Quantitative Assessment of Robotic Swarm Coverage

This paper studies a generally applicable, sensitive, and intuitive error metric for the assessment of robotic swarm density controller performance. Inspired by vortex blob numerical methods, it overcomes the shortcomings of a common strategy based on discretization, and unifies other continuous notions of coverage. We present two benchmarks against which to compare the error metric value of a given swarm configuration: non-trivial bounds on the error metric, and the probability density function of the error metric when robot positions are sampled at random from the target swarm distribution. We give rigorous results that this probability density function of the error metric obeys a central limit theorem, allowing for more efficient numerical approximation. For both of these benchmarks, we present supporting theory, computation methodology, examples, and MATLAB implementation code.Comment: Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Porto, Portugal, 29--31 July 2018. 11 pages, 4 figure

Quantifying Robotic Swarm Coverage

In the field of swarm robotics, the design and implementation of spatial density control laws has received much attention, with less emphasis being placed on performance evaluation. This work fills that gap by introducing an error metric that provides a quantitative measure of coverage for use with any control scheme. The proposed error metric is continuously sensitive to changes in the swarm distribution, unlike commonly used discretization methods. We analyze the theoretical and computational properties of the error metric and propose two benchmarks to which error metric values can be compared. The first uses the realizable extrema of the error metric to compute the relative error of an observed swarm distribution. We also show that the error metric extrema can be used to help choose the swarm size and effective radius of each robot required to achieve a desired level of coverage. The second benchmark compares the observed distribution of error metric values to the probability density function of the error metric when robot positions are randomly sampled from the target distribution. We demonstrate the utility of this benchmark in assessing the performance of stochastic control algorithms. We prove that the error metric obeys a central limit theorem, develop a streamlined method for performing computations, and place the standard statistical tests used here on a firm theoretical footing. We provide rigorous theoretical development, computational methodologies, numerical examples, and MATLAB code for both benchmarks.Comment: To appear in Springer series Lecture Notes in Electrical Engineering (LNEE). This book contribution is an extension of our ICINCO 2018 conference paper arXiv:1806.02488. 27 pages, 8 figures, 2 table

Autonomous Capabilities for Small Unmanned Aerial Systems Conducting Radiological Response: Findings from a High-fidelity Discovery Experiment

This article presents a preliminary work domain theory and identifies autonomous vehicle, navigational, and mission capabilities and challenges for small unmanned aerial systems (SUASs) responding to a radiological disaster. Radiological events are representative of applications that involve flying at low altitudes and close proximities to structures. To more formally understand the guidance and control demands, the environment in which the SUAS has to function, and the expected missions, tasks, and strategies to respond to an incident, a discovery experiment was performed in 2013. The experiment placed a radiological source emitting at 10 times background radiation in the simulated collapse of a multistory hospital. Two SUASs, an AirRobot 100B and a Leptron Avenger, were inserted with subject matter experts into the response, providing high operational fidelity. The SUASs were expected by the responders to fly at altitudes between 0.3 and 30 m, and hover at 1.5 m from urban structures. The proximity to a building introduced a decrease in GPS satellite coverage, challenging existing vehicle autonomy. Five new navigational capabilities were identified: scan, obstacle avoidance, contour following, environment-aware return to home, andreturn to highest reading. Furthermore, the data-to-decision process could be improved with autonomous data digestion and visualization capabilities. This article is expected to contribute to a better understanding of autonomy in a SUAS, serve as a requirement document for advanced autonomy, and illustrate how discovery experimentation serves as a design tool for autonomous vehicles

Interacting particles with L\'{e}vy strategies: limits of transport equations for swarm robotic systems

L\'{e}vy robotic systems combine superdiffusive random movement with emergent collective behaviour from local communication and alignment in order to find rare targets or track objects. In this article we derive macroscopic fractional PDE descriptions from the movement strategies of the individual robots. Starting from a kinetic equation which describes the movement of robots based on alignment, collisions and occasional long distance runs according to a L\'{e}vy distribution, we obtain a system of evolution equations for the fractional diffusion for long times. We show that the system allows efficient parameter studies for a search problem, addressing basic questions like the optimal number of robots needed to cover an area in a certain time. For shorter times, in the hyperbolic limit of the kinetic equation, the PDE model is dominated by alignment, irrespective of the long range movement. This is in agreement with previous results in swarming of self-propelled particles. The article indicates the novel and quantitative modeling opportunities which swarm robotic systems provide for the study of both emergent collective behaviour and anomalous diffusion, on the respective time scales.Comment: 23 pages, 3 figures, to appear in SIAM Journal on Applied Mathematic

Steering herds away from dangers in dynamic environments

Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill to herd animals, enable crowd control and rescue from danger. Equipping robots with the capability of shepherding would allow performing such tasks with increased efficiency and reduced labour costs. So far, only single-robot or centralized multi-robot solutions have been proposed. The former is unable to observe dangers at any place surrounding the herd, and the latter does not generalize to unconstrained environments. Therefore, we propose a decentralized control algorithm for multi-robot shepherding, where the robots maintain a caging pattern around the herd to detect potential nearby dangers. When danger is detected, part of the robot swarm positions itself in order to repel the herd towards a safer region. We study the performance of our algorithm for different collective motion models of the herd. We task the robots to shepherd a herd to safety in two dynamic scenarios: (i) to avoid dangerous patches appearing over time and (ii) to remain inside a safe circular enclosure. Simulations show that the robots are always successful in shepherding when the herd remains cohesive, and enough robots are deployed

From Norm to Swarm: development of a balanced scorecard for evaluating automation in construction

Industry 4.0 technologies in construction (e.g. Building Information Modelling (BIM), robotics or 3D printing) offer radically different ways of planning and constructing the built environment. As a result, construction organisations expect an increase of productivity, efficiency, quality and safety, as well as a reduction of costs, emissions and waste. Yet a lack of management tools and standards to evaluate automation and set business strategic improvement drivers is hindering wider adoption in the construction industry. The aim of the project is to deliver a Balanced Scorecard (BSC) to support the adoption of automation in in the UK building industry by delivering a framework to evaluate automated construction processes from a holistic perspective (i.e. financial, social, and environmental). The BSC is co-created with industry and focuses on assessing performance indicators such as productivity, resource consumption, and GHG emissions, helping construction organisations to set improvement targets to achieve their long-term strategy. The Key Performance Indicators (KPIs) included in the BSC are tested using data from a case study of 3D printing with aerial robotics. Access to the EPSRC-funded project Aerial Additive Building Manufacturing will provide the principal dataset, supplemented by data provided by industry partners during two workshops

P&I Club Membership As Potential Incentivization For Adherence To Best Space Traffic Management Practices: A Maritime Analogue

It has been said that the space environment is becoming so accessible, we are at risk of depleting it as a resource, thereby risking society’s space-dependent functions. Law, regulations, policies, and guidelines exist to guide entities to act to preserve the space environment. However, best space traffic management (STM) practice implementation and regulatory compliance could be costly and resource-intensive, especially for a small business. Some entities may not undertake innovative space endeavors at all, or worse, ignore laws, regulations, policies, and guidelines. A question arises of how space actors could be persuaded to work toward meeting STM laws, regulations, policies, and guidelines and perhaps take on potentially costly practices to follow them. This thesis attempts to answer whether liability apportionment and risk-pooling through a space protection and indemnity (P&I) club membership could benefit a space actor enough to drive implementation of best space traffic management practices where actors could be more likely to adhere to laws, regulations, policies, and guidelines. The study is limited to one example model space P&I club in the U.S. as a foundation for a potential larger international group in the future. The study assumes both insurance and P&I calls can be based on publicly available financial information, though need for more detailed information on insurance premiums and P&I calls is needed to create a fine-tuned model. The study also assumes a potential space P&I club member would be subject to U.S. law, regulations, and policy. Methods include document and policy analysis, interviews with space insurance and risk management subject matter experts, and cost analyses. Arguably, a case does indeed exist wherein a potential space P&I club membership could benefit a space actor enough to encourage implementation of best space traffic management practices. However, it would be best used as part of the bigger STM picture alongside existing regulations and policies. Still, a P&I club membership could provide a significant enough benefit where actors could be more likely to adhere to regulations and policies, which would, in turn, have a positive impact on keeping the space environment sustainable for current and future activities

Miniature mobile sensor platforms for condition monitoring of structures

In this paper, a wireless, multisensor inspection system for nondestructive evaluation (NDE) of materials is described. The sensor configuration enables two inspection modes-magnetic (flux leakage and eddy current) and noncontact ultrasound. Each is designed to function in a complementary manner, maximizing the potential for detection of both surface and internal defects. Particular emphasis is placed on the generic architecture of a novel, intelligent sensor platform, and its positioning on the structure under test. The sensor units are capable of wireless communication with a remote host computer, which controls manipulation and data interpretation. Results are presented in the form of automatic scans with different NDE sensors in a series of experiments on thin plate structures. To highlight the advantage of utilizing multiple inspection modalities, data fusion approaches are employed to combine data collected by complementary sensor systems. Fusion of data is shown to demonstrate the potential for improved inspection reliability