5 accepted papers
This paper introduces a game-theoretical approach to the multi-robot task allocation problem, where each robot is considered as self-interested and cannot share its personal utility functions. We consider the case where each robot can execute multiple tasks and each task requires only one robot. For…
Effective task allocation is an essential component to the coordination of heterogeneous robots. This paper proposes a hybrid task allocation algorithm that improves upon given initial solutions, for example from the popular decentralized market-based allocation algorithm, via a derivative-free opti…
We propose a memory-constrained partition-based method to extract symbolic representations of the belief state and its dynamics in order to solve planning problems in a partially observable Markov decision process (POMDP). Our K-means partitioning strategy uses a fixed number of symbols to represent…
This paper proposes a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field. The algorithm is based on a bio-inspired approach known as Speeding-Up and Slowing-Down (SUSD) for distributed source seeking problems. Our algorithm leverages a Gauss…
Modern unmanned aerial vehicles (UAVs) rely on constant periodic sensor measurements to detect and avoid obstacles. However, constant checking and replanning are time and energy consuming and are often not necessary especially in situations in which the UAV can safely fly in uncluttered environments…