Heterogeneous teams of robots, leveraging a balance between autonomy and human interaction, bring powerful capabilities to the problem of exploring dangerous, unstructured subterranean environments. Here we describe the solution developed by Team CSIRO Data61, consisting of CSIRO, Emesent, and Georgia Tech, during the DARPA Subterranean Challenge. These presented systems were fielded in the Tunnel Circuit in August 2019, the Urban Circuit in February 2020, and in our own Cave event, conducted in September 2020. A unique capability of the fielded team is the homogeneous sensing of the platforms utilized, which is used to obtain a decentralized multi-agent SLAM solution on each platform (both ground agents and UAVs) using peer-to-peer communications. This approach enabled a shift in focus from constructing a pervasive communications network to relying on multi-agent autonomy, motivated by experiences in early circuit events. These experiences also showed the surprising capability of rugged tracked platforms for challenging terrain, which in turn led to the heterogeneous team structure based on a BIA5 OzBot Titan ground robot and an Emesent Hovermap UAV, supplemented by smaller tracked or legged ground robots. The ground agents use a common CatPack perception module, which allowed reuse of the perception and autonomy stack across all ground agents with minimal adaptation.
Extended Kalman filter (EKF) does not guarantee consistent mean and covariance under linearization, even though it is the main framework for robotic localization. While Lie group improves the modeling of the state space in localization, the EKF on Lie group still relies on the arbitrary Gaussian assumption in face of nonlinear models. We instead use von Mises filter for orientation estimation together with the conventional Kalman filter for position estimation, and thus we are able to characterize the first two moments of the state estimates. Since the proposed algorithm holds a solid probabilistic basis, it is fundamentally relieved from the inconsistency problem. Furthermore, we extend the localization algorithm to fully circular representation even for position, which is similar to grid patterns found in mammalian brains and in recurrent neural networks. The applicability of the proposed algorithms is substantiated not only by strong mathematical foundation but also by the comparison against other common localization methods.
This thesis presents CoLo-a performance evaluation system for two-dimensional cooperative localization algorithms. Multi-robot system have been used in a wide range of applications and cooperative localization is one of the fundamental tasks for mobile multirobot systems. However, developing cooperative localization algorithm is complex and timeconsuming. CoLo is created to reduce cooperative localization algorithm development cycle time. The system consists of two main parts: a physical experiment (CoLo-PE) for data collection and a software analysis tool (CoLo-AT) using real-world datasets to evaluate the performances of users' cooperative localization algorithms. CoLo uses an intuitive algorithm framework to allow researchers to conveniently add their cooperative localization algorithms to it. Instead of creating simulations or designing a new robotic testbed from the ground up, researchers only needed to load their algorithms in CoLo-AT and analyze them using data collected from CoLo-PE. Also, CoLo is aimed to create a standard so that effective comparisons can be made across research on localization algorithm. This paper details the design and operation of the physical experiment (CoLo-PE) which provides users guidelines to create their own robotic testbed with a ROS-based, scalable and affordable robotic team. And the paper explains how the software analysis tool (CoLo-AT) tests algorithms by running simulation processes to recreate the experiment trials using compatible real-world datasets of odometry data, measurement data, and the related groundtruth ii data. Researchers can test their algorithms and compare them with other state-of-the-art algorithms in various settings. CoLo-AT provides insightful metrics, and graphs include location error for localization accuracy and trace of state covariance to detect over-confident estimation results. It also has an animated plot to show the estimated trajectory and actual trajectory of each robot, which presents an intuitive visualization of the algorithm. CoLo has been used in the development of a published localization algorithm, where it was used to test the performance of the algorithm and compared to other existing algorithms objectively. CoLo provided the performance results in various setting and saved much time in experimental validation, which enabled a more rapid algorithm design process.
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