Towards trajectory planning from a given path for multirotor aerial robots trajectory tracking

Planning feasible trajectories given desired collision-free paths is an essential capability of multirotor aerial robots that enables the trajectory tracking task, in contrast to path following. This paper presents a trajectory planner for multirotor aerial robots carefully designed considering the requirements of real applications such as aerial inspection or package delivery, unlike other research works that focus on aggressive maneuvering. Our planned trajectory is formed by a set of polynomials of two kinds, acceleration/deceleration and constant velocity. The trajectory planning is carried out by means of an optimization that minimizes the trajectory tracking time, applying some typical constraints as m-continuity or limits on velocity, acceleration and jerk, but also the maximum distance between the trajectory and the given path. Our trajectory planner has been tested in real flights with a big and heavy aerial platform such the one that would be used in a real operation. Our experiments demonstrate that the proposed trajectory planner is suitable for real applications and it is positively influencing the controller for the trajectory tracking task.

[1]  Antonio Franchi,et al.  Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories , 2017, IEEE Robotics and Automation Letters.

[2]  Tamir Tassa,et al.  Planning high order trajectories with general initial and final conditions and asymmetric bounds , 2014, Int. J. Robotics Res..

[3]  Raffaello D'Andrea,et al.  A model predictive controller for quadrocopter state interception , 2013, 2013 European Control Conference (ECC).

[4]  Salah Sukkarieh,et al.  An Analytical Continuous-Curvature Path-Smoothing Algorithm , 2010, IEEE Transactions on Robotics.

[5]  Jose Luis Sanchez-Lopez,et al.  Visual Marker based Multi-Sensor Fusion State Estimation , 2017 .

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

[7]  Rachid Alami,et al.  Planning agile motions for quadrotors in constrained environments , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Min Wang,et al.  A Real-Time 3D Path Planning Solution for Collision-Free Navigation of Multirotor Aerial Robots in Dynamic Environments , 2019, J. Intell. Robotic Syst..

[9]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[10]  Pascual Campoy Cervera,et al.  A Multi-Layered Component-Based Approach for the Development of Aerial Robotic Systems: The Aerostack Framework , 2017, J. Intell. Robotic Syst..

[11]  Victor Ng-Thow-Hing,et al.  Fast smoothing of manipulator trajectories using optimal bounded-acceleration shortcuts , 2010, 2010 IEEE International Conference on Robotics and Automation.

[12]  Sven Behnke,et al.  Analytical time-optimal trajectory generation and control for multirotors , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[13]  Jose Luis Sanchez-Lopez,et al.  A robust real-time path planner for the collision-free navigation of multirotor aerial robots in dynamic environments , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

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

[15]  Sven Behnke,et al.  Fast full state trajectory generation for multirotors , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[16]  Miguel A. Olivares-Méndez,et al.  Evasive Maneuvering for UAVs: An MPC Approach , 2017, ROBOT.

[17]  Roland Siegwart,et al.  Inversion based direct position control and trajectory following for micro aerial vehicles , 2013, IROS 2013.

[18]  Raffaello D'Andrea,et al.  A computationally efficient algorithm for state-to-state quadrocopter trajectory generation and feasibility verification , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Miguel A. Olivares-Méndez,et al.  Vision based fuzzy control autonomous landing with UAVs: From V-REP to real experiments , 2015, 2015 23rd Mediterranean Conference on Control and Automation (MED).

[20]  P. Tsiotras,et al.  On-line Path Generation for Small Unmanned Aerial Vehicles Using B-Spline Path Templates , 2008 .

[21]  Eloy García,et al.  Tightly Bounding the Shortest Dubins Paths Through a Sequence of Points , 2017, Journal of Intelligent & Robotic Systems.

[22]  Panagiotis Tsiotras,et al.  On-Line Path Generation for Unmanned Aerial Vehicles Using B-Spline Path Templates , 2008 .

[23]  Helge J. Ritter,et al.  On-line planning of time-optimal, jerk-limited trajectories , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Timothy W. McLain,et al.  Spline Based Path Planning for Unmanned Air Vehicles , 2001 .

[25]  Martin Molina,et al.  AEROSTACK: An architecture and open-source software framework for aerial robotics , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[26]  Elizabeth A. Croft,et al.  Jerk-bounded manipulator trajectory planning: design for real-time applications , 2003, IEEE Trans. Robotics Autom..