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.

Maintaining superiority on the battlefield is vital in ensuring mission success. The use of advanced technologies such as Autonomy and Artificial Intelligence offers the ability for the human to let the machines do the heavy lifting of tasks; especially those that require rapid processing of complex information and within a dynamic environment. This paper outlines the MASTER SOUP project that explores and demonstrates the use of AI to manage multiple sensors, whilst also introducing a new method to assist the multidisciplinary design team to better understand how the system deals with decisions that occur during the mission. The use of a method utilizing decision strings is outlined and discussed; whilst using previous studies that have theorized its use, this paper is the first to employ this approach. By adopting this method, it was found that it was beneficial to both Human Factors and AI Engineers in terms of designing the Human-Machine Teaming concept. The use of decision strings provides an intuitive methodology for identifying and deconstructing the nature of decisions within the Human-Machine Team. Further to this it can be used to the benefit of all members of the design team to facilitate the elicitation of design requirements that provide benefits across multidisciplinary fields. It is noted that this approach is new, we hope it will allow other researchers to adopt this methodology and contribute to the validation of this important method used within multidisciplinary design teams.

Source term estimation of a hazardous release remains a topic of significant interest in the robotics and state estimation communities, with application to many safety critical scenarios including gas or nuclear release, locating suspicious smells or response to emergency incidents. Limited sensing resources and time constraints mean that deciding on how to act in order to improve efficiency of estimation is also of significant interest. This paper has two main focuses: a sequential Monte Carlo technique for performing source term estimation from gas concentration measurements taken on a mobile sensor platform and a Monte Carlo tree search (MCTS) framework to perform sensor motion planning to maximise Kullback-Leibler divergence (KLD). Both algorithms are implemented in the open source tracking and estimation framework: Stone Soup, creating several key contributions to this Python based toolkit. The presented algorithm demonstrates superior performance when compared to a greedy myopic alternative when considering source position estimation error, release rate error and successful rate performance measures.

Automating sensing processes is of high interest to both the target tracking and the control community. Active sensing is focused on solving this task, usually with information based or task driven selection of optimal sensing actions. This paper presents an active sensing formulation that combines task based, in the form of standoff tracking, and information based active sensing by implementing the dual control for exploitation and exploration (DCEE) concept to control a mobile sensor platform with a limited field-of-view. The DCEE based cost function is integrated into the Monte Carlo tree search (MCTS) framework for non-myopic decision making. Using the Bernoulli particle filter for single target tracking with bearing-only measurements, the DCEE observer control method is benchmarked against the popular Rényi divergence information metric with two different parameterisations. Whilst the Rényi divergence performs marginally better when considering existence estimation, spatial results clearly demonstrate that our formulation is able to outperform the benchmark algorithm with improved target localisation performance resulting from outmanoeuvring of the target.