Co-design of Control and Planning for Multi-rotor UAVs with Signal Temporal Logic Specifications

Urban Air Mobility (UAM), or the scenario where multiple manned and Unmanned Aerial Vehicles (UAVs) carry out various tasks over urban airspaces, is a transportation concept of the future that is gaining prominence. UAM missions with complex spatial, temporal and reactive requirements can be succinctly represented using Signal Temporal Logic (STL), a behavioral specification language. However, planning and control of systems with STL specifications is computationally intensive, usually resulting in planning approaches that do not guarantee dynamical feasibility, or control approaches that cannot handle complex STL specifications. Here, we present an approach to co-design the planner and control such that a given STL specification (possibly over multiple UAVs) is satisfied with trajectories that are dynamically feasible and our controller can track them with a bounded tracking-error that the planner accounts for. The tracking controller is formulated for the non-linear dynamics of the individual UAVs, and the tracking error bound is computed for this controller when the trajectories satisfy some kinematic constraints. We also augment an existing multi-UAV STL-based trajectory generator in order to generate trajectories that satisfy such constraints. We show that this co-design allows for trajectories that satisfy a given STL specification, and are also dynamically feasible in the sense that they can be tracked with bounded error. The applicability of this approach is demonstrated through simulations of multi-UAV missions.

[1]  P. Parrilo Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization , 2000 .

[2]  Majid Zamani,et al.  Approximate abstractions of control systems with an application to aggregation , 2018, Autom..

[3]  Vijay Kumar,et al.  Automated composition of motion primitives for multi-robot systems from safe LTL specifications , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Matthew Johnson-Roberson,et al.  Bridging the gap between safety and real-time performance in receding-horizon trajectory design for mobile robots , 2018, Int. J. Robotics Res..

[5]  Mo Chen,et al.  Robust Tracking with Model Mismatch for Fast and Safe Planning: an SOS Optimization Approach , 2018, WAFR.

[6]  Francesco Sabatino,et al.  Quadrotor control: modeling, nonlinearcontrol design, and simulation , 2015 .

[7]  Alberto L. Sangiovanni-Vincentelli,et al.  Model predictive control with signal temporal logic specifications , 2014, 53rd IEEE Conference on Decision and Control.

[8]  Dimos V. Dimarogonas,et al.  Control Barrier Functions for Signal Temporal Logic Tasks , 2019, IEEE Control Systems Letters.

[9]  Markus Hehn,et al.  A Computationally Efficient Motion Primitive for Quadrocopter Trajectory Generation , 2015, IEEE Transactions on Robotics.

[10]  Calin Belta,et al.  Robust temporal logic model predictive control , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[11]  Murat Arcak,et al.  Optimization Based Planner–Tracker Design for Safety Guarantees , 2019, 2020 American Control Conference (ACC).

[12]  Alberto Bemporad,et al.  Control of systems integrating logic, dynamics, and constraints , 1999, Autom..

[13]  George J. Pappas,et al.  Robustness of temporal logic specifications for continuous-time signals , 2009, Theor. Comput. Sci..

[14]  Stanley W. Smith,et al.  Continuous Abstraction of Nonlinear Systems using Sum-of-Squares Programming , 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).

[15]  George J. Pappas,et al.  Hierarchical control system design using approximate simulation , 2001 .

[16]  Astrid H. Brodtkorb,et al.  Continuous and discrete abstractions for planning, applied to ship docking , 2019, IFAC-PapersOnLine.

[17]  Mo Chen,et al.  FaSTrack: A modular framework for fast and guaranteed safe motion planning , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[18]  Marco Pavone,et al.  Robust Tracking with Model Mismatch for Fast and Safe Planning: an SOS Optimization Approach , 2018, Workshop on the Algorithmic Foundations of Robotics.

[19]  Houssam Abbas,et al.  Smooth operator: Control using the smooth robustness of temporal logic , 2017, 2017 IEEE Conference on Control Technology and Applications (CCTA).

[20]  Houssam Abbas,et al.  Fly-by-Logic: Control of Multi-Drone Fleets with Temporal Logic Objectives , 2018, 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS).

[21]  Dejan Nickovic,et al.  Monitoring Temporal Properties of Continuous Signals , 2004, FORMATS/FTRTFT.