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

Abstract

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 specification 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. KeywordsMulti-drone fleets, control, signal temporal logic, robustness maximization, quadrotors, crazyflie Disciplines Computer Engineering | Electrical and Computer EngineeringThis conference paper is available at ScholarlyCommons: https://repository.upenn.edu/mlab_papers/107 Abstract-The problem of safe planning and control for multidrone systems across a variety of missions is of critical importance, 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 specification 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.

Control from Signal Temporal Logic Specifications with Smooth Cumulative Quantitative Semantics

“…It is common in the literature to combine the robustness degree and control cost in a single optimization problem, using ξρ − ∑ J as the objective function [25]. We cannot do the same here since the combination of cumulative robustness ρ + with cost ∑ J in a single objective function can lead the value ofρ + to become zero again in the second stage of Algorithm 1.…”

Fly-by-Logic: A Tool for Unmanned Aircraft System Fleet Planning Using Temporal Logic

“…We then formulate the problem of mission satisfaction as that of maximizing a notion of robustness of STL specifications [3]. Using the approach of [4], we generate trajectories for all the UAS involved such that they satisfy the given mission objectives.…”

“…While there are multiple methods and tools that aim to solve such a problem, e.g. Mixed Integer Programmingbased [7] and based on stochastic heuristics [8], we use an underlying method [4] that is tailored for generating trajectories for multi-rotor UAS, including those that allow hovering, to satisfy STL specifications in continuous-time. A detailed comparison can be found in [4,9].…”

“…With this proposed tool we aim to bridge the gap between the ease-of-use of the UAS mission planning software popular among amateur drone enthusiasts, and the capabilities of academic tools [7,8] for control/planning with STL specifications. By doing this, we generate trajectories for multi UAS fleets that can satisfy complicated mission requirements while providing strong guarantees on mission satisfaction as well as the ability of the multi-rotor UAS to follow out their planned trajectories [4]. The main contributions of our tool are:…”

“…Here, these parameters are used to generate a function for the continuously differentially approximation of the robustness of the STL specification associated with the mission. An optimization to maximize this function [4] value is then formulated in Casadi [11]. Solving this optimization via IPOPT [12] results in a sequence of way-points for every UAS (uniformly apart in time).…”