Human-Collective Collaborative Target Selection

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.

Visualization Design for Human-Collective Teams

Robotic collectives (i.e., colonies and swarms) are applicable to a wide range of applications, including environmental monitoring, search and rescue, as well as infrastructure monitoring. The presented evaluation focuses on how two visualization designs impact human-collective team performance during a best-of- n sequential decision making task with colonies of 200 agents. Traditional visualizations present all the individual robots that encompass the entirety of the collective, which may cause the human operator to suffer from information overload which hinders understanding the collective’s current state, the reasoning behind actions, and associated predictive future outcomes. Interface designs that abstract the individual collective member details and present the collective’s state are needed to alleviate high workload and mitigate human error. The evaluation determined that an abstract visualization of the collective’s state produced better overall performance than the visualization that showed all the individual agents.