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Health aware stochastic planning for persistent package delivery missions using quadrotors
- Source :
- IROS, Agha-mohammad
- Publication Year :
- 2014
- Publisher :
- IEEE, 2014.
-
Abstract
- In persistent missions, taking system’s health and capability degradation into account is an essential factor to predict and avoid failures. The state space in health-aware planning problems is often a mixture of continuous vehicle-level and discrete mission-level states. This in particular poses a challenge when the mission domain is partially observable and restricts the use of computationally expensive forward search methods. This paper presents a method that exploits a structure that exists in many health-aware planning problems and performs a two-layer planning scheme. The lower layer exploits the local linearization and Gaussian distribution assumption over vehicle-level states while the higher layer maintains a non-Gaussian distribution over discrete mission-level variables. This two-layer planning scheme allows us to limit the expensive online forward search to the mission-level states, and thus predict system’s behavior over longer horizons in the future. We demonstrate the performance of the method on a long duration package delivery mission using a quadrotor in a partially-observable domain in the presence of constraints and health/capability degradation.
- Subjects :
- Scheme (programming language)
Engineering
Mathematical optimization
Exploit
business.industry
Gaussian
Control engineering
Domain (software engineering)
symbols.namesake
Linearization
symbols
State space
Limit (mathematics)
Layer (object-oriented design)
business
computer
computer.programming_language
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
- Accession number :
- edsair.doi.dedup.....764253ee58fb8ffb9aab84071b07aeda