Neglect benevolence in human control of swarms in the presence of latency

Abstract

Autonomous swarm algorithms have been studied extensively in the past several years. However, there is little research on the effect of injecting human influence into a robot swarm-whether it be to update the swarm's current goals or reshape swarm behavior. While there has been growing research in the field of human-swarm interaction (HSI), no previous studies have investigated how humans interact with swarms under communication latency. We investigate the effects of latency both with and without a predictive display in a basic swarm foraging task to see if such a display can help mitigate the effects of delayed feedback of the swarm state. Furthermore, we introduce a new concept called neglect benevolence to represent how a human operator may need to give time for swarm algorithms to stabilize before issuing new commands, and we investigate it with respect to task performance. Our study shows that latency did affect a user's ability to control a swarm to find targets in the foraging task, and that the predictive display helped to remove these effects. We also found evidence for neglect benevolence, and that operators exploited neglect benevolence in different ways, leading to two different, but equally successful strategies in the target-searching task.

Bounds of Neglect Benevolence in Input Timing for Human Interaction with Robotic Swarms

“…In a previous experimental study using a foraging scenario [38], it was found that the performance of the human-swarm system was strongly affected by the time between two commands that the human applied to the robots. In particular, it was found that one group of subjects who performed well waited for some time after they issued a command before issuing another corrective command (when they wanted to change the direction in which the swarm was heading).…”

“…For other inputs, such as a command to switch between algorithms, delivered by broadcast or propagation, the relation between input and a desired effect on the swarm may be even less clear. Difficulties in developing internal models of dynamics have been found to be particularly acute when timing of inputs or lags in response are present [38] as is likely in controlling a swarm. A close counterpart lies in industrial processes where substantial lags between input and response have been abstracted to laboratory tasks such as Crossman's water bath [17].…”

Human Control of Leader-Based Swarms

“…In [9], the authors create a user interface whereby operators can use video game controllers to move around subgroups of the swarm, or take control of individuals when necessary. This idea of controlling the swarm as a single group is continued in [2] and [3], where the authors broadcast global heading commands to the swarm, and then the robots perform consensus algorithms to overcome odometry error and flock in a common direction. Other work has expanded on this by taking inspiration from the literature on levels of automation (LOAs) and applying that to swarm control.…”

“…Because swarms are made up of numerous robots that operate under scalable, distributed algorithms, they can cover more area more robustly than a single robot or teams of independent robots. This makes them suitable for jobs such as exploration and foraging [1], [2], [3], construction [4], [5], and fire fighting or HAZMAT situations [6], [7]. Indeed, in the recent years, we have seen swarms move from a theoretical possibility to systems implemented on real robots in laboratory settings, such as those in [8], [9], [10].…”

Human control of robot swarms with dynamic leaders

“…There are different models of robotic swarms, such as broadcasting commands to all robots simultaneously [7], and treating the swarm as a spatial computer [8]. In this paper, we study human control of bio-inspired swarms-namely swarms that operate via simple local control laws.…”