This paper presents an investigation of odor localization by groups of autonomous mobile robots. First, we describe a distributed algorithm by which groups of agents can solve the full odor localization task. Next, we establish that conducting polymerbased odor sensors possess the combination of speed and sensitivity necessary to enable real world odor plume tracing and we demonstrate that simple local position, odor, and flow information, tightly coupled with robot behavior, is sufficient to allow a robot to localize the source of an odor plume. Finally, we show that elementary communication among a group of agents can increase the efficiency of the odor localization system performance.

Abstract| This paper presents an investigation of o d o r localization by groups of autonomous mobile robots using principles of Swarm Intelligence. We describe a distributed algorithm by which groups of agents can solve the full o d o r localization task more e ciently than a single agent. We demonstrate that a group of real robots under fully distributed control can successfully traverse a real o d o rplume. Finally, we show that an embodied simulator can faithfully reproduce the real robots experiments and thus can b ea useful tool for o -line study and optimization of real world o d o rlocalization.

This paper presents an investigation of flocking, the formation and maintenance of coherent group movement, by teams of autonomous mobile robots using principles of Swarm Intelligence. First, we present a simple flocking task, and we describe a leaderless distributed flocking algorithm (LD) that is more conducive to implementation on embodied agents than the established algorithms used in computer animation. Next, we use an embodied simulator and reinforcement learning techniques to optimize LD performance under different conditions, showing that this method can be used not only to improve performance but also to gain insight into which algorithm components contribute most to system behavior. Finally, we demonstrate that a group of real robots executing LD with emulated sensors can successfully flock (even in the presence of individual agent failure) and that systematic characterization (and therefore optimization) of real robot flocking parameters is achievable.

This paper presents an investigation of odor localization by groups of autonomous mobile robots using principles of Swarm Intelligence. First, we describe a distributed algorithm by which groups of agents can solve the full odor localization task more efficiently than a single agent. Next, we demonstrate that a group of real robots under fully distributed control can successfully traverse a real odor plume, and that an embodied simulator can faithfully reproduce these real robots experiments. Finally, we use the embodied simulator combined with a reinforcement learning algorithm to optimize performance across group size, showing that it can be useful not only for improving real world odor localization, but also for quantitatively characterizing the influence of group size on task performance.

Abstract. This paper presents a quantitative analysis of the tradeoffs between group size and efficiency in collective search tasks that considers both the timesensitive nature of search completion and the system operating cost. First, the search task is defined and a performance metric is presented that can account for all of the costs associated with the task. Next, for both random and coordinated search strategies, analytical expressions are derived that can be used to predict optimal system performance bounds given a particular task description, and the performance benefit of using coordinated search is shown to be dependent on the relative values of the different cost components. Finally, an embodied computer simulation is used to support the analytical results, suggesting that the assumptions involved in their derivation are sound.