THE UTILITY OF HETEROGENEOUS SWARMS OF SIMPLE UAVS WITH LIMITED SENSORY CAPACITY IN DETECTION AND TRACKING TASKS Matthias Scheutz * , Paul Schermerhorn * , and Peter Bauer ** (*) Department of Computer Science and Engineering (**) Department of Electrical Engineering University of Notre Dame Notre Dame, IN 46556, USA {mscheutz,pscherm1,pbauer}@nd.edu ABSTRACT We present a physically realizable UAV model for locating and tracking chemical clouds. Simulation results are pre- sented for implementations of this model with two configu- rations, one that is faster and requires more space to avoid collisions, and one that is slower and can cover an area more densely. Heterogeneous swarms of agents are shown to have better performance than homogeneous swarms of similar size because they take advantage of the strengths of each configuration. 1. INTRODUCTION Biologically inspired swarms of autonomous agents have been used successfully in a variety of applications ranging from various kinds of ground-based robots, to unmanned aerial vehicles (UAVs). The agents employed in swarm sys- tems are typically governed by simple rules and coordinate their behavior based on simple interaction principles that use, among other factors, the distance between all agents within a given local neighborhood (e.g., variants to Reynold’s three rules “flock centering”, “obstacle avoidance”, and “ve- locity matching” [3]). Theoretical results have been proved about various properties of swarms governed by such rules (e.g., collision avoidance or stability [4, 5]). From a practical perspective, however, some assump- tions about the availability of information about the swarm are idealized and difficult to meet in implementation. In par- ticular, it might not be possible to get exact distance read- ings for all agents within a given neighborhood (e.g., be- cause of occlusion effects, or simply because sensors that could provide that information are too expensive or complex to be used in the agent implementation, lack of GPS, etc.). Moreover, it might not be possible to determine the overall goal direction (e.g., because the goal cannot be sensed at a distance). Finally, theoretical investigations typically limit swarms to homogeneous groups, while it might be advanta- geous to use heterogeneous groups, e.g., because they give rise to a more robust system, improve overall task perfor- mance, or require fewer resources. In this paper we address the above concerns and investi- gate the utility of heterogeneous swarms of extremely sim- ple, physically realizable agents for a class of local detec- tion and tracking tasks. Potential field-based goal detec- tion and tracking methods are not directly applicable in this case because the objects to be tracked cannot be sensed at a distance (which is a prerequisite for standard potential-field based navigation). As a specific instance of this class of tasks, we consider a “chemical detection and tracking sce- nario”, in which a cloud of chemicals of a particular kind with unknown density, extension, heading and dispersion over time has to be detected and enclosed by unmanned aerial vehicles without collisions among UAVs. Each UAV has an on-board sensor that can detect chemicals of this type whenever they come in touch with the sensor surface, but cannot detect chemicals otherwise. Consequently, it is not possible for any UAV to detect the chemical cloud at a dis- tance using its chemical sensors. Our solution to overcome this problem is to equip each UAV with an “attractive beacon” that it will turn on when- ever it detects a chemical, thus subsequently attracting other UAVs to that area. The task is then accomplished in two phases: (1) a detection phase where UAVs swarm, find the cloud and once one UAV has detected it others will move toward it (top in Fig. 1), and (2) an enclosure and tracking phase, where the UAVs cover the cloud area as tightly as possible and move along with it (bottom in Fig. 1). A related study employs biologically-inspired plume- tracking mechanisms to allow robots to track odor plumes [1, 2]. In some configurations, the robots work together in a manner similar to that described below, with a robot de- tecting an odor plume able to attract other robots to the plume. One main difference between this work and the present study is that the robotic agents use scent gradients,