Abstract-This paper presents an approach for deploying a team of mobile sensor nodes to form a sensor network in indoor environments. The challenge in this work is that the mobile sensor nodes have no ability for localization or obstacle avoidance. Thus, our approach entails the use of more capable "helper" robots that "herd" the mobile sensor nodes into their deployment positions. To extensively explore the issues of heterogeneity in multi-robot teams, we employ the use of two types of helper robots -one that acts as a leader and a second that: 1) acts as a follower and 2) autonomously teleoperates the mobile sensor nodes. Due to limited sensing capabilities, neither of these helper robots can herd the mobile sensor nodes alone; instead, our approach enables the team as a whole to successfully accomplish the sensor deployment task. Our approach involves the use of line-of-sight formation keeping, which enables the follower robot to use visual markers to move the group along the path executed by the leader robot. We present results of the implementation of this approach in simulation, as well as results to date in the implementation on physical robot systems. To our knowledge, this is the first implementation of robot herding using such highly heterogeneous robots, in which no single type of robot could accomplish the sensor network deployment task, even if multiple copies of that robot type were available.
I. INTRODUCTIONIn this paper, we address the issue of robot team heterogeneity in the context of mobile sensor net deployment in an indoor environment. In general, if all mobile sensor nodes have the ability to locomote and to sense other robots and obstacles in the environment, then a distributed dispersion algorithm based on potential fields (e.g., [1]) would be an appropriate solution strategy for deploying the mobile sensor network. However, if some of the robots do not have the sensing capability to detect obstacles or other robots (but they do have locomotion capabilities and special-purpose sensors needed in the sensor network, such as acoustic or chemical sensors), then such a solution strategy would no longer work. On the other hand, if some of the robot team members were highly capable robots that could help navigate the less capable robots, then a workable solution strategy would be for the more capable robots to guide the less capable robots to a deployment position. This is the approach we present in this paper.Section II provides an overview to our approach and the behaviors of the various robots. In Section III, we discuss our approach to vision-based detection of robot ID and relative pose using visual markers. Section IV discusses our approach to maintaining line-of-sight formations. Our approach for planning for sensor net deployment is briefly discussed in Section V. We present the results of our integrated approach
In this paper, we lay the groundwork for extending our previously developed ASyMTRe architecture to enable constructivist learning for multi-robot team tasks. The ASyMTRe architecture automatically configures schemas within, and across, robots to form the highest utility solution that achieves a given multi-robot team task. We believe that the schemabased approach used in ASyMTRe is a useful abstraction not only for forming heterogeneous coalitions, but also for enabling constructivist learning, in which chunks of schemas that solve intermediate subproblems are learned and then made available for future task solutions. However, the existing ASyMTRe search algorithm for finding configurations of schemas that completely solve given tasks (Centralized ASyMTRe-CA) is not well-suited for identifying useful chunks of schemas that could solve intermediate subtasks that may be useful in the future. Thus, in this current work, we explore an Evolutionary Learning (EL) technique for the offline learning of schema chunks that could be saved and used later in an online search (using the regular CA algorithm) for coalition configurations. However, we do not want to sacrifice solution quality in making use of the evolutionary search technique. Thus, we compare the solutions discovered by the EL algorithm with those that are found using CA, as well as with a third algorithm that randomizes the CA algorithm, called RA. Four different applications in simulation are used to evaluate the EL, CA, and RA techniques. Our results show that the EL approach indeed finds solutions of comparable quality to the CA technique, while also providing the added benefit of learning highly fit partial solutions, or schema chunks, that may be beneficial for future tasks via constructivist learning. We conclude by arguing that the combination of the online CA search for solving current multi-robot tasks can be combined with the offline EL approach that can identify intermediate solutions (or schema chunks) that may be useful for future team tasks. This combination should lead to an overall efficiency improvement for identifying coalition formations, as well as for continual learning.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.