This paper proposes a novel framework that combines both planning and learning-based trajectory generation methods to handle complex robotic assembly tasks. The framework utilizes MoveIt! for planning large-scale reaching motions and Dynamic Movement Primitives (DMPs) for precise grasping and placing movements, with both methods integrated into a single system controlled by a behavior tree. An impedance controller is employed to ensure smooth and safe execution of the generated trajectories, particularly in scenarios that involve human interaction.

The proposed framework was evaluated within the context of the European Space Agency-funded Rhizome project, which focuses on off-earth habitat construction. The project involves assembling habitats using custom-designed Voronoi-shaped building blocks, which were also utilized in experiments to test the framework. The results showed that combining planning for large-reaching motions with DMPs for detailed movements effectively addressed the limitations of each individual method, delivering a flexible and robust solution to the challenges of robotic assembly.

This research paper describes the design and implementation of a Model Predictive Controller, using economic engineering principles, on a model of the US economy. The purpose of the Model Predictive Controller is to mimic the US
government policy by maximizing the Net Domestic Product. The Model Predictive Controller is integrated with a bond graph model which is modeled based on macroeconomic principles and used to simulate the US economy. In all cases, the Model Predictive Controller was able to successfully stabilize the US economy, while maximizing the Net Domestic Product e.g. the economic output of the economy. The model described in this paper provides a promising new way of generating more accurate prospects of future market movements.