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The purpose of this paper is to propose a distributed unmanned aerial vehicle (UAV) swarm control method to ensure safety and obstacle avoidance during swarm flight and realize effective guidance.

This paper proposes a distributed UAV swarm control framework with limited interaction. UAVs in the swarm realize the selection of limited interactive neighbors according to the random line of sight and limited field of view. The designed interaction force and obstacle avoidance mechanism are combined to ensure the safety of UAVs and avoid collisions and obstacles. Informed UAVs are deployed to guide the swarm to move in the desired direction.

The proposed distributed swarm control framework achieves high safety of swarm motion and the participation of informed UAVs is conducive to the guidance of the UAV swarm. Simulation results demonstrate the feasibility and effectiveness of the proposed approach.

The UAV swarm control method developed in this paper can be applied to the practice of UAV swarm control.

A distributed UAV swarm control method is proposed to ensure the effective control of the consistency and safety of swarm motion.

The purpose of this paper is to demonstrate a novel use of a generalized Lennard‐Jones (LJ) force law in Physicomimetics, combined with offline evolutionary learning, for the control of swarms of robots moving through obstacle fields towards a goal. The paper then extends the paradigm to demonstrate the utility of a real‐time online adaptive approach named distributed agent evolution with dynamic adaptation to local unexpected scenarios (DAEDALUS).

To achieve the best performance, the parameters of the force law used in the Physicomimetics approach are optimized, using an evolutionary algorithm (EA) (offline learning). A weighted fitness function is utilized consisting of three components: a penalty for collisions, lack of swarm cohesion, and robots not reaching the goal. Each robot of the swarm is then given a slightly mutated copy of the optimized force law rule set found with offline learning and the robots are introduced to a more difficult environment. The online learning framework (DAEDALUS) is used for swarm adaptation in this more difficult environment.

The novel use of the generalized LJ force law combined with an EA surpasses the prior state‐of‐the‐art in the control of swarms of robots moving through obstacle fields. In addition, the DAEDALUS framework allows the swarms of robots to not only learn and share behavioral rules in changing environments (in real time), but also to learn the proper amount of behavioral exploration that is appropriate.

There are significant issues that arise with respect to “wall following methods” and “local minimum trap” problems. “Local minimum trap” problems have been observed in this paper, but this issue is not addressed in detail. The intention is to explore other approaches to develop more robust adaptive algorithms for online learning. It is believed that the learning of the proper amount of behavioral exploration can be accelerated.

In order to provide meaningful comparisons, this paper provides a more complete set of metrics than prior papers in this area. The paper examines the number of collisions between robots and obstacles, the distribution in time of the number of robots that reach the goal, and the connectivity of the formation as it moves.

This paper addresses the difficult task of moving a large number of robots in formation through a large number of obstacles. The important real‐world constraint of “obstructed perception” is modeled. The obstacle density is approximately three times the norm in the literature. The paper shows how concepts from population genetics can be used with swarms of agents to provide fast online adaptive learning in these challenging environments. In addition, this paper also presents a more complete set of metrics of performance.

Swarm controlled emergence is proposed as an approach to control emergent effects in (artificial) swarms. The method involves the introduction of specific control agents into the swarm systems. Control agents behave similar to the normal agents and do not directly influence the behavior of the normal agents. The specific design of the control agents depends on the particular swarm system considered. The aim of this paper is to apply the method to ant clustering. Ant clustering, as an emergent effect, can be observed in nature and has inspired the design of several technical systems, e.g. moving robots, and clustering algorithms.

Different types of control agents for that ant clustering model are designed by introducing slight changes to the behavioural rules of the normal agents. The clustering behaviour of the resulting swarms is investigated by extensive simulation studies.

It is shown that complex behavior can emerge in systems with two types of agents (normal agents and control agents). For a particular behavior of the control agents, an interesting swarm size dependent effect was found. The behaviour prevents clustering when the number of control agents is large, but leads to stronger clustering when the number of control agents is relatively small.

Although swarm controlled emergence is a general approach, in the experiments of this paper the authors concentrate mainly on ant clustering. It remains for future research to investigate the application of the method in other swarm systems. Swarm controlled emergence might be applied to control emergent effects in computing systems that consist of many autonomous components which make decentralized decisions based on local information.

The particular finding, that certain behaviours of control agents can lead to stronger clustering, can help to design improved clustering algorithms by using heterogeneous swarms of agents.

In general, the control of (unwanted) emergent effects in artificial systems is an important problem. However, to date not much research has been done on this topic. This paper proposes a new approach and opens a different research direction towards future control principles for self‐organized systems that consist of a large number of autonomous components.

The purpose of this paper is to describe a multi‐robot solution to the problem of chemical source localization, in which a team of inexpensive, simple vehicles with short‐range, low‐power sensing, communication, and processing capabilities trace a chemical plume to its source emitter

The source localization problem is analyzed using computational fluid dynamics simulations of airborne chemical plumes. The analysis is divided into two parts consisting of two large experiments each: the first part focuses on the issues of collaborative control, and the second part demonstrates how task performance is affected by the number of collaborating robots. Each experiment tests a key aspect of the problem, e.g. effects of obstacles, and defines performance metrics that help capture important characteristics of each solution.

The new empirical simulations confirmed previous theoretical predictions: a physics‐based approach is more effective than the biologically inspired methods in meeting the objectives of the plume‐tracing mission. This gain in performance is consistent across a variety of plume and environmental conditions. This work shows that high success rate can be achieved by robots using strictly local information and a fully decentralized, fault‐tolerant, and reactive control algorithm.

This is the first paper to compare a physics‐based approach against the leading alternatives for chemical plume tracing under a wide variety of fluid conditions and performance metrics. This is also the first presentation of the algorithms showing the specific mechanisms employed to achieve superior performance, including the underlying fluid and other physics principles and their numerical implementation, and the mechanisms that allow the practitioner to duplicate the outstanding performance of this approach under conditions of many robots navigating through obstacle‐dense environments.

The chapter relates to advanced management of large distributed dynamic systems in unpredictable and crisis situations. It briefs the DARPA Mosaic Warfare concept and shows its possible expression under SGT together with exemplary solutions for such tasks as grouping of scattered elements into more powerful forces with unified control, and observation and elimination of dangerous elements by collective operation of causal forces around them. Of practical importance may be mosaics-related approaches using massive robotics. It is shown in SGL how easy to assemble teams of UCAVs for intelligent swarming, self-restructuring and observing territory with collection, distribution and impacting of targets discovered. Another SGL scenario organizes automatic fight of aerial swarm with other group/swarm, autonomously and without external control. It is also shown how broken into pieces platoon of unmanned vehicles, due to situations on roads, is self-recomposing into normal platoon chain again, with vehicles symbolically considered as mosaic tiles.

This paper describes the design of a scalable bio‐mimetic framework that addresses several key issues of autonomous agents in the functional management domain of complex Ubiquitous Service‐Oriented Networks.We propose an autonomous network service management platform ‐ SwarmingNet, which is motivated by observations of the swarm intelligence in biological systems (e.g., Termite, Ant/Bees colonies, or Locusts ). In this SwarmingNet architecture, the required network service processes are implemented by a group of highly diverse and autonomic objects. These objects are called TeleService Solons (TSSs) as elements of TeleService Holons (TSHs), analoguous to individual insects as members of the whole colony. A single TSS is only able to pursue simple behaviors and interactions with local neighbors, on the contrary, a group of TSSs have the capabilities of fulfilling the complex tasks relating to service discovery and service activation.We simulate a service configuration process for a Multimedia Messaging Service, and a performance comparison between the bio‐agents and normal agents is analyzed. Finally, we conclude that through bio‐swarming intelligence behaviors, this infrastructure develops the enhanced self‐X capabilities which give IP networks advantages of instinctive compatibility, efficiency and scalability.

This study aims to investigate the rule‐based decentralised control framework for a swarm of UAVs carrying out a cooperative ground target engagement mission scenario.

This study is to investigate the rule‐based decentralised control framework for missions which require high‐level cooperation between team members. The design of the authors’ control strategy is based on agent‐level interactions. Different to a centralized task assignment algorithm, the cooperation of the agents is entirely implicit. The behaviour of the UAVs is governed by rule sets which ultimately lead to cooperation at a system level. The information theoretic measures are adopted to estimate the value of possible future actions. The prediction model is further considered to enhance the team performance in the scenario where there are tight coupled task constraints.

The simulation study evaluates the performance of the decentralised controller and compares it with a centralised controller quantitatively. The results show that the proposed approach leads to a highly cooperative performance of the group without the need for a centralised control authority. The performance of the decentralised control depends on the complexity of the coupled task constraints. It can be improved by using a prediction model to provide information such as the intentions of the neighbours that is not available locally.

The achievable performance of the decentralised control was considered to be low due to the absence of communication and little global coordinating information. This study demonstrated that the decentralised control can achieve highly cooperative performance. The achievable performance is related to the complexity of the coupled constraints and the accuracy of the prediction model.

In light of the current international concerns with security and terrorism, interest is increasing on the topic of using robot swarms to locate the source of chemical hazards. The purpose of this paper is to place this task, called chemical plume tracing (CPT), in the context of fluid dynamics.

This paper provides a foundation for CPT based on the physics of fluid dynamics. The theoretical approach is founded upon source localization using the divergence theorem of vector calculus, and the fundamental underlying notion of the divergence of the chemical mass flux. A CPT algorithm called fluxotaxis is presented that follows the gradient of this mass flux to locate a chemical source emitter.

Theoretical results are presented confirming that fluxotaxis will guide a robot swarm toward chemical sources, and away from misleading chemical sinks. Complementary empirical results demonstrate that in simulation, a swarm of fluxotaxis‐guided mobile robots rapidly converges on a source emitter despite obstacles, realistic vehicle constraints, and flow regimes ranging from laminar to turbulent. Fluxotaxis outperforms the two leading competitors, and the theoretical results are confirmed experimentally. Furthermore, initial experiments on real robots show promise for CPT in relatively uncontrolled indoor environments.

A physics‐based approach is shown to be a viable alternative to existing mainly biomimetic approaches to CPT. It has the advantage of being analyzable using standard physics analysis methods.

The fluxotaxis algorithm for CPT is shown to be “correct” in the sense that it is guaranteed to point toward a true source emitter and not be fooled by fluid sinks. It is experimentally (in simulation), and in one case also theoretically, shown to be superior to its leading competitors at finding a source emitter in a wide variety of challenging realistic environments.

The aim of this paper is to provide a review of recent developments in the application of swarm intelligence to robotics.

This paper initially considers swarm intelligence and then discusses its application to robotics through reference to a number of recent research programmes.

Based on the principles of swarm intelligence, which is derived from the swarming behaviour of biological entities, swarm robotics research is widespread but still at an early stage. Much aims to gain an understanding of biological swarming and apply it to autonomous, mobile multi‐robot systems. European activities are particularly strong and several large, collaborative projects are underway. Research in the USA has a military bias and much is funded by defence agencies.

The paper provides an up‐to‐date insight into swarm robot research and development.

The requirement of robust cooperative control is essential to achieve consensus between unmanned aerial vehicles (UAVs) operating in swarm formation. Often the performance of these swarm formations is affected by wind gust disturbances. This study proposes an effective robust consensus protocol, which will ensure the UAVs in swam formation to collectively meet the desired objective in real-time scenario.

In this work, the swarm of UAVs are modeled as multiagent systems by using the concepts of algebraic graph theory. To address the challenges of a complex and dynamic environment, an adaptive sliding mode control (SMC)-based consensus protocol is proposed. The closed loop stability analysis is established through Lyapunov theory.

The efficacy of the discussed robust consensus controller is analyzed through numerical simulations. Further, the quantitative analysis using Monte-Carlo simulations validates performance of the proposed robust consensus protocol. The presented consensus protocol can be easily implementable as robust flight controller for swarm of UAVs. Also, as the consensus theory is based on the algebraic graph theory, the proposed design is scalable for a large number of UAVs in swarm formation.

The proposed adaptive SMC achieves robust consensus of longitudinal dynamics states between all the UAVs by mitigating the effects of wind gust disturbances. Also, the adaptive SMC offers chattering-free control efforts.