We present an augmented reality human-swarm interface that combines two modalities of interaction: environment-oriented and robot-oriented. The environment-oriented modality allows the user to modify the environment (either virtual or physical) to indicate a goal to attain for the robot swarm. The robot-oriented modality makes it possible to select individual robots to reassign them to other tasks to increase performance or remedy failures. Previous research has concluded that environment-oriented interaction might prove more difficult to grasp for untrained users. In this paper, we report a user study which indicates that, at least in collective transport, environment-oriented interaction is more effective than purely robot-oriented interaction, and that the two combined achieve remarkable efficacy.
At the opposite side of the spectrum, swarm-oriented interactions occur when a user treats the swarm as a unique entity. This modality of interaction has been demonstrated in navigation tasks, e.g., beacon-based @cite_16 , density-based @cite_7 , and waypoint-based @cite_8 . The main advantage of swarm-based interaction is that a small number of commands, e.g., the target position, is sufficient to control a large swarm. The price to pay, however, is the lack of fine-grained control on the robots. This makes it impossible to deal with suboptimal task assignment, individual failures, and error cascades.
@cite_16 @cite_7 @cite_8
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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. This study shows that appropriate human interaction can benefit a swarm of robots to achieve goals more efficiently. A set of desirable features for human swarm interaction is identified based on the principles of swarm robotics. Human swarm interaction architecture is then proposed that has all of the desirable features. A swarm simulation environment is created that allows simulating a swarm behavior in an indoor environment. The swarm behavior and the results of user interaction are studied by considering radiation source search and localization application of the swarm. Particle swarm optimization algorithm is slightly modified to enable the swarm to autonomously explore the indoor environment for radiation source search and localization. The emergence of intelligence is observed that enables the swarm to locate the radiation source completely on its own. Proposed human swarm interaction is then integrated in a simulation environment and user evaluation experiments are conducted. Participants are introduced to the interaction tool and asked to deploy the swarm to complete the missions. The performance comparison of the user guided swarm to that of the autonomous swarm shows that the interaction interface is fairly easy to learn and that user guided swarm is more efficient in achieving the goals. The results clearly indicate that the proposed interaction helped the swarm achieve emergence. This paper studies how an operator with limited situational awareness can collaborate with a swarm of simulated robots. The robots are distributed in an environment with wall obstructions. They aggregate autonomously but are unable to form a single cluster due to the obstructions. The operator lacks the bird’s-eye perspective, but can interact with one robot at a time, and influence the behavior of other nearby robots. We conducted a series of experiments. They show that untrained participants had marginal influence on the performance of the swarm. Expert participants succeeded in aggregating 85 of the robots while untrained participants, with bird’s-eye view, succeeded in aggregating 90 . This demonstrates that the controls are sufficient for operators to aid the autonomous robots in the completion of the task and that lack of situational awareness is the main difficulty. An analysis of behavioral differences reveals that trained operators learned to gain superior situational awareness. This paper investigates how haptic interactions can be defined for enabling a single operator to control and interact with a team of mobile robots. Since there is no unique or canonical mapping from the swarm configuration to the forces experienced by the operator, a suitable mapping must be developed. To this end, multi-agent manipulability is proposed as a potentially useful mapping, whereby the forces experienced by the operator relate to how inputs, injected at precise locations in the team, translate to swarm-level motions. Small forces correspond to directions in which it is easy to move the swarm, while larger forces correspond to more costly directions. Initial experimental results support the viability of the proposed, haptic, human-swarm interaction mapping, through a user study where operators are tasked with driving a collection of robots through a series of way points.