Multiagent Information Coverage Inspired Target Searching and Tracking in Stone Soup

Searching and tracking for mobile targets with multiple mobile sensing platforms is a relevant capability for many real-world scenarios such as search and rescue and surveillance. This paper presents an approach to performing searching and tracking for a single target of interest using multiple mobile sensor systems in urban environments. Formulating the searching and tracking problem as a partially observable Markov decision process, a coverage control objective inspired reward function is designed, to create a single objective function for both searching and tracking behaviours. Furthermore, the approach alleviates differentiability requirements in typical coverage control approaches. Visibility informed Bernoulli filter provides a suitable tracking algorithm estimating both probability that a target exists and its state. Road network information is used to inform the information coverage objective for scenario tailored search behaviour. Monte Carlo tree search is adopted to implement the algorithm non-myopically and balance exploitation of immediate rewards with exploration of potential future gains. Contribution to the open source Stone Soup framework creates several new components for the toolkit, where the flexibility of the approach can be leveraged with the established wealth of tracking and filtering algorithms. Simulated Urban Mobility software package is integrated with Stone Soup to create a realistic urban simulation scenario with two sensing agents. Through Monte Carlo simulations, the algorithm demonstrates efficient search behaviour, good target tracking performance and effective coverage of the target once found.