2020/04/20 by Jason R. Cody, Cody, Jason R., Karina A. Roundtree +3
Computer Science · Engineering · Environmental Science · #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Mobile Crowdsensing and Crowdsourcing #Modular Robots and Swarm Intelligence #Multiagent Systems (cs.MA) #Species Distribution and Climate Change #cs.HC #cs.MA
paper · pdf · doi:10.48550/arxiv.2004.09581
27 pages, 5 figures
arxiv created 2020/04/20 · openalex publication_date 2020/04/20 · arxiv updated 2020/04/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Robotic collectives are large groups (at least 50) of locally sensing and communicating robots that encompass characteristics of swarms and colonies, whose emergent behaviors accomplish complex tasks. Future human-collective teams will extend the ability of operators to monitor, respond, and make decisions in disaster response, search and rescue, and environmental monitoring problems. This manuscript evaluates two collective best-of-n decision models for enabling collectives to identify and choose the highest valued target from a finite set of n targets. Two challenges impede the future use of human-collective shared decisions: 1) environmental bias reduces collective decision accuracy when poorer targets are easier to evaluate than higher quality targets, and 2) little is understood about shared human-collective decision making interaction strategies. The two evaluated collective best-of-n models include an existing insect colony decision model and an extended bias-reducing model that attempts to reduce environmental bias in order to improve accuracy. Collectives using these two strategies are compared independently and as members of human-collective teams. Independently, the extended model is slower than the original model, but the extended algorithm is 57% more accurate in decisions where the optimal option is more difficult to evaluate. Human-collective teams using the bias-reducing model require less operator influence and achieve 25% higher accuracy with difficult decisions, than the human-collective teams using the original model. Further, a novel human-collective interaction strategy enables operators to adjust collective autonomy while making multiple simultaneous decisions.