Autonomous drone simulation using ROS and Gazebo @ UniBW Munich | Random tree environment скачать в хорошем качестве

Autonomous drone simulation using ROS and Gazebo @ UniBW Munich | Random tree environment

The work integrates the popular RotorS simulator platform [1] developed at ETH Zurich and the planning ROS package MoveIt! We also add appropriate lidar sensors for 360-degree information of the environment. Additionally, a local mapper and a local trajectory generator are added derived from ideas in [2], [3]. The simulation environment is fully open source. Email for further information: mahathi.bhargavapuri@unibw.de This simulation was performed at the University of the Federal Armed Forces, Munich by Mahathi Bhargavapuri and Gunther Reissig. This work is partially funded by the European Leadership Joint Undertaking (ECSEL JU), under grant agreement No. 876019, the ADACORSA project - Airborne Data Collection on Resilient System Architectures. [1] F. Furrer, M. Burri, M. Achtelik, and R. Siegwart, Robot Operating System (ROS): The Complete Reference (Volume 1). Cham: Springer International Publishing, 2016, ch. RotorS—A Modular Gazebo MAV Simulator Framework, pp. 595–625. [2] M. Ryll, J. Ware, J. Carter, and N. Roy, “Efficient trajectory planning for high-speed flight in unknown environments,” in 2019 International conference on robotics and automation (ICRA). IEEE, 2019, pp. 732–738. [3] J. Tordesillas, B. T. Lopez, and J. P. How, “FASTER: Fast and safe trajectory planner for flights in unknown environments,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019.