Swarm shape manipulation through connection control

The control of a large swarm of distributed agents is a well known challenge within the study of unmanned autonomous systems. However, it also presents many new opportunities. The advantages of operating a swarm through distributed means has been assessed in the literature for efficiency from both operational and economical aspects; practically as the number of agents increases, distributed control is favoured over centralised control, as it can reduce agent computational costs and increase robustness on the swarm. Distributed architectures, however, can present the drawback of requiring knowledge of the whole swarm state, therefore limiting the scalability of the swarm. In this paper a strategy is presented to address the challenges of distributed architectures, changing the way in which the swarm shape is controlled and providing a step towards verifiable swarm behaviour, achieving new configurations, while saving communication and computation resources. Instead of applying change at agent level (e.g. modify its guidance law), the sensing of the agents is addressed to a portion of agents, differentially driving their behaviour. This strategy is applied for swarms controlled by artificial potential functions which would ordinarily require global knowledge and all-to-all interactions. Limiting the agents' knowledge is proposed for the first time in this work as a methodology rather than obstacle to obtain desired swarm behaviour

A Framework for Automatic Behavior Generation in Multi-Function Swarms

17 USC 105 interim-entered record; under review.Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of Radio Frequency (RF) emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire enables the swarm to online transition between behaviors featuring different trade-offs of applications depending on the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study investigates the importance of individual sensor or controller inputs. This is done through ablation, where individual inputs are disabled and their impact on the performance of the swarm controllers is assessed and analyzed

A new path planning approach based on artificial electric potential energy

Path planning is one of the most fundamental desired autonomous navigation capabilities for aircrafts. A sensible environment modeling method plays a significant role in improving the path planning algorithm, and the electric potential principle has a unique advantage in this regard. Due to the random node generation of the sampling-based algorithm, it is difficult to generate an optimum path. In the integration of electric potential cost function and probability function, the calculation has approved that there is a negative correlation between the path cost value and probability value, that is, the lower the cost value, the higher the probability that the path nodes is to be selected. Meanwhile, the electric potential value of the entire path is also used to evaluate the safety of an entire route. The simulation results depict that, compared with other traditional methods, the algorithm proposed in this article has distinctive superiority in raising and enhancing computational efficiency and path safety

A Framework for Automatic Behavior Generation in Multi-Function Swarms

Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of RF emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire would enable the swarm to transition between behavior trade-offs online, according to the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study develops a methodology for analyzing the makeup of the resulting controllers. This is done through a parameter variation study where the importance of individual inputs to the swarm controllers is assessed and analyzed

Problems of sensor placement for intelligent environments of robotic testbeds

In this paper, we consider problems of sensor placement for intelligent environments of robotic testbeds. In particular, we consider the problem of sensor placement and the problem of sensor placement for triangulation based localization. We present experimental results for these problems. Also, we use artificial physics optimization algorithms for solution of the problem of sensor placement. We consider artificial physics optimization for different virtual forces. In particular, we use Runge Kutta neural networks for the calculation of values of virtual forces. © 2013 Andrey Sheka

A counter abstraction technique for the verification of robot swarms.

We study parameterised verification of robot swarms against temporal-epistemic specifications. We relax some of the significant restrictions assumed in the literature and present a counter abstraction approach that enable us to verify a potentially much smaller abstract model when checking a formula on a swarm of any size. We present an implementation and discuss experimental results obtained for the alpha algorithm for robot swarms

A Distributed Scalable Approach to Formation Control in Multi-Robot Systems

A new algorithm for the control of formations of mobile robots is presented. Formations with a triangular lattice structure are created using distributed control rules, using only local information on each robot. The overall direction of movement of the formation is not pre-established but rather results from local interactions, giving all the robots a common, self-organized heading. Experiments were done to test the algorithm, yielding results in which robots behaved as expected, moving at a reasonable speed and maintaining the desired distances among themselves. Up to seven robots were used in real experiments and up to forty in simulation

Human Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms

In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors

Exploration via Structured Triangulation by a Multi-Robot System with Bearing-Only Low-Resolution Sensors

This paper presents a distributed approach for exploring and triangulating an unknown region using a multi- robot system. The objective is to produce a covering of an unknown workspace by a fixed number of robots such that the covered region is maximized, solving the Maximum Area Triangulation Problem (MATP). The resulting triangulation is a physical data structure that is a compact representation of the workspace; it contains distributed knowledge of each triangle, adjacent triangles, and the dual graph of the workspace. Algorithms can store information in this physical data structure, such as a routing table for robot navigation Our algorithm builds a triangulation in a closed environment, starting from a single location. It provides coverage with a breadth-first search pattern and completeness guarantees. We show the computational and communication requirements to build and maintain the triangulation and its dual graph are small. Finally, we present a physical navigation algorithm that uses the dual graph, and show that the resulting path lengths are within a constant factor of the shortest-path Euclidean distance. We validate our theoretical results with experiments on triangulating a region with a system of low-cost robots. Analysis of the resulting quality of the triangulation shows that most of the triangles are of high quality, and cover a large area. Implementation of the triangulation, dual graph, and navigation all use communication messages of fixed size, and are a practical solution for large populations of low-cost robots.Comment: 8 pages, 11 figures. To appear in ICRA 201