H-DrunkWalk

Abstract

Large-scale micro-aerial vehicle (MAV) swarms provide promising solutions for situational awareness in applications such as environmental monitoring, urban surveillance, search and rescue, and so on. However, these scenarios do not provide localization infrastructure and limit cost and size of on-board capabilities of individual nodes, which makes it challenging for nodes to autonomously navigate to suitable preassigned locations. In this article, we present H-DrunkWalk , a collaborative and adaptive technique for heterogeneous MAV swarm navigation in environments not formerly preconditioned for operation. Working with heterogeneous MAV swarm, the H-DrunkWalk achieves high accuracy through collaboration but still maintains a low cost of the entire swarm. The heterogeneous MAV swarm consists of two types of nodes: (1) basic MAVs with limited sensing, communication, computing capabilities and (2) advanced MAVs with premium sensing, communication, computing capabilities. The key focus behind this networked MAV swarm research is to (1) rely on collaboration to overcome limitations of individual nodes and efficiently achieve system-wide sensing objectives and (2) fully take advantage of advanced MAVs to help basic MAVs improve their performance. The evaluations based on real MAV testbed experiments and large-scale physical-feature-based simulations show that compared to the traditional non-collaborative and non-adaptive method (dead reckoning with map bias), our system achieves up to 6× reductions in location estimation errors, and as much as 3× improvements in navigation success rate under the given time and accuracy constraints. In addition, by comprehensively considering the environment, heterogeneous structure, and quality of location estimation, our H-DrunkWalk brings 2× performance improvement (on average) as that of a hardware upgrade.

Document Details

Document Type
Pub Defense Publication
Publication Date
Apr 17, 2020
Source ID
10.1145/3382094

Entities

People

  • Aveek Purohit
  • Carlos E. Ruiz
  • Liyao Gao
  • Pei Zhang
  • Sihan Zeng
  • Stefano Carpin
  • Xinlei Chen

Organizations

  • Carnegie Mellon University
  • Defense Advanced Research Projects Agency
  • Google
  • National Science Foundation
  • Purdue University
  • Tsinghua University
  • University of California

Tags

Fields of Study

  • Computer science

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