Fly-by-Logic: Control of Multi-Drone Fleets with Temporal Logic Objectives

The problem of safe planning and control for multi- drone systems across a variety of missions is of critical impor- tance, as the scope of tasks assigned to such systems increases. In this paper, we present an approach to solve this problem for multi-quadrotor missions. Given a mission expressed in Signal Temporal Logic (STL), our controller maximizes robustness to generate trajectories for the quadrotors that satisfy the STL spec- ification in continuous-time. We also show that the constraints on our optimization guarantees that these trajectories can be tracked nearly perfectly by lower level off-the-shelf position and attitude controllers. Our approach avoids the oversimplifying abstractions found in many planning methods, while retaining the expressiveness of missions encoded in STL allowing us to handle complex spatial, temporal and reactive requirements. Through experiments, both in simulation and on actual quadrotors, we show the performance, scalability and real-time applicability of our method

Technical Report: Anytime Computation and Control for Autonomous Systems

The correct and timely completion of the sensing and action loop is of utmost importance in safety critical autonomous systems. A crucial part of the performance of this feedback control loop are the computation time and accuracy of the estimator which produces state estimates used by the controller. These state estimators, especially those used for localization, often use computationally expensive perception algorithms like visual object tracking. With on-board computers on autonomous robots being computationally limited, the computation time of a perception-based estimation algorithm can at times be high enough to result in poor control performance. In this work, we develop a framework for co-design of anytime estimation and robust control algorithms while taking into account computation delays and estimation inaccuracies. This is achieved by constructing a perception-based anytime estimator from an off-the-shelf perception-based estimation algorithm, and in the process we obtain a trade-off curve for its computation time versus estimation error. This information is used in the design of a robust predictive control algorithm that at run-time decides a contract for the estimator, or the mode of operation of estimator, in addition to trying to achieve its control objectives at a reduced computation energy cost. In cases where the estimation delay can result in possibly degraded control performance, we provide an optimal manner in which the controller can use this trade-off curve to reduce estimation delay at the cost of higher inaccuracy, all the while guaranteeing that control objectives are robustly satisfied. Through experiments on a hexrotor platform running a visual odometry algorithm for state estimation, we show how our method results in upto a 10% improvement in control performance while saving 5-6% in computation energy as compared to a method that does not leverage the co-design