Trajectory Planning for Quadrotor Swarms

We describe a method for multirobot trajectory planning in known, obstacle-rich environments. We demonstrate our approach on a quadrotor swarm navigating in a warehouse setting. Our method consists of following three stages: 1) roadmap generation that generates sparse roadmaps annotated with possible interrobot collisions; 2) discrete planning that finds valid execution schedules in discrete time and space; 3) continuous refinement that creates smooth trajectories. We account for the downwash effect of quadrotors, allowing safe flight in dense formations. We demonstrate computational efficiency in simulation with up to 200 robots and physical plausibility with an experiment on 32 nano-quadrotors. Our approach can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes.

[1]  Steven M. LaValle,et al.  Multi-agent Path Planning and Network Flow , 2012, WAFR.

[2]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning , 2011, Int. J. Robotics Res..

[3]  Steven M. LaValle,et al.  Structure and Intractability of Optimal Multi-Robot Path Planning on Graphs , 2013, AAAI.

[4]  Lydia E. Kavraki,et al.  The Open Motion Planning Library , 2012, IEEE Robotics & Automation Magazine.

[5]  Howie Choset,et al.  M*: A complete multirobot path planning algorithm with performance bounds , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Charles Richter,et al.  Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments , 2016, ISRR.

[7]  Vijay Kumar,et al.  Goal Assignment and Trajectory Planning for Large Teams of Aerial Robots , 2013, Robotics: Science and Systems.

[8]  Jonathan P. How,et al.  Decoupled multiagent path planning via incremental sequential convex programming , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Kostas E. Bekris,et al.  Sparse roadmap spanners for asymptotically near-optimal motion planning , 2014, Int. J. Robotics Res..

[10]  Soon-Jo Chung,et al.  Decentralized Model Predictive Control of Swarms of Spacecraft Using Sequential Convex Programming , 2013 .

[11]  Ella M. Atkins,et al.  An Empirical Model of Rotorcrafy UAV Downwash for Disturbance Localization and Avoidance , 2015 .

[12]  Vijay Kumar,et al.  Safe and complete trajectory generation for robot teams with higher-order dynamics , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[13]  Nathan R. Sturtevant,et al.  Conflict-based search for optimal multi-agent pathfinding , 2012, Artif. Intell..

[14]  N. Roy,et al.  Polynomial Trajectory Planning for Quadrotor Flight , 2012 .

[15]  Nathan R. Sturtevant,et al.  Meta-Agent Conflict-Based Search For Optimal Multi-Agent Path Finding. , 2012 .

[16]  Sven Koenig,et al.  Optimal Target Assignment and Path Finding for Teams of Agents , 2016, AAMAS.

[17]  Vijay Kumar,et al.  Minimum snap trajectory generation and control for quadrotors , 2011, 2011 IEEE International Conference on Robotics and Automation.

[18]  Steven M. LaValle,et al.  Planning optimal paths for multiple robots on graphs , 2012, 2013 IEEE International Conference on Robotics and Automation.

[19]  Srinivas Akella,et al.  Coordinating Multiple Robots with Kinodynamic Constraints Along Specified Paths , 2005, Int. J. Robotics Res..

[20]  Gaurav S. Sukhatme,et al.  Downwash-aware trajectory planning for large quadrotor teams , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[21]  Vijay Kumar,et al.  Mixed-integer quadratic program trajectory generation for heterogeneous quadrotor teams , 2012, 2012 IEEE International Conference on Robotics and Automation.

[22]  Stephen P. Boyd,et al.  CVXGEN: a code generator for embedded convex optimization , 2011, Optimization and Engineering.

[23]  Jorge Nocedal,et al.  A trust region method based on interior point techniques for nonlinear programming , 2000, Math. Program..

[24]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[25]  Vijay Kumar,et al.  The GRASP Multiple Micro-UAV Testbed , 2010, IEEE Robotics & Automation Magazine.

[26]  Wolfram Burgard,et al.  OctoMap: an efficient probabilistic 3D mapping framework based on octrees , 2013, Autonomous Robots.

[27]  Roland Siegwart,et al.  Optimal Reciprocal Collision Avoidance for Multiple Non-Holonomic Robots , 2013 .

[28]  Mircea D. Farcas,et al.  About Bernstein polynomials , 2008 .

[29]  Roni Stern,et al.  Suboptimal Variants of the Conflict-Based Search Algorithm for the Multi-Agent Pathfinding Problem , 2014, SOCS.

[30]  Dinesh Manocha,et al.  FCL: A general purpose library for collision and proximity queries , 2012, 2012 IEEE International Conference on Robotics and Automation.

[31]  Angela P. Schoellig,et al.  Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approach , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[32]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[33]  Jur P. van den Berg,et al.  Reciprocal collision avoidance for robots with linear dynamics using LQR-Obstacles , 2013, 2013 IEEE International Conference on Robotics and Automation.

[34]  Sven Koenig,et al.  Formation change for robot groups in occluded environments , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[35]  Paul T. Boggs,et al.  Sequential Quadratic Programming , 1995, Acta Numerica.

[36]  M. E. Flores.  Real-Time Trajectory Generation for Constrained Nonlinear Dynamical Systems Using Non-Uniform Rational B-Spline Basis Functions , 2008 .