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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.
In order to allow robust obstacle detection for autonomous freight traffic using freight trains or shunting locomotives, several different sensors are required. Humans and other objects must be detected so that the vehicle can stop in time. Laser scanners deliver distance information and are popular in robotics and automation. Cameras deliver further pieces of information on the environment and are especially useful for the classification of objects, but do not deliver distance measurements. Thermal cameras are ideal for the detection of humans based on their body temperature if the surrounding temperature is not too similar. It is only the combination of these different sensors which delivers enough robustness. Therefore a sensor fusion and an extrinsic calibration has to take place. This article presents an approach fusing a 2D and an 8-layer 3D laser scanner with a thermal and a Red-Green-Blue (RGB) camera, using a triangular calibration target taking all six degrees of freedom into account. The calibration was tested and the results validated during reference measurements and autonomous and manually controlled field tests. This sensor fusion approach was used for the obstacle detection of an autonomous shunting locomotive.
This publication describes an application of a
Truncated Signed Distance Mapping approach for disaster
intervention in underground mine shafts through geometrical
change detection of the shaft walls. The paper describes two
main problems of such an approach (aligning two potentially
huge point clouds and automatic change detection by comparing
the reconstructed volumes) and explains in detail the proposed
solution.
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.
This publication describes a novel approach to
generic robot navigation using elevation maps based on pointregion-Quadtrees. The described approach plans optimized trajectories in dependency of the robot’s morphology by detecting and rating obstacles. This is achieved by tailoring the tree based
elevation map to the robot’s design. The approach and the related
work, it is based on, is described in detail, experiments are
provided, which verify the results.
As public interest in autonomous driving systems grows, safety is becoming a critical issue. Extensive testing is therefore required before these systems can be deployed in real-world traffic. Simulation-based testing has proven to be a valuable tool for evaluating autonomous systems, but there remains a gap between simulation and real-world testing. A system may be well tested in simulation, but in real-world testing it may encounter sudden events that result in unpredictable behavior, and equipment may be damaged in the event of a malfunction. This paper describes a novel augmentation interface for Light Detection And Ranging (LiDAR) sensor data that aims to bridge this gap. The interface is designed to generate realistic test scenarios on live data streams, making it ideal for investigating special and borderline cases. The interface has been developed for the Robot Operating System (ROS) using the Point Cloud Library and is intended to be used for testing an autonomous shunting locomotive. With this interface, a data stream from a 3D LiDAR can be augmented with any given object represented by a point cloud, allowing for the use of data from both simulation and real-world environments.