Towards human control of robot swarms

In this paper we investigate principles of swarm control that enable a human operator to exert influence on and control large swarms of robots. We present two principles, coined selection and beacon control, that differ with respect to their temporal and spatial persistence. The former requires active selection of groups of robots while the latter exerts a passive influence on nearby robots. Both principles are implemented in a testbed in which operators exert influence on a robot swarm by switching between a set of behaviors ranging from trivial behaviors up to distributed autonomous algorithms. Performance is tested in a series of complex foraging tasks in environments with different obstacles ranging from open to cluttered and structured. The robotic swarm has only local communication and sensing capabilities with the number of robots ranging from 50 to 200. Experiments with human operators utilizing either selection or beacon control are compared with each other and to a simple autonomous swarm with regard to performance, adaptation to complex environments, and scalability to larger swarms. Our results show superior performance of autonomous swarms in open environments, of selection control in complex environments, and indicate a potential for scaling beacon control to larger swarms

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

Connectivity Differences between Human Operators of Swarms and Bandwidth Limitations

Human interaction with robot swarms (HSI) is a young field with very few user studies that explore operator behavior. All these studies assume perfect communication between the operator and the swarm. A key challenge in the use of swarm robotic systems in human supervised tasks is to understand human swarm interaction in the presence of limited communication bandwidth, which is a constraint arising in many practical scenarios. In this paper, we present results of human-subject experiments designed to study the effect of bandwidth limitations in human swarm interaction. We consider three levels of bandwidth availability in a swarm foraging task. The lowest bandwidth condition performs poorly, but the medium and high bandwidth condition both perform well. In the medium bandwidth condition, we display useful aggregated swarm information (like swarm centroid and spread) to compress the swarm state information. We also observe interesting operator behavior and adaptation of operators' swarm reaction

Neglect Benevolence in Human-Swarm Interaction with Communication Latency

In practical applications of robot swarms with bio-inspired behaviors, a human operator will need to exert control over the swarm to fulfill the mission objectives. In many operational settings, human operators are remotely located and the communication environment is harsh. Hence, there exists some latency in information (or control command) transfer between the human and the swarm. In this paper, we conduct experiments of human-swarm interaction to investigate the effects of communication latency on the performance of a human-swarm system in a swarm foraging task. We develop and investigate the concept of neglect benevolence, where a human operator allows the swarm to evolve on its own and stabilize before giving new commands. Our experimental results indicate that operators exploited neglect benevolence in different ways to develop successful strategies in the foraging task. Furthermore, we show experimentally that the use of a predictive display can help mitigate the adverse effects of communication latency

Differences between Old and Young Adults’ Ability to Recognize Human Faces Underlie Processing of Horizontal Information

Recent psychophysical research supports the notion that horizontal information of a face is primarily important for facial identity processes. Even though this has been demonstrated to be valid for young adults, the concept of horizontal information as primary informative source has not yet been applied to older adults’ ability to correctly identify faces. In the current paper, the role different filtering methods might play in an identity processing task is examined for young and old adults, both taken from student populations. Contrary to most findings in the field of developmental face perception, only a near-significant age effect is apparent in upright and un-manipulated presentation of stimuli, whereas a bigger difference between age groups can be observed for a condition which removes all but horizontal information of a face. It is concluded that a critical feature of human face perception, the preferential processing of horizontal information, is less efficient past the age of 60 and is involved in recognition processes that undergo age-related decline usually found in the literature

Extracting surveillance graphs from robot maps

Abstract — GRAPH-CLEAR is a recently introduced theo-retical framework to model surveillance tasks accomplished by multiple robots patrolling complex indoor environments. In this paper we provide a first step to close the loop between its graph-based theoretical formulation and practical scenarios. We show how it is possible to algorithmically extract suitable so-called surveillance graphs from occupancy grid maps. We also identify local graph modification operators, called contractions, that alter the graph being extracted so that the original surveillance problem can be solved using less robots. The algorithm we present is based on the Generalized Voronoi Diagram, a structure that can be simply computed using watershed like algorithms. Our algorithm is evaluated by processing maps produced by mobile robots exploring indoor environments. It turns out that the proposed algorithm is fast, robust to noise, and opportunistically modifies the graph so that less expensive strategies can be computed. I

Multi-robot surveillance: An improved algorithm for the GRAPH-CLEAR problem

Abstract—The main contribution of this paper is an im-proved algorithm for the GRAPH-CLEAR problem, a novel NP-complete graph theoretic problem we recently introduced as a tool to model multi-robot surveillance tasks. The proposed al-gorithm combines two previously developed solving techniques and produces strategies that require less robots to be executed. We provide a theoretical framework useful to identify the conditions for the existence of an optimal solution under special circumstances, and a set of mathematical tools characterizing the problem being studied. Finally we also identify a set of open questions deserving more investigations. I

Probabilistic Graph-Clear

Abstract — This paper introduces a probabilistic model for multirobot surveillance applications with limited range and possibly faulty sensors. Sensors are described with a footprint and a false negative probability, i.e. the probability of failing to report a target within their sensing range. The model implements a probabilistic extension to our formerly developed deterministic approach for modeling surveillance tasks in large environments with large robot teams known as Graph-Clear. This extension leads to a new algorithm that allows to answer new design and performance questions, namely 1) how many robots are needed to obtain a certain confidence that the environment is free from intruders, and 2) given a certain number of robots, how should they coordinate their actions to minimize their failure rate. I

Robotic swarm connectivity with human operation and bandwidth limitations

Human interaction with robot swarms (HSI) is a young field with very few user studies that explore operator behavior. All these studies assume perfect communication between the operator and the swarm. A key challenge in the use of swarm robotic systems in human supervised tasks is to understand human swarm interaction in the presence of limited communication bandwidth, which is a constraint arising in many practical scenarios. In this paper, we present results of human-subject experiments designed to study the effect of bandwidth limitations in human swarm interaction. We consider three levels of bandwidth availability in a swarm foraging task. The lowest bandwidth condition performs poorly, but the medium and high bandwidth condition both perform well. In the medium bandwidth condition, we display useful aggregated swarm information (like swarm centroid and spread) to compress the swarm state information. We also observe interesting operator behavior and adaptation of operators' swarm reaction

Investigating neglect benevolence and communication latency during human-swarm interaction

In practical applications of robot swarms with bioinspired behaviors, a human operator will need to exert control over the swarm to fulfill the mission objectives. In many operational settings, human operators are remotely located and the communication environment is harsh. Hence, there exists some latency in information (or control command) transfer between the human and the swarm. In this paper, we conduct experiments of human-swarm interaction to investigate the effects of communication latency on the performance of a humanswarm system in a swarm foraging task.We develop and investigate the concept of neglect benevolence, where a human operator allows the swarm to evolve on its own and stabilize before giving new commands. Our experimental results indicate that operators exploited neglect benevolence in different ways to develop successful strategies in the foraging task. Furthermore, we show experimentally that the use of a predictive display can help mitigate the adverse effects of communication latency