Safe motion planning is essential for autonomous vessel operations, especially in challenging spaces such as narrow inland waterways. However, conventional motion planning approaches are often computationally intensive or overly conservative. This paper proposes a safe motion planning strategy combining Model Predictive Control (MPC) and Control Barrier Functions (CBFs). We introduce a time-varying inflated ellipse obstacle representation, where the inflation radius is adjusted depending on the relative position and attitude between the vessel and the obstacle. The proposed adaptive inflation reduces the conservativeness of the controller compared to traditional fixed-ellipsoid obstacle formulations. The MPC solution provides an approximate motion plan, and high-order CBFs ensure the vessel’s safety using the varying inflation radius. Simulation and real-world experiments demonstrate that the proposed strategy enables the fully-actuated autonomous robot vessel to navigate through narrow spaces in real time and resolve potential deadlocks, all while ensuring safety.

Fast and safe manipulation of flexible objects with a robot manipulator necessitates measures to cope with vibrations. Existing approaches either increase the task execution time or require complex models and/or additional instrumentation to measure vibrations. This paper develops a model-based method that overcomes these limitations. It relies on a simple pendulum-like model for modeling the beam, open-loop optimal control for suppressing vibrations, and does not require any exteroceptive sensors. We experimentally show that the proposed method drastically reduces residual vibrations – at least 90% – and outperforms the commonly used input shaping (IS) for trajectories with the same execution time. Besides, our method can also execute the task faster than IS with a minor reduction in vibration suppression performance, thereby facilitating the development of new solutions for flexible object manipulation tasks.

We present Tasho (Task specification for receding horizon control), an open-source Python toolbox that facilitates systematic programming of optimal control problem (OCP)-based robot motion skills. Separation-of-concerns is followed while designing the components of a motion skill, which promotes their modularity and reusability. This allows us to program complex motion tasks by configuring and composing simpler tasks. We provide templates for several basic tasks like point-to-point and end-effector path-following tasks to speed up prototyping. Internally, the task's symbolic expressions are computed using CasADi and the resulting OCP is transcribed using Rockit. A wide and growing range of mature open-source optimization solvers are supported for solving the OCP. Monitor functions can be easily specified and are automatically deployed with the motion skill, so that the generated motion skills can be easily embedded in a larger control architecture involving higher-level discrete controllers. The motion skills thus programmed can be directly deployed on robot platforms using the C-code generation capabilities of CasADi. The toolbox has been validated through several experiments both in simulation and on physical robot systems. The open-source toolbox can be accessed at: https://gitlab.kuleuven.be/meco-software/tasho