This manuscript presents an efficient algorithm for solving the inverse kinematics problem of a 6R robot manipulator to be deployed on embedded control hardware. The proposed method utilizes the geometric relationship between the end-effector and the base of the manipulator, resulting in a computationally efficient solution. The approach aims to minimize computational complexity and memory consumption while maintaining the accuracy and real-time performance demonstrated by simulations and verified by experimental results on an embedded system. Furthermore, the manipulator is analyzed in terms of singularities, limits, the workspace, and general solvability. Due to the simplicity of the algorithm, a platform-independent implementation is possible. As a result, the average calculation time is reduced by a factor of five to eight and the average error is decreased by a factor of fifty compared to a powerful analytical solver.

Future planetary exploration missions will rely heavily on efficient human–robot interaction to ensure astronaut safety and maximize scientific return. In this context, digital twins offer a promising tool for planning, simulating, and optimizing extravehicular activities. This study presents the development and evaluation of a digital twin for the AMADEE-24 analog Mars mission, organized by the Austrian Space Forum and conducted in Armenia in March 2024. Alternative local positioning methods were evaluated to enhance the system’s utility in Global Navigation Satellite System (GNSS)-denied environments. The digital twin integrates telemetry from the Aouda space suit simulators, inertial measurement unit motion capture (IMU-MoCap), and sensor data from the Intuitive Rover Operation and Collecting Samples (iROCS) rover. All nine experiment runs were reconstructed successfully by the developed digital twin. A comparative analysis of localization methods found that Simultaneous Localization and Mapping (SLAM)-based rover positioning and IMU-MoCap localization of the astronaut matched Global Positioning System (GPS) performance. Adaptive Cluster Detection showed significantly higher deviations compared to the previous GNSS alternatives. However, the IMU-MoCap method was limited by discontinuous segment-wise measurements, which required intermittent GPS recalibration. Despite these limitations, the results highlight the potential of alternative localization techniques for digital twin integration.

In the past decades, developments in the field of computer vision have made both the software and hardware more capable and more easily accessible. This has enabled otherwise complex vision systems to be used in other fields, such as autonomous robotics. Although vision systems in the visible light spectrum are commonplace in robotics nowadays, the thermal spectrum is still rarely used, even though it offers certain advantages. A thermal camera can sense the temperature of objects, is independent of illumination and can actually see through heavy smoke and fog. This makes it a useful tool in particular in the field of rescue robotics, where poor vision conditions are to be expected. In this paper, the feasibility of using two thermal cameras in a stereo vision setup to map indoor scenes is to be examined. It is meant to allow an autonomous robot to perceive its indoor surroundings as a 3D space, even in poor vision conditions. The biggest challenges are the calibration of thermal cameras and the proper filtering of the raw image and the resulting disparity map. Simple and easily implemented solutions are proposed for each of these issues.

Terrain modelling influences various aspects of mobile robot navigation. The ability to explore in rough terrain and to recognise ground conditions are essential to perform different activities efficiently, safely and satisfactorily. For this reason, intelligent vehicles and robotic systems need cognitive capabilities to understand the terrain and derive information from it. The information is mostly acquired and processed by very high resolution 3D-cameras and LiDAR sensors which provide full 360-degree environmental view to deliver accurate 3D data. The aim of this paper is to find out whether a low-cost sensor variant can measure sufficient and significant data from the terrain in order to modify the navigation behaviour and provide the correct control commands. In this paper we describe a low-cost sensor with Infrared Time-of-Flight (ToF) technology and 64 pixel depth image. Furthermore, different experiments on the detection of the sensor were conducted and with appropriate filters and signal processing algorithms the environmental perception could be significantly improved. In summary, our results provide both evidence and guidelines for the use of the selected sensor in environmental perception to improve local obstacle detection and terrain modelling, which we believe will lead to a very cost-effective improvement in competence and situational awareness.

In the field of automated working machines, not only is the general trend towards automation in industry, transport and logistics reflected, but new areas of application and markets are also constantly emerging. In this paper we present a pipeline for terrain classification in offroad environments and in the field of "automated maintenance of slopes", which offers potential for solving numerous socio-economic needs. Working tasks can be made more efficient, more ergonomic and, in particular, much safer, because mature, automated vehicles are used. At present, however, such tasks can only be carried out remotely or semi-automatically, under the supervision of a trained specialist. This only partially facilitates the work. The real benefit only comes when the supervising person is released from this task and is able to pursue other work. In addition to the development of a safe integrated system and sensor concept for use in public spaces as a basic prerequisite for vehicles licensed in the future, increased situational awareness of mobile systems through machine learning in order to increase their efficiency and flexibility is also of great importance.