Andrew P. Kendall

Yutian Pang, Andrew Paul Kendall, John-Paul Clarke

Mission-critical operations of highly maneuverable Remotely Piloted Aircraft Systems (RPAS) require reliable communication to ensure safe integration into existing airspace. Understanding system-level performance under stochastic communication conditions is essential for estimating mission success and assessing operational risks. This study quantifies the impact of communication latency and complete signal loss on the mission completion performance of a highly maneuverable RPAS. The mission is defined as a static waypoint tracking task in three-dimensional airspace. We first derive mathematical formulations for key reliability metrics within the Required Communication Performance (RCP) framework. These stochastic communication factors, including latency and availability, are then incorporated into flight control simulations to evaluate system behavior. Extensive multiprocessing Monte Carlo simulations are conducted using high-performance computing to generate mission success rate and mission completion time envelopes. Results show significant degradation in flight performance as communication latency increases or availability decreases, which directly reduces the system stability margin. To better characterize this relationship, we introduce a new reliability metric, communicability, which integrates three key RCP metrics and provides insight into the maximum tolerable latency for flight control. The proposed framework informs RPAS design by revealing trade-offs between communication capability and flight control performance. The code used in this study is publicly available at this \href{https://github.com/YutianPangASU/comm-dynamics}{repository}.

Ethan N. Evans, Andrew P. Kendall, Evangelos A. Theodorou

Correlated with the trend of increasing degrees of freedom in robotic systems is a similar trend of rising interest in Spatio-Temporal systems described by Partial Differential Equations (PDEs) among the robotics and control communities. These systems often exhibit dramatic under-actuation, high dimensionality, bifurcations, and multimodal instabilities. Their control represents many of the current-day challenges facing the robotics and automation communities. Not only are these systems challenging to control, but the design of their actuation is an NP-hard problem on its own. Recent methods either discretize the space before optimization, or apply tools from linear systems theory under restrictive linearity assumptions in order to arrive at a control solution. This manuscript provides a novel sampling-based stochastic optimization framework based entirely in Hilbert spaces suitable for the general class of \textit{semi-linear} SPDEs which describes many systems in robotics and applied physics. This framework is utilized for simultaneous policy optimization and actuator co-design optimization. The resulting algorithm is based on variational optimization, and performs joint episodic optimization of the feedback control law and the actuation design over episodes. We study first and second order systems, and in doing so, extend several results to the case of second order SPDEs. Finally, we demonstrate the efficacy of the proposed approach with several simulated experiments on a variety of SPDEs in robotics and applied physics including an infinite degree-of-freedom soft robotic manipulator.