H-DrunkWalk
| Autor: | Sihan Zeng, Aveek Purohit, Xinlei Chen, Liyao Gao, Carlos Ruiz, Stefano Carpin, Pei Zhang |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Situation awareness Computer Networks and Communications Computer science Testbed Real-time computing Swarm behaviour 020206 networking & telecommunications 02 engineering and technology Swarm intelligence 020901 industrial engineering & automation Dead reckoning 0202 electrical engineering electronic engineering information engineering Performance improvement Wireless sensor network Search and rescue |
| Zdroj: | ACM Transactions on Sensor Networks. 16:1-27 |
| ISSN: | 1550-4867 1550-4859 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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