Core Concepts
The author proposes ASPIRe, an informative trajectory planning approach for mobile target search and tracking in cluttered environments. By utilizing sigma point-based mutual information approximation, ASPIRe outperforms benchmark methods in terms of search efficiency and estimation accuracy.
Abstract
ASPIRe introduces a novel approach to trajectory planning for target search and tracking. It combines adaptive particle filter tree with sigma point-based mutual information approximation to achieve real-time computation and superior performance compared to existing methods. The simulations and experiments demonstrate the effectiveness of ASPIRe in various scenarios.
Key Points:
- ASPIRe is designed for mobile target search and tracking in cluttered environments.
- The approach utilizes sigma point-based mutual information approximation.
- Adaptive Particle Filter Tree (APFT) is developed for informative trajectory planning.
- ASPIRe outperforms benchmark methods in terms of search efficiency and estimation accuracy.
- Real-world experiments validate the effectiveness of ASPIRe in different scenarios.
Stats
"Simulations and physical experiments demonstrate that ASPIRe achieves real-time computation."
"ASPIRe outperforms benchmark methods in terms of both search efficiency and estimation accuracy."
Quotes
"No measurement can be obtained when the target is outside the FOV."
"ASPIRe significantly outperforms other methods by a large margin."