Ignorance is Not Bliss: An Analysis of CentralPlace Foraging Algorithms Centralplace foraging (CPF) is a canonical task in collective robotics with applications to planetary exploration, automated mining, warehousing, and search and rescue operations. We compare the performance of three CentralPlace Foraging Algorithms (CPFAs), variants of which have been shown to work well in real robots: spiralbased, rotatingspoke, and randomballistic. To understand the difference in performance between these CPFAs, we define the price of ignorance and show how this metric explains our previously published empirical results. We obtain upperbounds for expected complete collection times for each algorithm and evaluate their performance in simulation. We show that sitefidelity (i.e. returning to the location of the last found target) and avoiding search redundancy are keyfactors that determine the efficiency of CPFAs. Our formal analysis suggests the following efficiency ranking from best to worst: spiral, spoke, and the stochastic ballistic algorithm.

Plasticity in Collective DecisionMaking for Robots: Creating Global Reference Frames, Detecting Dynamic Environments, and Preventing Lockins Swarm robots operate as autonomous agents and a swarm as a whole gets autonomous by its capability of collective decisionmaking. Despite intensive research on models of collective decisionmaking, the implementation in multirobot systems is still challenging. Here, we advance the state of the art by introducing more plasticity to the decisionmaking process and by increasing the scenario difficulty. Most studies on largescale multirobot decisionmaking are limited to one instance of an iterated explorationdissemination phase followed by successful and permanent convergence. We investigate a dynamic environment that requires constant collective monitoring of option qualities. Once a significant change in qualities is detected by the swarm, it has to collectively reconsider its previous decision accordingly. This is only possible by preventing lockins, a global consensus state of no return (i.e., a dominant majority of robots prevents the swarm from switching to another, possibly better option). In addition, we introduce a scenario of increased difficulty as the robots must locate themselves to assess the quality of an option. Using local communication, swarm robots propagate hopcount information throughout the swarm to form a global reference frame. We successfully validate our implementation in many swarm robot experiments concerning robustness to disruptions of the reference frame, scalability, and adaptivity to a dynamic environment.