Abstract: We will present UTRC’s research initiative in Autonomous and Intelligent Systems with an emphasis on complex human/machine intelligent systems including unmanned rotorcraft. The research, conducted by a diverse team of researchers in robotics, dynamical systems, control, applied mathematics, computer vision, and computer science (in partnership with several leading universities including CMU, MIT, UPenn, and Caltech) includes:
• Real-time algorithms for dynamic collision avoidance in an obstacle-rich environment using probabilistic roadmaps.
• Multi-vehicle missions including efficient search algorithms based on ergodic theory methods.
• Multi-vehicle navigation with imperfect and intermittent sensors in GPS degraded environments.
• Intelligent system design methodology including architectures for autonomy, human-machine systems, and formal verification.
In particular, we will provide an overview of a new hierarchical planning framework for mission planning and execution in uncertain and dynamic environments. We consider missions that involve motion planning in large, cluttered environments, trading off mission objectives while satisfying logical/spatial/temporal constraints. Our framework enables the decomposition of the planning problem across different layers, leveraging the difference in spatial and temporal scales of the mission objectives. Of the hierarchical planner we will describe, in some more detail, a novel motion planning algorithm that, starting from a probabilistic roadmap, efficiently constructs an expanded graph used to search for the optimal solution of a multi-objective problem that trades off path length and state estimation accuracy when navigating in a GPS denied environment. Tradeoff between optimality and computational complexity will be discussed as well as open challenge problems.
During the second half of this seminar, the focus turns to the problem of state estimation in GPS-denied, but structurally rich environments (e.g., urban canyons). A homological mapping and state estimation pipeline is formulated that makes use of dual simplicial nerve complexes for metric-free, time-asynchronous map building using only the binary (and visibility-based) detection of signals of opportunity (e.g., MAC addresses of WiFi access points). Using these constructs as an approximation to environmental free-space, the notion of “homological sensing” is proposed whereby an agent uses local homology computations to implicitly enumerate the number of physical structures (e.g., buildings) within some coarse proximity to its unknown location. Experimental results demonstrating the utility of said approach for coarse localization using a non-parametric Bayesian filter are presented.
We will conclude with research problems of interest to UTRC and discuss existing and future career opportunities in the broad area of robotics.