Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels

Detection and Localization Sensor Assignment with Exact and Fuzzy Locations

Sensor networks introduce new resource allocation problems in which sensors need to be assigned to the tasks they best help. Such problems have been previously studied in simplified models in which utility from multiple sensors is assumed to combine additively. In this paper we study more complex utility models, focusing on two particular applications: event detection and target localization. We develop distributed algorithms to assign directional sensors of different types to multiple simultaneous tasks using exact location information. We extend our algorithms by introducing the concept of fuzzy location which may be desirable to reduce computational overhead and/or to preserve location privacy. We show that our schemes perform well using both exact or fuzzy location information

Research trends in combinatorial optimization

Acknowledgments This work has been partially funded by the Spanish Ministry of Science, Innovation, and Universities through the project COGDRIVE (DPI2017-86915-C3-3-R). In this context, we would also like to thank the Karlsruhe Institute of Technology. Open access funding enabled and organized by Projekt DEAL.Peer reviewedPublisher PD

Auction aggregation protocols for wireless robot-robot coordination

Abstract. Robots coordinate among themselves to select one of them to re-spond to an event reported to one of robots. The goal is to minimize the com-munication cost of selecting best robot, response time, and cost of performing the task. Existing solutions are either centralized, neglecting communication cost, assuming complete graph, or based on flooding with individual responses to robot decision maker (simple auction protocol), ignoring communication cost and response time bound. This article proposes auction aggregation protocols for task assignment in multi-hop wireless robot networks. Robot collector leads an auction and initiates response tree construction by transmitting search mes-sage. Each robot, after receiving the message, makes decision on whether to re-transmit search message, based on the estimated response cost of its robots up to k-hops away. Robots wait to receive the bids from its children in the search tree, aggregates responses by selecting the best bid, and forward it back toward the robot collector (auctioning robot). When distance is used as sole cost met-rics, traversal aggregation algorithm (RFT – routing toward the event with tra-versal of face containing the event) can be applied and is an optimal solution. Advantage of our auction aggregation over simple auction protocol is shown by simulation results