Swarm formation morphing for congestion-aware collision avoidance

The focus of this work is to present a novel methodology for optimal distribution of a swarm formation on either side of an obstacle, when evading the obstacle, to avoid overpopulation on the sides to reduce the agents' waiting delays, resulting in a reduced overall mission time and lower energy consumption. To handle this, the problem is divided into two main parts: 1) the disturbance phase: how to morph the formation optimally to avoid the obstacle in the least possible time in the situation at hand, and 2) the convergence phase: how to optimally resume the intended formation shape once the threat of potential collision has been eliminated. For the first problem, we develop a methodology which tests different formation morphing combinations and finds the optimal one, by utilizing trajectory, velocity, and coordinate information, to bypass the obstacle. For the second problem, we utilize thin-plate splines (TPS) inspired temperature function minimization method to bring the agents back from the distorted formation into the desired formation in an optimal manner, after collision avoidance has been successfully performed. Experimental results show that, in the considered test scenario, the proposed approach results in substantial energy savings as compared with the traditional methods

Towards a swarm robotic approach for cooperative object recognition

Social insects have inspired the behaviours of swarm robotic systems for the last 20 years. Interactions of the simple individuals in these swarms form solutions to relatively complex problems. A novel swarm robotic method is investigated for future robotic cooperative object recognition tasks. Previous multi-agent systems involve cameras and image analyses to identify objects. They cooperate only to improve their hypotheses of the shape's identity. The system proposed uses agents whose interactions with each other around the physical boundaries of the object's shape allow the distinguishing features found. The agents are a physical embodiment of the vision system, making them suitable for environments where it would not be possible to use a camera. A Simplified Hexagonal Model was developed to simulate and examine the strategies. The hexagonal cells of which can be empty, contain an agent (hBot) or part of an object shape. Initially the hBots are required to identify the valid object shapes from a set of two types of known shapes. To do this the hBots change state when in contact with an object and when touching other hBots of the same state level, where some states are only achieved when neighbouring certain object shapes. The agents are oblivious, anonymous and homogeneous. They also do not know their position or orientation and cannot distinguish between object shapes alone due to their limited sensor range. Further work increased the number of object shapes to provide a range of scenarios

Task-Oriented Communication Design in Cyber-Physical Systems: A Survey on Theory and Applications

Communication system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel. However, in the era of cyber-physical systems, the effectiveness of communications is not dictated simply by the bit rate, but most importantly by the efficient completion of the task in hand, e.g., controlling remotely a robot, automating a production line or collaboratively sensing through a drone swarm. In parallel, it is projected that by 2023, half of the worldwide network connections will be among machines rather than humans. In this context, it is crucial to establish a new paradigm for designing communication strategies for multi-agent cyber-physical systems. This is a daunting task, since it requires a combination of principles from information, communication, control theories and computer science in order to formalize a general framework for task-oriented communication designs. In this direction, this paper reviews and structures the relevant theoretical work across a wide range of scientific communities. Subsequently, it proposes a general conceptual framework for task-oriented communication design, along with its specializations according to targeted use cases. Furthermore, it provides a survey of relevant contributions in dominant applications, such as industrial internet of things, multi-unmanned aerial vehicle (UAV) systems, autonomous vehicles, distributed learning systems, smart manufacturing plants, 5G and beyond self-organizing networks, and tactile internet. Finally, this paper also highlights the most important open research topics from both the theoretical framework and application points of view

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Swarm-based planning and control of robotic functions

University of Technology, Sydney. Faculty of Engineering and Information Technology.Basic issues with a robotic task that requires multiple mobile robots moving in formations are to assemble at an initial point in the work space for establishing a desired formation, to maintain the formation while moving, to avoid obstacles by occasionally splitting/deforming and then re-establishing the formation, and to change the shape of the formation upon requests to accommodate new tasks or safety conditions. In the literature, those issues have been often addressed separately. This research proposes a generic framework that allows for tackling these issues in an integrated manner in the optimal formation planning and control context. Within this proposed framework, a leader robot will be assigned and the path for the leader is obtained by utilising a modified A* search together with a vector approach, and then smoothed out to reduce the number of turns and to satisfy the dynamic and kinematic constraints of mobile robots. Next, a reference trajectory is generated for the leader robot. Based on the formation configuration and the workspace environment, desired trajectories for follower robots in the group are obtained. At the lowest level, each robot tracks its own trajectory using a unified tracking controller. The problem of formation initialisation, in which a group of robots, initially scattering in the workspace, is deployed to get into a desired formation shape, is dealt with by using a Discrete Particle Swarm Optimisation (DPSO) technique incorporated with a behaviour-based strategy. The proposed technique aims to optimally assign desired positions for each robot in the formation by minimisation of a cost function associated with the predefined formation shape. Once each robot has been assigned with a desired position, a search scheme is implemented to obtain a collision free trajectory for each robot to establish the formation. Towards optimal maintenance of the motion patterns, the path that has been obtained for robots in the group by using the modified A* search, is further adjusted. For this, the Particle Swarm Optimisation (PSO) technique is proposed to minimise a cost function involving global motion of the formation, with the main objective of preventing unnecessary changes in the follower robot trajectories when avoiding obstacles. A PSO formation motion planning algorithm is proposed to search for motion commands for each robot. This algorithm can be used to initialise the formation or to navigate the formation to its target. The proposed PSO motion planning method is able to maintain the formation subject to the kinematic and velocity constraints. Analytical work of the thesis is validated by extensive simulation of multiple differential drive wheeled mobile robots based on their kinematic models. The techniques proposed in this thesis are also experimentally tested, in part, on two Amigo mobile robots

Stupid robot tricks : a behavior-based distributed algorithm library for programming swarms of robots

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Page 127 blank.Includes bibliographical references (p. 123-126).As robots become ubiquitous, multiple robots dedicated to a single task will become commonplace. Groups of robots can solve problems in fundamentally different ways than individuals while achieving higher levels of performance, but present unique challenges for programming and coordination. This work presents a set of communication techniques and a library of behaviors useful for programming large groups, or swarms, of robots to work together. The gradient-flood communications algorithms presented are resilient to the constantly changing network topology of the Swarm. They provide real-time information that is used to communicate data and to guide robots around the physical environment. Special attention is paid to ensure orderly removal of messages. Decomposing swarm actions into individual behaviors is a daunting task. Complex and subtle local interactions among individuals produce global behaviors, sometimes unexpectedly so. The behavior library presented provides group behavior "building blocks" that interact in predictable manner and can be combined to build complex applications. The underlying distributed algorithms are scaleable, robust, and self-stabilizing. The library of behaviors is designed with an eye towards practical applications, such as exploration, searching, and coordinated motion. All algorithms have been developed and tested on a swarm of 100 physical robots. Data is presented on algorithm correctness and efficiency. stabilizing. The library of behaviors is designed with an eye towards practical applications, such as exploration, searching, and coordinated motion. All algorithms have been developed and tested on a swarm of 100 physical robots. Data is presented on algorithm correctness and efficiency.by James D. McLurkin.S.M

Towards a Theory of Control Architecture: A quantitative framework for layered multi-rate control

This paper focuses on the need for a rigorous theory of layered control architectures (LCAs) for complex engineered and natural systems, such as power systems, communication networks, autonomous robotics, bacteria, and human sensorimotor control. All deliver extraordinary capabilities, but they lack a coherent theory of analysis and design, partly due to the diverse domains across which LCAs can be found. In contrast, there is a core universal set of control concepts and theory that applies very broadly and accommodates necessary domain-specific specializations. However, control methods are typically used only to design algorithms in components within a larger system designed by others, typically with minimal or no theory. This points towards a need for natural but large extensions of robust performance from control to the full decision and control stack. It is encouraging that the successes of extant architectures from bacteria to the Internet are due to strikingly universal mechanisms and design patterns. This is largely due to convergent evolution by natural selection and not intelligent design, particularly when compared with the sophisticated design of components. Our aim here is to describe the universals of architecture and sketch tentative paths towards a useful design theory.Comment: Submitted to IEEE Control Systems Magazin

Compensating for model uncertainty in the control of cooperative field robots

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2002.Includes bibliographical references (p. 113-123).Current control and planning algorithms are largely unsuitable for mobile robots in unstructured field environment due to uncertainties in the environment, task, robot models and sensors. A key problem is that it is often difficult to directly measure key information required for the control of interacting cooperative mobile robots. The objective of this research is to develop algorithms that can compensate for these uncertainties and limitations. The proposed approach is to develop physics-based information gathering models that fuse available sensor data with predictive models that can be used in lieu of missing sensory information. First, the dynamic parameters of the physical models of mobile field robots may not be well known. A new information-based performance metric for on-line dynamic parameter identification of a multi-body system is presented. The metric is used in an algorithm to optimally regulate the external excitation required by the dynamic system identification process. Next, an algorithm based on iterative sensor planning and sensor redundancy is presented to enable field robots to efficiently build 3D models of their environment. The algorithm uses the measured scene information to find new camera poses based on information content. Next, an algorithm is presented to enable field robots to efficiently position their cameras with respect to the task/target. The algorithm uses the environment model, the task/target model, the measured scene information and camera models to find optimum camera poses for vision guided tasks. Finally, the above algorithms are combined to compensate for uncertainties in the environment, task, robot models and sensors. This is applied to a cooperative robot assembly task in an unstructured environment.(cont.) Simulations and experimental results are presented that demonstrate the effectiveness of the above algorithms on a cooperative robot test-bed.by Vivek Anand Sujan.Ph.D

Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks