We introduce a multi-agent route planning problem for col-lecting sensor data in hostile or dangerous environmentswhen communication is unavailable. Solutions must considerthe risk of losing robots as they travel through the environ-ment, maximizing the expected value of a plan. This requiresplans that balance the number of agents used with the riskof losing them and the data they have collected so far. Whilethere are existing approaches that mitigate risk during task as-signment, they do not explicitly account for the loss of robotsas part of the planning process. We analyze the unique prop-erties of the problem and provide a hierarchical agglomera-tive clustering algorithm that finds high value solutions withlow computational overhead. We show that our solution ishighly scalable, exhibiting performance gains on large problem instances with thousands of tasks.

According to Destin, Hudack used automation to make Deliveroo's ordering process more efficient. The startup refers to live orders as "orders in flight," and Hudack specifically looked at the number of times Deliveroo has contact with an order, from the moment a customer puts in a request for food through to the moment it's delivered by a driver. Through automation, Deliveroo reduced that number "by 98%" -- making 25 people redundant in the process. March figures from PwC suggest Destin is probably right about automation destroying jobs, though the actual numbers are more conservative.