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The accuracy of stockpile estimations is of immense criticality to process optimisation and overall financial decision making within manufacturing operations. Despite well-established correlations between inventory management and profitability, safe deployment of stockpile measurement and inspection activities remain challenging and labour-intensive. This is perhaps owing to a combination of size, shape irregularity as well as the health hazards of cement manufacturing raw materials and products. Through a combination of simulations and real-life assessment within a fully integrated cement plant, this study explores the potential of drones to safely enhance the accuracy of stockpile volume estimations. Different types of LiDAR sensors in combination with different flight trajectory options were fully assessed through simulation whilst mapping representative stockpiles placed in both open and fully confined areas. During the real-life assessment, a drone was equipped with GPS for localisation, in addition to a 1D LiDAR and a barometer for stockpile height estimation. The usefulness of the proposed approach was established based on mapping of a pile with unknown volume in an open area, as well as a pile with known volume within a semi-confined area. Visual inspection of the generated stockpile surface showed strong correlations with the actual pile within the open area, and the volume of the pile in the semi-confined area was accurately measured. Finally, a comparative analysis of cost and complexity of the proposed solution to several existing initiatives revealed its proficiency as a low-cost robotic system within confined spaces whereby visibility, air quality, humidity, and high temperature are unfavourable.
Ahmad Alsayed; Akilu Yunusa-Kaltungo; Mark K. Quinn; Farshad Arvin; Mostafa R. A. Nabawy. Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation. Remote Sensing 2021, 13, 3356 .
AMA StyleAhmad Alsayed, Akilu Yunusa-Kaltungo, Mark K. Quinn, Farshad Arvin, Mostafa R. A. Nabawy. Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation. Remote Sensing. 2021; 13 (17):3356.
Chicago/Turabian StyleAhmad Alsayed; Akilu Yunusa-Kaltungo; Mark K. Quinn; Farshad Arvin; Mostafa R. A. Nabawy. 2021. "Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation." Remote Sensing 13, no. 17: 3356.
Flocking is a social animals’ common behaviour observed in nature. It has a great potential for real-world applications such as exploration in agri-robotics using low-cost robotic solutions. In this paper, an extended model of a self-organised flocking mechanism using heterogeneous swarm system is proposed. The proposed model for swarm robotic systems is a combination of a collective motion mechanism with obstacle avoidance functions, which ensures a collision-free flocking trajectory for the followers. An optimal control model for the leader is also developed to steer the swarm to a desired goal location. Compared to the conventional methods, by using the proposed model, the swarm network has less requirement for power and storage. The feasibility of the proposed self-organised flocking algorithm is validated by realistic robotic simulation software.
Zhe Ban; Junyan Hu; Barry Lennox; Farshad Arvin. Self-Organised Collision-Free Flocking Mechanism in Heterogeneous Robot Swarms. Mobile Networks and Applications 2021, 1 -11.
AMA StyleZhe Ban, Junyan Hu, Barry Lennox, Farshad Arvin. Self-Organised Collision-Free Flocking Mechanism in Heterogeneous Robot Swarms. Mobile Networks and Applications. 2021; ():1-11.
Chicago/Turabian StyleZhe Ban; Junyan Hu; Barry Lennox; Farshad Arvin. 2021. "Self-Organised Collision-Free Flocking Mechanism in Heterogeneous Robot Swarms." Mobile Networks and Applications , no. : 1-11.
Cooperative control of multirobot systems (MRSs) has earned significant research interest over the past two decades due to its potential applications in multidisciplinary engineering problems. In contrast to a single specialized robot, the MRS can be designed to offer flexibility, reconfigurability, robustness to faults, and cost-effectiveness in solving complex and challenging tasks. In this article, we aim to develop a unified cluster formation containment coordination framework for networked robots that can be decomposed into two layers containing the leaders and the followers. According to the proposed methodology, the leader robots are first distributed into a set of distinct and nonoverlapping clusters depending on the positions and priorities of the targets exploiting a game-theoretic rule. Then, they are steered to attain the desired formations around the corresponding targets. Subsequently, the follower robots are made to converge into the convex hull spanned by the leaders of the individual clusters. A prototype search and rescue operation is considered to highlight the usefulness of the proposed coordination framework. Furthermore, real-time hardware experiments were conducted on miniature mobile robots to validate the feasibility of the theoretical results.
Junyan Hu; Parijat Bhowmick; Inmo Jang; Farshad Arvin; Alexander Lanzon. A Decentralized Cluster Formation Containment Framework for Multirobot Systems. IEEE Transactions on Robotics 2021, PP, 1 -20.
AMA StyleJunyan Hu, Parijat Bhowmick, Inmo Jang, Farshad Arvin, Alexander Lanzon. A Decentralized Cluster Formation Containment Framework for Multirobot Systems. IEEE Transactions on Robotics. 2021; PP (99):1-20.
Chicago/Turabian StyleJunyan Hu; Parijat Bhowmick; Inmo Jang; Farshad Arvin; Alexander Lanzon. 2021. "A Decentralized Cluster Formation Containment Framework for Multirobot Systems." IEEE Transactions on Robotics PP, no. 99: 1-20.
Coordination of robot swarms has received significant research interest over the last decade due to its wide real-world applications including precision agriculture, target surveillance, planetary exploration, etc. Many of these practical activities can be formulated as a formation tracking problem. This brief aims to design a robust control strategy for networked robot swarms subjected to nonlinear dynamics and unknown disturbances. Firstly, a robust adaptive formation coordination protocol is proposed for robot swarms, which utilizes only local information for tracking a dynamic target with uncertain maneuvers. A rigorous theoretical proof utilizing the Lyapunov stability approach is then provided to guarantee the control performance. Towards the end, real-time hardware experiments with wheeled mobile robots are conducted to validate the robustness and feasibility of the proposed formation coordination approach.
Junyan Hu; Ali Emre Turgut; Barry Lennox; Farshad Arvin. Robust Formation Coordination of Robot Swarms with Nonlinear Dynamics and Unknown Disturbances: Design and Experiments. IEEE Transactions on Circuits and Systems II: Express Briefs 2021, PP, 1 -1.
AMA StyleJunyan Hu, Ali Emre Turgut, Barry Lennox, Farshad Arvin. Robust Formation Coordination of Robot Swarms with Nonlinear Dynamics and Unknown Disturbances: Design and Experiments. IEEE Transactions on Circuits and Systems II: Express Briefs. 2021; PP (99):1-1.
Chicago/Turabian StyleJunyan Hu; Ali Emre Turgut; Barry Lennox; Farshad Arvin. 2021. "Robust Formation Coordination of Robot Swarms with Nonlinear Dynamics and Unknown Disturbances: Design and Experiments." IEEE Transactions on Circuits and Systems II: Express Briefs PP, no. 99: 1-1.
There are many potential applications of swarm robotic systems in real-world scenarios. In this paper, formation-containment controller design for single-integrator and double-integrator swarm robotic systems with input saturation is investigated. The swarm system contains two types of robots—leaders and followers. A novel control protocol and an implementation algorithm are proposed that enable the leaders to achieve the desired formation via semidefinite programming (SDP) techniques. The followers then converge into the convex hull formed by the leaders simultaneously. In contrast to conventional consensus-based formation control methods, the relative formation reference signal is not required in the real-time data transmission, which provides greater feasibility for implementation on hardware platforms. The effectiveness of the proposed formation-containment control algorithm is demonstrated with both numerical simulations and experiments using real robots that utilize the miniature mobile robot, Mona.
Kefan Wu; Junyan Hu; Barry Lennox; Farshad Arvin. SDP-Based Robust Formation-Containment Coordination of Swarm Robotic Systems with Input Saturation. Journal of Intelligent & Robotic Systems 2021, 102, 1 -16.
AMA StyleKefan Wu, Junyan Hu, Barry Lennox, Farshad Arvin. SDP-Based Robust Formation-Containment Coordination of Swarm Robotic Systems with Input Saturation. Journal of Intelligent & Robotic Systems. 2021; 102 (1):1-16.
Chicago/Turabian StyleKefan Wu; Junyan Hu; Barry Lennox; Farshad Arvin. 2021. "SDP-Based Robust Formation-Containment Coordination of Swarm Robotic Systems with Input Saturation." Journal of Intelligent & Robotic Systems 102, no. 1: 1-16.
This brief proposes a bearing-only collision-free formation coordination strategy for networked heterogeneous robots, where each robot only measures the relative bearings of its neighbors to achieve cooperation. Different from many existing studies that can only guarantee global asymptotic stability (i.e., the formation can only be formed over an infinite settling period), a gradient-descent control protocol is designed to make the robots achieve a target formation within a given finite time. The stability of the multi-robot system is guaranteed via Lyapunov theory, and the convergence time can be defined by users. Moreover, we also present sufficient conditions for collision avoidance. Finally, a simulation case study is provided to verify the effectiveness of the proposed approach.
Kefan Wu; Junyan Hu; Barry Lennox; Farshad Arvin. Finite-Time Bearing-Only Formation Tracking of Heterogeneous Mobile Robots with Collision Avoidance. IEEE Transactions on Circuits and Systems II: Express Briefs 2021, PP, 1 -1.
AMA StyleKefan Wu, Junyan Hu, Barry Lennox, Farshad Arvin. Finite-Time Bearing-Only Formation Tracking of Heterogeneous Mobile Robots with Collision Avoidance. IEEE Transactions on Circuits and Systems II: Express Briefs. 2021; PP (99):1-1.
Chicago/Turabian StyleKefan Wu; Junyan Hu; Barry Lennox; Farshad Arvin. 2021. "Finite-Time Bearing-Only Formation Tracking of Heterogeneous Mobile Robots with Collision Avoidance." IEEE Transactions on Circuits and Systems II: Express Briefs PP, no. 99: 1-1.
This paper presents an open-source simulation platform developed for implementation of both homogeneous and heterogeneous robotic swarm scenarios. BeeGround is a fully modular simulation software that allows for a variety of experimental setups with different robotic platforms and population sizes. Users are able to define environmental conditions, e.g. size, various properties like temperature and humidity, and obstacles arrangements. The swarm controller, the individual's behaviour, is defined with a separate programming script. In this paper, we simulated honeybees aggregation mechanism as a case study to investigate the feasibility of the developed simulation platform. The results demonstrated that the developed platform is a reliable simulation software for implementing multi-agent and swarm robotics scenarios with very large population sizes, e.g. 1000 robots.
Sean Lim; Shiyi Wang; Barry Lennox; Farshad Arvin. BeeGround - An Open-Source Simulation Platform for Large-Scale Swarm Robotics Applications. 2021 7th International Conference on Automation, Robotics and Applications (ICARA) 2021, 75 -79.
AMA StyleSean Lim, Shiyi Wang, Barry Lennox, Farshad Arvin. BeeGround - An Open-Source Simulation Platform for Large-Scale Swarm Robotics Applications. 2021 7th International Conference on Automation, Robotics and Applications (ICARA). 2021; ():75-79.
Chicago/Turabian StyleSean Lim; Shiyi Wang; Barry Lennox; Farshad Arvin. 2021. "BeeGround - An Open-Source Simulation Platform for Large-Scale Swarm Robotics Applications." 2021 7th International Conference on Automation, Robotics and Applications (ICARA) , no. : 75-79.
Optimising a set of parameters for swarm flocking is a tedious task as it requires hand-tuning of the parameters. In this paper, we developed a self-organised flocking mechanism with a swarm of homogeneous robots. The proposed mechanism used deep reinforcement learning to teach the swarm to perform the flocking in a continuous state and action space. Collective motion was represented by a self-organising dynamic model that is based on linear spring-like forces between self-propelled particles in an active crystal. We tuned the inverse rotational and translational damping coefficients of the dynamic model for swarm populations of $N\in \{25,\ 100\}$ E {25, 100} robots. We study the application of reinforcement learning in a centralised multi-agent approach, where we have a global state space matrix that is accessible by actor and critic networks. Furthermore, we showed that our method could train the system to flock regardless of the sparsity of the swarm population, which is a significant result.
Mehmet B. Bezcioglu; Barry Lennox; Farshad Arvin. Self-Organised Swarm Flocking with Deep Reinforcement Learning. 2021 7th International Conference on Automation, Robotics and Applications (ICARA) 2021, 226 -230.
AMA StyleMehmet B. Bezcioglu, Barry Lennox, Farshad Arvin. Self-Organised Swarm Flocking with Deep Reinforcement Learning. 2021 7th International Conference on Automation, Robotics and Applications (ICARA). 2021; ():226-230.
Chicago/Turabian StyleMehmet B. Bezcioglu; Barry Lennox; Farshad Arvin. 2021. "Self-Organised Swarm Flocking with Deep Reinforcement Learning." 2021 7th International Conference on Automation, Robotics and Applications (ICARA) , no. : 226-230.
Swarm robotics is mainly inspired by the collective behaviour of social animals in nature. Among different behaviours such as foraging and flocking performed by social animals; aggregation behaviour is often considered as the most basic and fundamental one. Aggregation behaviour has been studied in different domains for over a decade. In most of these studies, the settings are over-simplified that are quite far from reality. In this paper, we investigate cue-based aggregation behaviour using BEECLUST in a complex environment having two cues –one being the local optimum and the other being the global optimum– with an obstacle between the two cues. The robotic validation of the BEECLUST strategy in a complex environment is the main motivation of this paper. We measured the aggregation size on both cues with and without the obstacle varying the number of robots. The simulations were performed on a custom open-source simulation platform, Bee-Ground, using MONA robots. The results showed that the aggregation behaviour with BEECLUST strategy was able to overcome a certain degree of environmental complexities revealing the robustness of the method. We also verified these results using our stock-flow model.
Shiyi Wang; Ali E. Turgut; Thomas Schmickl; Barry Lennox; Farshad Arvin. Investigation of Cue-Based Aggregation Behaviour in Complex Environments. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021, 18 -36.
AMA StyleShiyi Wang, Ali E. Turgut, Thomas Schmickl, Barry Lennox, Farshad Arvin. Investigation of Cue-Based Aggregation Behaviour in Complex Environments. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2021; ():18-36.
Chicago/Turabian StyleShiyi Wang; Ali E. Turgut; Thomas Schmickl; Barry Lennox; Farshad Arvin. 2021. "Investigation of Cue-Based Aggregation Behaviour in Complex Environments." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 18-36.
Flocking is a common behaviour observed in social animals such as birds and insects, which has received considerable attention in swarm robotics research studies. In this paper, a homogeneous self-organised flocking mechanism was implemented using simulated robots to verify a collective model. We identified and proposed solutions to the current gap between the theoretical model and the implementation with real-world robots. Quantitative experiments were designed with different factors which are swarm population size, desired distance between robots and the common goal force. To evaluate the group performance of the swarm, the average distance within the flock was chosen to show the coherency of the swarm, followed by statistical analysis to investigate the correlation between these factors. The results of the statistical analysis showed that compared with other factors, population size had a significant impact on the swarm flocking performance. This provides guidance on the application with real robots in terms of factors and strategic design.
Zhe Ban; Craig West; Barry Lennox; Farshad Arvin. Self-organised Flocking with Simulated Homogeneous Robotic Swarm. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021, 3 -17.
AMA StyleZhe Ban, Craig West, Barry Lennox, Farshad Arvin. Self-organised Flocking with Simulated Homogeneous Robotic Swarm. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2021; ():3-17.
Chicago/Turabian StyleZhe Ban; Craig West; Barry Lennox; Farshad Arvin. 2021. "Self-organised Flocking with Simulated Homogeneous Robotic Swarm." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 3-17.
In this paper, a novel 3-degree-of-freedom (DOF) nanopositioner was investigated in order to position objects with nanometer scale accuracy. Nanopositioners are used in a variety of real-world applications, e.g. biomedical technology and nanoassembly. In this work, a nanopositioner was firstly designed with the flexure diaphragm guider, capacitive sensors and walking piezoelectric actuators. The specifically designed monolithic flexure diaphragm guider was able to significantly restrict motions in the other unwanted directions. The walking piezoelectric actuator can enable the developed nanopositioner to have nanometer scale positioning accuracy and a large travel range. Then a closed-loop sliding mode control strategy was developed to overcome the effect of the actuator’s speed nonlinearity and its stability was analysed based on Lyapunov theory. Finally, experiments focused on coupling displacement and point-to-point movement were conducted. The observed results revealed that the ratio of coupling displacement to Z displacement was less than 0.1%, which means that the coupling displacement was less than 120 nm during the Z direction travel range of the nanopositioner from −80 μm to 80 μm. Moreover, the positioning accuracy in the Z direction of point-to-point movement was within 10 nm and the dynamic response settled within 0.2 s. Therefore, the experimental results showed that the novel piezoelectric driven nanopositioner has excellent performance in terms of coupling displacement and nanometer scale accuracy for point-to-point movement.
Peng-Zhi Li; De-Fu Zhang; Barry Lennox; Farshad Arvin. A 3-DOF piezoelectric driven nanopositioner: Design, control and experiment. Mechanical Systems and Signal Processing 2021, 155, 107603 .
AMA StylePeng-Zhi Li, De-Fu Zhang, Barry Lennox, Farshad Arvin. A 3-DOF piezoelectric driven nanopositioner: Design, control and experiment. Mechanical Systems and Signal Processing. 2021; 155 ():107603.
Chicago/Turabian StylePeng-Zhi Li; De-Fu Zhang; Barry Lennox; Farshad Arvin. 2021. "A 3-DOF piezoelectric driven nanopositioner: Design, control and experiment." Mechanical Systems and Signal Processing 155, no. : 107603.
This paper presents a sensor-level mapless collision avoidance algorithm for use in mobile robots that map raw sensor data to linear and angular velocities and navigate in an unknown environment without a map. An efficient training strategy is proposed to allow a robot to learn from both human experience data and self-exploratory data. A game format simulation framework is designed to allow the human player to tele-operate the mobile robot to a goal and human action is also scored using the reward function. Both human player data and self-playing data are sampled using prioritized experience replay algorithm. The proposed algorithm and training strategy have been evaluated in two different experimental configurations: Environment 1, a simulated cluttered environment, and Environment 2, a simulated corridor environment, to investigate the performance. It was demonstrated that the proposed method achieved the same level of reward using only 16% of the training steps required by the standard Deep Deterministic Policy Gradient (DDPG) method in Environment 1 and 20% of that in Environment 2. In the evaluation of 20 random missions, the proposed method achieved no collision in less than 2 h and 2.5 h of training time in the two Gazebo environments respectively. The method also generated smoother trajectories than DDPG. The proposed method has also been implemented on a real robot in the real-world environment for performance evaluation. We can confirm that the trained model with the simulation software can be directly applied into the real-world scenario without further fine-tuning, further demonstrating its higher robustness than DDPG. The video and code are available: https://youtu.be/BmwxevgsdGc https://github.com/hanlinniu/turtlebot3_ddpg_collision_avoidance
Hanlin Niu; Ze Ji; Farshad Arvin; Barry Lennox; Hujun Yin; Joaquin Carrasco. Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player. 2021 IEEE/SICE International Symposium on System Integration (SII) 2021, 144 -149.
AMA StyleHanlin Niu, Ze Ji, Farshad Arvin, Barry Lennox, Hujun Yin, Joaquin Carrasco. Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player. 2021 IEEE/SICE International Symposium on System Integration (SII). 2021; ():144-149.
Chicago/Turabian StyleHanlin Niu; Ze Ji; Farshad Arvin; Barry Lennox; Hujun Yin; Joaquin Carrasco. 2021. "Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player." 2021 IEEE/SICE International Symposium on System Integration (SII) , no. : 144-149.
Ahmad Alsayed; Mostafa R. Nabawy; Akilu Yunusa-Kaltungo; Farshad Arvin; Mark K. Quinn. Towards Developing an Aerial Mapping System for Stockpile Volume Estimation in Cement Plants. AIAA Scitech 2021 Forum 2021, 1 .
AMA StyleAhmad Alsayed, Mostafa R. Nabawy, Akilu Yunusa-Kaltungo, Farshad Arvin, Mark K. Quinn. Towards Developing an Aerial Mapping System for Stockpile Volume Estimation in Cement Plants. AIAA Scitech 2021 Forum. 2021; ():1.
Chicago/Turabian StyleAhmad Alsayed; Mostafa R. Nabawy; Akilu Yunusa-Kaltungo; Farshad Arvin; Mark K. Quinn. 2021. "Towards Developing an Aerial Mapping System for Stockpile Volume Estimation in Cement Plants." AIAA Scitech 2021 Forum , no. : 1.
Autonomous exploration is an important application of multi-vehicle systems, where a team of networked robots are coordinated to explore an unknown environment collaboratively. This technique has earned significant research interest due to its usefulness in search and rescue, fault detection and monitoring, localization and mapping, etc. In this paper, a novel cooperative exploration strategy is proposed for multiple mobile robots, which reduces the overall task completion time and energy costs compared to conventional methods. To efficiently navigate the networked robots during the collaborative tasks, a hierarchical control architecture is designed which contains a high-level decision making layer and a low-level target tracking layer. The proposed cooperative exploration approach is developed using dynamic Voronoi partitions, which minimizes duplicated exploration areas by assigning different target locations to individual robots. To deal with sudden obstacles in the unknown environment, an integrated deep reinforcement learning based collision avoidance algorithm is then proposed, which enables the control policy to learn from human demonstration data and thus improve the learning speed and performance. Finally, simulation and experimental results are provided to demonstrate the effectiveness of the proposed scheme.
Junyan Hu; Hanlin Niu; Joaquin Carrasco; Barry Lennox; Farshad Arvin. Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology 2020, 69, 14413 -14423.
AMA StyleJunyan Hu, Hanlin Niu, Joaquin Carrasco, Barry Lennox, Farshad Arvin. Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology. 2020; 69 (12):14413-14423.
Chicago/Turabian StyleJunyan Hu; Hanlin Niu; Joaquin Carrasco; Barry Lennox; Farshad Arvin. 2020. "Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning." IEEE Transactions on Vehicular Technology 69, no. 12: 14413-14423.
The robotic shepherding problem has earned significant research interest over the last few decades due to its potential application in precision agriculture. In this paper, we first modeled the sheep flocking behavior using adaptive protocols and artificial potential field methods. Then we designed a coordination algorithm for the robotic dogs. An occlusion-based motion control strategy was proposed to herd the sheep to the desired location. Compared to formation based techniques, the proposed control strategy provides more flexibility and efficiency when herding a large number of sheep. Simulation and lab-based experiments, using real robots and global vision-based tracking system, were carried out to validate the effectiveness of the proposed approach.
Junyan Hu; Ali Emre Turgut; Tomas Krajnik; Barry Lennox; Farshad Arvin. Occlusion-Based Coordination Protocol Design for Autonomous Robotic Shepherding Tasks. IEEE Transactions on Cognitive and Developmental Systems 2020, PP, 1 -1.
AMA StyleJunyan Hu, Ali Emre Turgut, Tomas Krajnik, Barry Lennox, Farshad Arvin. Occlusion-Based Coordination Protocol Design for Autonomous Robotic Shepherding Tasks. IEEE Transactions on Cognitive and Developmental Systems. 2020; PP (99):1-1.
Chicago/Turabian StyleJunyan Hu; Ali Emre Turgut; Tomas Krajnik; Barry Lennox; Farshad Arvin. 2020. "Occlusion-Based Coordination Protocol Design for Autonomous Robotic Shepherding Tasks." IEEE Transactions on Cognitive and Developmental Systems PP, no. 99: 1-1.
Swarm Intelligence (SI) is a popular multi-agent framework that has been originally inspired by swarm behaviors observed in natural systems, such as ant and bee colonies. In a system designed after swarm intelligence, each agent acts autonomously, reacts on dynamic inputs, and, implicitly or explicitly, works collaboratively with other swarm members without a central control. The system as a whole is expected to exhibit global patterns and behaviors. Although well-designed swarms can show advantages in adaptability, robustness, and scalability, it must be noted that SI system haven’t really found their way from lab demonstrations to real-world applications, so far. This is particularly true for embodied SI, where the agents are physical entities, such as in swarm robotics scenarios. In this paper, we start from these observations, outline different definitions and characterizations, and then discuss present challenges in the perspective of future use of swarm intelligence. These include application ideas, research topics, and new sources of inspiration from biology, physics, and human cognition. To motivate future applications of swarms, we make use of the notion of cyber-physical systems (CPS). CPSs are a way to encompass the large spectrum of technologies including robotics, internet of things (IoT), Systems on Chip (SoC), embedded systems, and so on. Thereby, we give concrete examples for visionary applications and their challenges representing the physical embodiment of swarm intelligence in autonomous driving and smart traffic, emergency response, environmental monitoring, electric energy grids, space missions, medical applications, and human networks. We do not aim to provide new solutions for the swarm intelligence or CPS community, but rather build a bridge between these two communities. This allows us to view the research problems of swarm intelligence from a broader perspective and motivate future research activities in modeling, design, validation/verification, and human-in-the-loop concepts.
Melanie Schranz; Gianni A. Di Caro; Thomas Schmickl; Wilfried Elmenreich; Farshad Arvin; Ahmet Şekercioğlu; Micha Sende. Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends. Swarm and Evolutionary Computation 2020, 60, 100762 .
AMA StyleMelanie Schranz, Gianni A. Di Caro, Thomas Schmickl, Wilfried Elmenreich, Farshad Arvin, Ahmet Şekercioğlu, Micha Sende. Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends. Swarm and Evolutionary Computation. 2020; 60 ():100762.
Chicago/Turabian StyleMelanie Schranz; Gianni A. Di Caro; Thomas Schmickl; Wilfried Elmenreich; Farshad Arvin; Ahmet Şekercioğlu; Micha Sende. 2020. "Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends." Swarm and Evolutionary Computation 60, no. : 100762.
Swarm robotics focuses on decentralised control of large numbers of simple robots with limited capabilities. Decentralised control in a swarm system requires a reliable communication link between the individuals that is able to provide linear and angular distances between the individuals—Range & Bearing. This study presents the development of an open-source, low-cost communication module which can be attached to miniature sized robots; e.g., Mona. In this study, we only focused on bearing estimation to mathematically model the bearings of neighbouring robots through systematic experiments using real robots. In addition, the model parameters were optimised using a genetic algorithm to provide a reliable and precise model that can be applied for all robots in a swarm. For further investigation and improvement of the system, an additional layer of optimisation on the hardware layout was implemented. The results from the optimisation suggested a new arrangement of the sensors with slight angular displacements on the developed board. The precision of bearing was significantly improved by optimising in both software level and re-arrangement of the sensors’ positions on the hardware layout.
Zheyu Liu; Craig West; Barry Lennox; Farshad Arvin. Local Bearing Estimation for a Swarm of Low-Cost Miniature Robots. Sensors 2020, 20, 1 .
AMA StyleZheyu Liu, Craig West, Barry Lennox, Farshad Arvin. Local Bearing Estimation for a Swarm of Low-Cost Miniature Robots. Sensors. 2020; 20 (11):1.
Chicago/Turabian StyleZheyu Liu; Craig West; Barry Lennox; Farshad Arvin. 2020. "Local Bearing Estimation for a Swarm of Low-Cost Miniature Robots." Sensors 20, no. 11: 1.
Pheromones are chemical substances released into the environment by an individual animal, which elicit stereotyped behaviours widely found across the animal kingdom. Inspired by the effective use of pheromones in social insects, pheromonal communication has been adopted to swarm robotics domain using diverse approaches such as alcohol, RFID tags and light. COSΦ is one of the light-based artificial pheromone systems which can emulate realistic pheromones and environment properties through the system. This article provides a significant improvement to the state-of-the-art by proposing a novel artificial pheromone system that simulates pheromones with environmental effects by adopting a model of spatio-temporal development of pheromone derived from a flow of fluid in nature. Using the proposed system, we investigated the collective behaviour of a robot swarm in a bio-inspired aggregation scenario, where robots aggregated on a circular pheromone cue with different environmental factors, that is, diffusion and pheromone shift. The results demonstrated the feasibility of the proposed pheromone system for use in swarm robotic applications.
Seongin Na; Yiping Qiu; Ali E Turgut; Jiří Ulrich; Tomáš Krajník; Shigang Yue; Barry Lennox; Farshad Arvin. Bio-inspired artificial pheromone system for swarm robotics applications. Adaptive Behavior 2020, 1 .
AMA StyleSeongin Na, Yiping Qiu, Ali E Turgut, Jiří Ulrich, Tomáš Krajník, Shigang Yue, Barry Lennox, Farshad Arvin. Bio-inspired artificial pheromone system for swarm robotics applications. Adaptive Behavior. 2020; ():1.
Chicago/Turabian StyleSeongin Na; Yiping Qiu; Ali E Turgut; Jiří Ulrich; Tomáš Krajník; Shigang Yue; Barry Lennox; Farshad Arvin. 2020. "Bio-inspired artificial pheromone system for swarm robotics applications." Adaptive Behavior , no. : 1.
The piezoelectric actuator is indispensable for driving the micro-manipulator. In this paper, a simplified interval type-2 (IT2) fuzzy system is proposed for hysteresis modelling and feedforward control of a piezoelectric actuator. The partial derivative of the output of IT2 fuzzy system with respect to the modelling parameters can be analytically computed with the antecedent part of IT2 fuzzy rule specifically designed. In the experiments, gradient based optimization was used to identify the IT2 fuzzy hysteresis model. Results showed that the maximum error of model identification is 0.42% with only 3 developed IT2 fuzzy rules. Moreover, the model validation was conducted to demonstrate the generalization performance of the identified model. Based on the analytic inverse of the developed model, feedforward control experiment for tracking sinusoidal trajectory of 20 Hz was carried out. As a result, the hysteresis effect of the piezoelectric actuator was reduced with the maximum tracking error being 4.6%. Experimental results indicated an improved performance of the proposed IT2 fuzzy system for hysteresis modelling and feedforward control of the piezoelectric actuator.
Peng-Zhi Li; De-Fu Zhang; Jun-Yan Hu; Barry Lennox; Farshad Arvin. Hysteresis Modelling and Feedforward Control of Piezoelectric Actuator Based on Simplified Interval Type-2 Fuzzy System. Sensors 2020, 20, 2587 .
AMA StylePeng-Zhi Li, De-Fu Zhang, Jun-Yan Hu, Barry Lennox, Farshad Arvin. Hysteresis Modelling and Feedforward Control of Piezoelectric Actuator Based on Simplified Interval Type-2 Fuzzy System. Sensors. 2020; 20 (9):2587.
Chicago/Turabian StylePeng-Zhi Li; De-Fu Zhang; Jun-Yan Hu; Barry Lennox; Farshad Arvin. 2020. "Hysteresis Modelling and Feedforward Control of Piezoelectric Actuator Based on Simplified Interval Type-2 Fuzzy System." Sensors 20, no. 9: 2587.
Junyan Hu; Parijat Bhowmick; Farshad Arvin; Alexander Lanzon; Barry Lennox. Cooperative Control of Heterogeneous Connected Vehicle Platoons: An Adaptive Leader-Following Approach. IEEE Robotics and Automation Letters 2020, 5, 977 -984.
AMA StyleJunyan Hu, Parijat Bhowmick, Farshad Arvin, Alexander Lanzon, Barry Lennox. Cooperative Control of Heterogeneous Connected Vehicle Platoons: An Adaptive Leader-Following Approach. IEEE Robotics and Automation Letters. 2020; 5 (2):977-984.
Chicago/Turabian StyleJunyan Hu; Parijat Bhowmick; Farshad Arvin; Alexander Lanzon; Barry Lennox. 2020. "Cooperative Control of Heterogeneous Connected Vehicle Platoons: An Adaptive Leader-Following Approach." IEEE Robotics and Automation Letters 5, no. 2: 977-984.