Human-Collective Collaborative Target Selection

Abstract

Robotic collectives are composed of hundreds or thousands of distributed robots using local sensing and communication that encompass characteristics of biological spatial swarms, colonies, or a combination of both. Interactions between the individual entities can result in emergent collective behaviors. Human operators in future disaster response or military engagement scenarios are likely to deploy semi-autonomous collectives to gather information and execute tasks within a wide area, while reducing the exposure of personnel to danger. This article presents and evaluates two action selection models in an experiment consisting of a single human operator supervising four simulated collectives. The action selection models have two parts: (1) a best-of- n decision-making model that attempts to choose the highest-quality target from a set of n targets and (2) a quorum sensing task sequencing model that enables autonomous target site occupation. An original biologically inspired insect colony decision model is compared to a bias-reducing model that attempts to reduce environmental bias, which can negatively influence collective best-of- n decisions when poorer-quality targets are easier to evaluate than higher-quality targets. The collective decision-making models are compared in both supervised and unsupervised trials. The bias-reducing model without human supervision is slower than the original model but is 57% more accurate for decisions where evaluating the optimal target is more difficult. Human-collective teams using the bias-reducing model require less operator influence and achieve 25% higher accuracy with difficult decisions compared to the teams using the original model.