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We investigate the interaction between a human and a miniature autonomous blimp using a wand as pointing device. The wand movement generated by the human is followed by the blimp through a tracking controller. The Vector Integration to Endpoint (VITE) model, previously applied to human–computer interface (HCI), has been applied to model the human generated wand movement when interacting with the blimp. We show that the closed-loop human–blimp dynamics are exponentially stable. Similar to HCI using computer mouse, overshoot motion of the blimp has been observed. The VITE model can be viewed as a special reset controller used by the human to generate wand movements that effectively reduce the overshoot of blimp motion. Moreover, we have observed undershoot motion of the blimp due to its inertia. The asymptotic stability of the human–blimp dynamics is beneficial towards tolerating the undershoot motion of the blimp.

 

Expert based ensemble learning algorithms often serve as online learning algorithms for an unknown, possibly time‐varying, probability distribution. Their simplicity allows flexibility in design choices, leading to variations that balance adaptiveness and consistency. This article provides an analytical framework to quantify the adaptiveness and consistency of expert based ensemble learning algorithms. With properly selected states, the algorithms are modeled as a Markov chains. Then quantitative metrics of adaptiveness and consistency can be calculated through mathematical formulas, other than relying on numerical simulations. Results are derived for several popular ensemble learning algorithms. Success of the method has also been demonstrated in both simulation and experimental results.

 

Robustness of nonlinear systems can be analyzed by computing robust forward invariant sets (RFISs). Knowledge of the smallest RFIS of a system, can help analyze system performance under perturbations. A novel algorithm is developed to compute an approximation of the smallest RFIS for two‐dimensional nonlinear systems subjected to a bounded additive disturbance. The problem of computing an RFIS is formulated as a path planning problem, and the algorithm developed plans a path which iteratively converges to the boundary of an RFIS. Rigorous mathematical analysis shows that the proposed algorithm terminates in a finite number of iterations, and that the output of the proposed algorithm is an RFIS. Simulations are presented to illustrate the proposed algorithm, and to support the mathematical results. This work may aid future development, for use with higher dimensional systems.

 

We propose an algorithm using method of evolving junctions to solve the optimal path planning problems with piece-wise constant flow fields. In such flow fields, we prove that the optimal trajectories, with respect to a convex Lagrangian in the objective function, must be formed by piece-wise constant velocity motions. Taking advantage of this property, we transform the infinite dimensional optimal control problem into a finite dimensional optimization and use intermittent diffusion to solve the problems. The algorithm is proven to be complete. At last, we demonstrate the performance of the algorithm with various simulation examples. 

We propose a new concept named subschedulability to relax schedulability conditions on task sets in the context of scheduling and control co-design. Subschedulability is less conservative compared to schedulablity requirement with respect to network utilization. But it can still guarantee that all tasks can be executed before or within a bounded time interval after their deadlines. Based on the subschedulability concept, we derive an analytical timing model to check the sub-schedulability and perform online prediction of time-delays caused by real-time scheduling. A modified event-triggered contention-resolving MPC is presented to co-design the scheduling and control for the sub-schedulable control tasks. Simulation results are demonstrated to show the effectiveness of the proposed method. 

The performance of a model predictive controller depends on the accuracy of the objective and prediction model of the system. Although significant efforts have been dedicated to improving the robustness of model predictive control (MPC), they typically do not take a risk-averse perspective. In this paper, we propose a risk-aware MPC framework, which estimates the underlying parameter distribution using online Bayesian learning and derives a risk-aware control policy by reformulating classical MPC problems as Bayesian Risk Optimization (BRO) problems. The consistency of the Bayesian estimator and the convergence of the control policy are rigorously proved. Furthermore, we investigate the consistency requirement and propose a risk monitoring mechanism to guarantee the satisfaction of the consistency requirement. Simulation results demonstrate the effectiveness of the proposed approach. 

In this paper, a constrained cooperative Kalman filter is developed to estimate field values and gradients along trajectories of mobile robots collecting measurements. We assume the underlying field is generated by a polynomial partial differential equation with unknown time-varying parameters. A long short-term memory (LSTM) based Kalman filter, is applied for the parameter estimation leveraging the updated state estimates from the constrained cooperative Kalman filter. Convergence for the constrained cooperative Kalman filter has been justified. Simulation results in a 2-dimensional field are provided to validate the proposed method. 

Miniature autonomous blimps are autonomous lighter-than-air vehicles that offer a variety of benefits over other existing flight platforms. In particular, blimps offer long flight times, soft envelopes that are resilient to collisions, and friendly human-robot interaction opportunities. As such, these platforms are well suited for indoor applications and human-cluttered environments as catastrophic or life-threatening collisions are far less likely. In this abstract, we detail some of our ongoing efforts to enable autonomous behaviors for lighter-than-air platforms through various sensing, actuation, and swarming efforts.