ranging from 10s of features to 2000+ features. For each IHIS, GWNO first selected the important features and then submitted those features to kNN for classification. The test results were highly impressive, with FS ending in less than 10 iterations and classification accuracy reaching 99%. For performance evaluation, GWNO was compared to eight recently published algorithms of its category on the same seven IHISs. It outperformed them all, in terms of FS speed, number of features selected, and classification accuracy. Specifically, it ended FS first, selected up to 77% less features, and achieved with those fewer features a classification accuracy higher than the competitors. For rigorous and credible results, tenfold cross-validation was used throughout the experiments.
... Rausch et al. [38] investigated scale-free properties of artificial collective systems using simulated robot swarms. Many studies focused on mitigating the negative feedback-loop in foraging robot swarms caused by congestion [51,69,115]. Even without including collisions in simulations, Hecker and Moses [55] observed sub-linear foraging performance as more robots were added in a foraging task simulation. ...
Purpose of Review
We review recent research on swarm robot foraging and contextualize it with foundational work. Recent work can be divided into two complementary camps: self-organizing algorithms that provide practical gains and analytical research focus on theoretical proofs.
Recent Findings
Encouragingly, the convergence between theory and practice is evident in analytical work on the scaling of transportation networks and in behavioral grammars that give formal insight into emergent properties of foraging. Augmented reality has enabled virtual pheromones to be used with hardware, blurring the line between physical and simulation experiments.
Summary
In this review we highlight bio-inspired and self-organizing approaches to swarm foraging and contrast them with approaches that can provide theoretical proofs, but which abstract away important features from foraging in real-world environments.
... • Cost -Manned systems are far more expensive to operate than unmanned systems [12]. ...
... • Coverage -Coverage rates and the ability to maintain constant awareness of the environment are improving as a result of technological advances in sensors and sensory systems [12,13]. ...
... A novel Fly Optimization Algorithm (FOA) was presented by Abidin et al. [103] and was successfully implemented for a team of USVs. This algorithm can be only implemented for small number of units (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24). Cao and Chen [104] proposed an improved artificial bee colony (I-ABC) . ...
Swarm robotic is a field of multi-robotics in which the robot's behavior is inspired from nature. With rapid development in the field of the multi-robotics and the lack of efficacy in traditional centralized controls method, decentralized nature inspired swarm algorithms were introduced to control the swarm behavior. Unmanned surface vehicles (USVs) are marine crafts that they can operate autonomously. Due to their potential in operating in different areas, these vehicles have been used for variety of reason including patrolling, border protection, environmental monitoring and oil spill confrontation. This paper provides a review of the Swarm of USVs, their application, simulation environments and the algorithms that has been used in the past and current projects.
