Autonomous path planning for radar and sonar sensing faces significant challenges arising from dynamic targets, obstacle occlusions, and low signal-to-noise (SNR) conditions. We propose a hierarchical sensor scheduling framework that combines a long-horizon strategic planner, based on the Rapidly-exploring Random Tree star (RRT*) algorithm, with a fast-adapting tactical planner. Efficient coordination of the two planners is achieved via a novel message passing mechanism, enabling guidance of the sensor out of complex environments while maintaining effective target tracking. Additionally, we introduce an RRT* rerooting strategy that significantly reduces computation time and so expedites the update of the strategic plan. Extensive simulation results demonstrate that our proposed fusion approach outperforms conventional stand-alone short-term and long-term planners in challenging scenarios and low-SNR regimes,

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.

This paper announces version 1.0 of Stone Soup: the open-source tracking and state estimation framework. We highlight key elements of the framework and outline example applications and community activities.Stone Soup is engineered with modularity and encapsulation at its heart. This means that its many components can be put together in any number of ways to build, compare, and assure almost any type of multi-target tracking and fusion algorithm. Since its inception in 2017, it has aimed to provide the target tracking and state estimation community with an open, easy-to-deploy framework to develop and assess the performance of different types of trackers. Now, through repeated application in many use cases, implementation of a wide variety of algorithms, multiple beta releases, and contributions from the community, the framework has reached a stable point.In announcing this release, we hope to encourage additional adoption and further contributions to the toolkit. We also acknowledge and express appreciation for the many contributions of time and expertise donated by the tracking and fusion community.