Visual observation of objects is essential for many robotic applications, such as object reconstruction and manipulation, navigation, and scene understanding. Machine learning algorithms constitute the state-of-the-art in many fields but require vast data sets, which are costly and time-intensive to collect. Automated strategies for observation and exploration are crucial to enhance the efficiency of data gathering. Therefore, a novel strategy utilizing the Next-Best-Trajectory principle is developed for a robot manipulator operating in dynamic environments. Local trajectories are generated to maximize the information gained from observations along the path while avoiding collisions. We employ a voxel map for environment modeling and utilize raycasting from perspectives around a point of interest to estimate the information gain. A global ergodic trajectory planner provides an optional reference trajectory to the local planner, improving exploration and helping to avoid local minima. To enhance computational efficiency, raycasting for estimating the information gain in the environment is executed in parallel on the graphics processing unit. Benchmark results confirm the efficiency of the parallelization, while real-world experiments demonstrate the strategy's effectiveness.

The collaboration and interaction between humans and robots intensify with ongoing research and industry needs. Robots require a motion planner that contributes to a safe environment for humans. This paper provides the online trajectory planner Moving Horizon Planning for Human-Robot Interaction (MHP4HRI), customizable for various robots, considering obstacles and humans in their environment. The planner generates motion commands in a moving horizon manner, similar to Model Predictive Control. This enables robots to react to dynamic changes in the environment in real-time. Descriptions of the planner and the underlying algorithms are given, as well as details about the provided framework regarding the benefits and usage for the community. Furthermore, we aim to provide a growing framework with new features in the future regarding the optimization and interaction with the environment, especially humans. The code, implemented mainly in C++ for the Robot Operating System (ROS), is available at GitHub: https://github.com/rst-tu-dortmund/mhp4hri.