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

Consensus and Flocking Under Communication Failures for a Class of Cucker-Smale Systems

We study sufficient conditions for the emergence of consensus and flocking in a class of strongly cooperative non-linear multi-agent systems subject to arbitrary communication failures. Our approach is based on a combination of Lyapunov analysis along with the formulation of a novel persistence of excitation condition for cooperative systems. This assumption can be interpreted in terms of average connectedness of the interaction graph of the system, and provides quantitative convergence rates towards consensus and flocking

Transporting Robotic Swarms via Mean-Field Feedback Control

With the rapid development of AI and robotics, transporting a large swarm of networked robots has foreseeable applications in the near future. Existing research in swarm robotics has mainly followed a bottom-up philosophy with predefined local coordination and control rules. However, it is arduous to verify the global requirements and analyze their performance. This motivates us to pursue a top-down approach, and develop a provable control strategy for deploying a robotic swarm to achieve a desired global configuration. Specifically, we use mean-field partial differential equations (PDEs) to model the swarm and control its mean-field density (i.e., probability density) over a bounded spatial domain using mean-field feedback. The presented control law uses density estimates as feedback signals and generates corresponding velocity fields that, by acting locally on individual robots, guide their global distribution to a target profile. The design of the velocity field is therefore centralized, but the implementation of the controller can be fully distributed -- individual robots sense the velocity field and derive their own velocity control signals accordingly. The key contribution lies in applying the concept of input-to-state stability (ISS) to show that the perturbed closed-loop system (a nonlinear and time-varying PDE) is locally ISS with respect to density estimation errors. The effectiveness of the proposed control laws is verified using agent-based simulations

Robotic Pollination - Targeting kiwifruit flowers for commercial application

This paper contains the initial evaluation of a novel platform mounted robotic pollination system. Advancement in artificial pollination is an important step forward in agricultural sectors due to the global decline of natural pollinators. Robotic pollination allows for potentially autonomous, precision operation; however, background research suggested that prior development in the area has been sparse. The featured wet-application robotic pollination system was capable of detecting >70% of flowers whilst driving at a slow-pace through kiwifruit orchard rows. Over 80% of flowers were robotically pollinated

A Pontryagin Maximum Principle in Wasserstein Spaces for Constrained Optimal Control Problems

In this paper, we prove a Pontryagin Maximum Principle for constrained optimal control problems in the Wasserstein space of probability measures. The dynamics, is described by a transport equation with non-local velocities and is subject to end-point and running state constraints. Building on our previous work, we combine the classical method of needle-variations from geometric control theory and the metric differential structure of the Wasserstein spaces to obtain a maximum principle stated in the so-called Gamkrelidze form.Comment: 35 page

Consensus and Flocking under Communication Failures for a Class of Cucker-Smale Systems

In this paper, we study sufficient conditions for the emergence of asymptotic consensus and flocking in a certain class of non-linear generalised Cucker-Smale systems subject to multiplicative communication failures. Our approach is based on the combination of strict Lyapunov design together with the formulation of a suitable persistence condition for multi-agent systems. The latter can be interpreted as a lower bound on the algebraic connectivity of the time-average of the interaction graph generated by the communication weights, and provides quantitative decay estimates for the variance functional along the solutions of the system

Large-Scale Multi-Agent Transport: Theory, Algorithms and Analysis

The problem of transport of multi-agent systems has received much attention in a wide range of engineering and biological contexts, such as spatial coverage optimization, collective migration, estimation and mapping of unknown environments. In particular, the emphasis has been on the search for scalable decentralized algorithms that are applicable to large-scale multi-agent systems.For large multi-agent collectives, it is appropriate to describe the configuration of the collective and its evolution using macroscopic quantities, while actuation rests at the microscopic scale at the level of individual agents. Moreover, the control problem faces a multitude of information constraints imposed by the multi-agent setting, such as limitations in sensing, communication and localization. Viewed in this way, the problem naturally extends across scales and this motivates a search for algorithms that respect information constraints at the microscopic level while guaranteeing performance at the macroscopic level.We address the above concerns in this dissertation on three fronts: theory, algorithms and analysis. We begin with the development of a multiscale theory of gradient descent-based multi-agent transport that bridges the microscopic and macroscopic perspectives and sets out a general framework for the design and analysis of decentralized algorithms for transport. We then consider the problem of optimal transport of multi-agent systems, wherein the objective is the minimization of the net cost of transport under constraints of distributed computation. This is followed by a treatment of multi-agent transport under constraints on sensing and communication, in the absence of location information, where we study the problem of self-organization in swarms of agents. Motivated by the problem of multi-agent navigation and tracking of moving targets, we then present a study of moving-horizon estimation of nonlinear systems viewed as a transport of probability measures. Finally, we investigate the robustness of multi-agent networks to agent failure, via the problem of identifying critical nodes in large-scale networks