Engineering Emergence: A Survey on Control in the World of Complex Networks

Complex networks make an enticing research topic that has been increasingly attracting researchers from control systems and various other domains over the last two decades. The aim of this paper was to survey the interest in control related to complex networks research over time since 2000 and to identify recent trends that may generate new research directions. The survey was performed for Web of Science, Scopus, and IEEEXplore publications related to complex networks. Based on our findings, we raised several questions and highlighted ongoing interests in the control of complex networks.publishedVersio

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Cooperative Control of Multiple Wheeled Mobile Robots: Normal and Faulty Situations

Recently, cooperative control of multiple unmanned vehicles has attracted a great deal of attention from scientific, industrial, and military aspects. Groups of unmanned ground, aerial, or marine vehicles working cooperatively lead to many advantages in a variety of applications such as: surveillance, search and exploration, cooperative reconnaissance, environmental monitoring, and cooperative manipulation, respectively. During mission execution, unmanned systems should travel autonomously between different locations, maintain a pre-defined formation shape, avoid collisions of obstacles and also other team members, and accommodate occurred faults and mitigate their negative effect on mission execution. The main objectives of this dissertation are to design novel algorithms for single wheeled mobile robots (WMRs) trajectory tracking, cooperative control and obstacle avoidance of WMRs in fault-free situations. In addition, novel algorithms are developed for fault-tolerant cooperative control (FTCC) with integration of fault detection and diagnosis (FDD) scheme. In normal/fault-free cases, an integrated approach combining input-output feedback linearization and distributed model predictive control (MPC) techniques is designed and implemented on a team of WMRs to accomplish the trajectory tracking as well as the cooperative task. An obstacle avoidance algorithm based on mechanical impedance principle is proposed to avoid potential collisions of surrounding obstacles. Moreover, the proposed control algorithm is implemented to a team of WMRs for pairing with a team of unmanned aerial vehicles (UAVs) for forest monitoring and fire detection applications. When actuator faults occur in one of the robots, two cases are explicitly considered: i) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are re-assigned to the remaining healthy robots to complete the mission with graceful performance degradation. Two methods are used to investigate this case: the Graph Theory, and formulating the FTCC problem as an optimal assignment problem; and ii) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure the controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, an FDD unit using a two-stage Kalman filter (TSKF) to detect and diagnose actuator faults is presented. In case of using any other nonlinear controller in fault-free case rather than MPC, and in case of severe fault occurrence, another FTCC strategy is presented. First, the new reconfiguration is formulated by an optimal assignment problem where each healthy WMR is assigned to a unique place. Second, the new formation can be reconfigured, while the objective is to minimize the time to achieve the new formation within the constraints of the WMRs' dynamics and collision avoidance. A hybrid approach of control parametrization and time discretization (CPTD) and particle swarm optimization (PSO) is proposed to address this problem. Since PSO cannot solve the continuous control inputs, CPTD is adopted to provide an approximate piecewise linearization of the control inputs. Therefore, PSO can be adopted to find the global optimum solution. In all cases, formation operation of the robot team is based on a leader-follower approach, whilst the control algorithm is implemented in a distributed manner. The results of the numerical simulations and real experiments demonstrate the effectiveness of the proposed algorithms in various scenarios

Cooperative Control of Multiple Wheeled Mobile Robots: Normal and Faulty Situations

Recently, cooperative control of multiple unmanned vehicles has attracted a great deal of attention from scientific, industrial, and military aspects. Groups of unmanned ground, aerial, or marine vehicles working cooperatively lead to many advantages in a variety of applications such as: surveillance, search and exploration, cooperative reconnaissance, environmental monitoring, and cooperative manipulation, respectively. During mission execution, unmanned systems should travel autonomously between different locations, maintain a pre-defined formation shape, avoid collisions of obstacles and also other team members, and accommodate occurred faults and mitigate their negative effect on mission execution. The main objectives of this dissertation are to design novel algorithms for single wheeled mobile robots (WMRs) trajectory tracking, cooperative control and obstacle avoidance of WMRs in fault-free situations. In addition, novel algorithms are developed for fault-tolerant cooperative control (FTCC) with integration of fault detection and diagnosis (FDD) scheme. In normal/fault-free cases, an integrated approach combining input-output feedback linearization and distributed model predictive control (MPC) techniques is designed and implemented on a team of WMRs to accomplish the trajectory tracking as well as the cooperative task. An obstacle avoidance algorithm based on mechanical impedance principle is proposed to avoid potential collisions of surrounding obstacles. Moreover, the proposed control algorithm is implemented to a team of WMRs for pairing with a team of unmanned aerial vehicles (UAVs) for forest monitoring and fire detection applications. When actuator faults occur in one of the robots, two cases are explicitly considered: i) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are re-assigned to the remaining healthy robots to complete the mission with graceful performance degradation. Two methods are used to investigate this case: the Graph Theory, and formulating the FTCC problem as an optimal assignment problem; and ii) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure the controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, an FDD unit using a two-stage Kalman filter (TSKF) to detect and diagnose actuator faults is presented. In case of using any other nonlinear controller in fault-free case rather than MPC, and in case of severe fault occurrence, another FTCC strategy is presented. First, the new reconfiguration is formulated by an optimal assignment problem where each healthy WMR is assigned to a unique place. Second, the new formation can be reconfigured, while the objective is to minimize the time to achieve the new formation within the constraints of the WMRs' dynamics and collision avoidance. A hybrid approach of control parametrization and time discretization (CPTD) and particle swarm optimization (PSO) is proposed to address this problem. Since PSO cannot solve the continuous control inputs, CPTD is adopted to provide an approximate piecewise linearization of the control inputs. Therefore, PSO can be adopted to find the global optimum solution. In all cases, formation operation of the robot team is based on a leader-follower approach, whilst the control algorithm is implemented in a distributed manner. The results of the numerical simulations and real experiments demonstrate the effectiveness of the proposed algorithms in various scenarios

A survey on multi-robot coverage path planning for model reconstruction and mapping

There has been an increasing interest in researching, developing and deploying multi-robot systems. This has been driven mainly by: the maturity of the practical deployment of a single-robot system and its ability to solve some of the most challenging tasks. Coverage path planning (CPP) is one of the active research topics that could benefit greatly from multi-robot systems. In this paper, we surveyed the research topics related to multi-robot CPP for the purpose of mapping and model reconstructions. We classified the topics into: viewpoints generation approaches; coverage planning strategies; coordination and decision-making processes; communication mechanism and mapping approaches. This paper provides a detailed analysis and comparison of the recent research work in this area, and concludes with a critical analysis of the field, and future research perspectives

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics