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.
ASPIRe proposes an informative trajectory planning approach for mobile target search and tracking in cluttered environments, achieving real-time computation and outperforming benchmark methods.
ASPIRe proposes an adaptive particle filter tree with sigma point-based mutual information reward approximation for mobile target search and tracking in cluttered environments.
ASPIRe bietet eine effiziente und präzise Methode für die mobile Zielsuche und -verfolgung in unübersichtlichen Umgebungen.