TY - CHAP A1 - Steinbrink, Marco A1 - Koch, Philipp A1 - May, Stefan A1 - Jung, Bernhard A1 - Schmidpeter, Michael T1 - State Machine for Arbitrary Robots for Exploration and Inspection Tasks T2 - Proceedings of the 2020 4th International Conference on Vision, Image and Signal Processing N2 - In this paper, a novel state machine for mobile robots is described that enables a direct use for exploration and inspection tasks. It offers a graphical user interface (GUI) to supervise the process and to issue commands if necessary. The state machine was developed for the open-source framework Robot Operating System (ROS) and can interface arbitrary algorithms for navigation and exploration. Interfaces to the commonly used ROS navigation stack and the explore_lite package are already included and can be utilized. In addition, routines for mapping and inspection can be added freely to adapt to the area of application. The state machine features a teleoperation mode to which it changes as soon as a respective command was issued. It also implements a software emergency stop and multiplexes all movement commands to the motor controller. To show the state machine's capabilities several simulations and real-world experiments are described in which it was used. KW - State Machine KW - Exploration KW - Inspection KW - Mobile Robotics Y1 - 2020 U6 - https://doi.org/10.1145/3448823.3448857 PB - ACM CY - New York, NY, USA ER - TY - CHAP A1 - Steinbrink, Marco A1 - Koch, Philipp A1 - Jung, Bernhard A1 - May, Stefan T1 - Rapidly-Exploring Random Graph Next-Best View Exploration for Ground Vehicles T2 - 2021 European Conference on Mobile Robots (ECMR) N2 - In this paper, a novel approach is introduced which utilizes a Rapidly-exploring Random Graph to improve sampling-based autonomous exploration of unknown environments with unmanned ground vehicles compared to the current state of the art. Its intended usage is in rescue scenarios in large indoor and underground environments with limited teleoperation ability. Local and global sampling are used to improve the exploration efficiency for large environments. Nodes are selected as the next exploration goal based on a gain-cost ratio derived from the assumed 3D map coverage at the particular node and the distance to it. The proposed approach features a continuously-built graph with a decoupled calculation of node gains using a computationally efficient ray tracing method. The Next-Best View is evaluated while the robot is pursuing a goal, which eliminates the need to wait for gain calculation after reaching the previous goal and significantly speeds up the exploration. Furthermore, a grid map is used to determine the traversability between the nodes in the graph while also providing a global plan for navigating towards selected goals. Simulations compare the proposed approach to state-of-the-art exploration algorithms and demonstrate its superior performance. Y1 - 2021 U6 - https://doi.org/10.1109/ecmr50962.2021.9568785 PB - IEEE ER -