Towards Real-Time Distributed Planning in Multi-Robot Systems

  • Mohamed Abdelkader

Student thesis: Doctoral Thesis

Abstract

Recently, there has been an increasing interest in robotics related to multi-robot applications. Such systems can be involved in several tasks such as collaborative search and rescue, aerial transportation, surveillance, and monitoring, to name a few. There are two possible architectures for the autonomous control of multi-robot systems. In the centralized architecture, a master controller communicates with all the robots to collect information. It uses this information to make decisions for the entire system and then sends commands to each robot. In contrast, in the distributed architecture, each robot makes its own decision independent from a central authority. While distributed architecture is a more portable solution, it comes at the expense of extensive information exchange (communication). The extensive communication between robots can result in decision delays because of which distributed architecture is often impractical for systems with strict real-time constraints, e.g. when decisions have to be taken in the order of milliseconds. In this thesis, we propose a distributed framework that strikes a balance between limited communicated information and reasonable system-wide performance while running in real-time. We implement the proposed approach in a game setting of two competing teams of drones, defenders and attackers. Defending drones execute a proposed linear program algorithm (using only onboard computing modules) to obstruct attackers from infiltrating a defense zone while having minimal local message passing. Another main contribution is that we developed a realistic simulation environment as well as lab and outdoor hardware setups of customized drones for testing the system in realistic scenarios. Our software is completely open-source and fully integrated with the well-known Robot Operating System (ROS) in hopes to make our work easily reproducible and for rapid future improvements.
Date of AwardApr 2018
Original languageEnglish (US)
Awarding Institution
  • Physical Science and Engineering
SupervisorJeff Shamma (Supervisor)

Keywords

  • multi-robot systems
  • distributed planning
  • real-time planning

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