With a growing number of citizens and tourists, the scarce public space, roads, and public transport in Amsterdam are experiencing rising pressure. Instead of utilizing the conventional transportation routes, Autonomous Surface Vessels (ASVs) could transport goods and people via the 165 canals present in Amsterdam.
However, urban canals are challenging for motion planning since the space can be narrow and contain other human-operated boats.
Unfortunately, there are limited existing works regarding motion planning for ASVs in urban canals with dynamic obstacles. Additionally, motion planners for other applications can not be applied directly because of the lacking traffic structure in the canals, the specific dynamics of ASVs, and the regulations applying to canals.
Therefore, the purpose of this thesis is to develop a motion planning framework that can plan dynamically feasible motions for ASVs in urban canals complying with regulations. These rules are not only mandatory but adhering to them makes the ASV's motion socially compliant, and therefore, more predictable by other canal users.
We build upon local model predictive contouring control and extend it with regulation compliance. Adherence to rules is achieved by constructing a new cost function for the optimization problem that allocates a higher cost on specific sides of the obstacle vessel. With this new cost function, the Roboat can behave according to the regulations in takeovers, head-on encounters, and crossings with boats from port and starboard.
Furthermore, the effect of predicting the obstacle vessel trajectories with a Social variational recurrent neural network is compared to the constant velocity model.
The entire motion planning framework is compared in simulation with LMPCC and the current motion planning and control framework of the Roboat, breadth first search combined with Nonlinear Model Predictive tracking Control (NMPC).
Additionally, the motion planning framework is implemented on a quarter-scale Roboat and is tested in an outdoor environment with disturbances.

Actuators using soft materials feature a large number of degrees of freedom. This tremendous flexibility allows a soft actuator to passively adapt its shape to the objects under interaction. In this paper, we propose a novel proprioception method for soft actuators during real-time interaction with previously unknown objects. First, we design a color-based sensing structure that instantly translates the inflation of a bellow into changes in color, which are subsequently detected by a miniaturized color sensor. The color sensor is small and, thus, multiple of them can be integrated into soft pneumatic actuators to reflect local deformations. Second, we make use of a feed-forward neural network to reconstruct a multivariate global shape deformation from local color signals. Our results demonstrate that deformations of the actuator during interaction, including sigmoid-like shapes, can be accurately reconstructed. The accurate shape sensing represents a significant step toward closed-loop control of soft robots in unstructured environments.

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This paper introduces a novel approach for sensing the bending deformation on soft robots by leveraging multicolor 3D printing. The measurement of deformation enables to complete the feedback loop of deformation control on soft actuators. The working principle of our approach is based on using compact color sensors to detect deformation that is visualized by the change of color ratios. Two novel designs are presented to generate color signals on 3D printed objects, which we call an external signal generator and an internal signal generator. Signal processing and calibration methods are developed to transform the raw RGB-data into a meaningful deformation metric. Our experimental tests taken on soft pneumatic actuators verify that color signals can be stably generated and captured to indicate the bending deformation. The results also demonstrate the usability of this sensing approach in deformation control.@en