Abstract
Computer-aided design (CAD) and 3D modeling are similar, but they have different functionalities and applications. CAD is a fundamental tool to create object models, design parts, and create 2D schematics from 3D designed objects that can later be used in manufacturing. Meanwhile, 3D modeling is mostly used in entertainment, to create meshes for animation and games. When there is the necessity of using real-life object models in game engines, a conversion process is required to go from CAD to 3D meshes. Converting from the continuous domain of CAD to the discrete domain of 3D models represents a trade-off between processing cost and visual accuracy, in order to obtain the best user experience. This work explores different methods for the creation of meshes and the reduction of the number of polygons used to represent them. Based on these concepts, an interactive application was created to allow the users to control how the model looks in the game engine, in a simple way, while also optimizing and simplifying the mapping of textures for the generated meshes. This application (CADto3D) generates accurate 3D models based on CAD surfaces while giving the user more control over the final result than other current solutions.

Abstract
Intelligent wheelchairs aim to improve mobility limitations by providing ingenious mechanisms to control and move the chair. This paper aims to enhance the autonomy level of intelligent wheelchair navigation by applying reinforcement learning algorithms to move the chair to the desired location. Also, as a second objective, add one more chair and move both chairs in pairs to promote group social activities. The experimental setup is based on a simulated environment using gazebo and ROS where a leader chair moves towards a goal, and the follower chair should navigate near the leader chair. The collected metrics (time to complete the task and the trajectories of the chairs) demonstrated that Deep Q-Network (DQN) achieved better results than the Q-Learning algorithm by being the unique algorithm to accomplish the pair navigation behaviour between two chairs.

Abstract
Content generation is a heavy task in virtual worlds design. Procedural content generation techniques aim to agile this process by automating the 3D modelling with some degree of parametrisation. The novelty of this work is the procedural generation of the marine alga (Asparagopsis armata), taking into consideration the underwater environmental factors. The depth and the occlusion were the two parameters in this study to simulate how the alga growth is influenced by the environment where the alga grows. Starting by building a prototype to explore different L-systems categories to model the alga, the stochastic L-systems with parametric features were selected to generate different alga plasticities. Qualitative methods were used to evaluate the designed grammar and alga's animation results by comparing videos and images of the Asparagopsis armata with the computer-generated versions. © 2022 University of West Bohemia. All rights reserved.

Abstract
Content generation is a heavy task in virtual worlds design. Procedural content generation techniques aim to agile this process by automating the 3D modelling with some degree of parametrisation. The novelty of this work is the procedural generation of the marine alga (Asparagopsis armata), taking into consideration the underwater environmental factors. The depth and the occlusion were the two parameters in this study to simulate how the alga growth is influenced by the environment where the alga grows. Starting by building a prototype to explore different L-systems categories to model the alga, the stochastic L-systems with parametric features were selected to generate different alga plasticities. Qualitative methods were used to evaluate the designed grammar and alga's animation results by comparing videos and images of the Asparagopsis armata with the computer-generated versions. © 2022 University of West Bohemia. All rights reserved.

Abstract
The preservation and sustainability of the marine ecosystem could benefit from the surge of new technologies to design autonomous vehicles. These underwater robots operate in a complex environment where the loss of human lives is highly probable. Consequently, a considerable percentage of the ocean remains unexplored due to the complexities of the underwater environment. Robotics can be a solution to overcome these limitations. However, training these complex systems is challenging and resource expensive. Human-in-the-loop input is essential in accelerating the training process by teaching the robots how to perform in specific scenarios and validate the simulated environment. This work presents a case study that simulates the dynamics of a Remotely Operated Vehicle in an underwater environment and uses imitation learning to train the vehicle to navigate autonomously toward a target. It was possible to measure and observe the similarity between the expert and the autonomous trajectories generated by the ROV. However, the imitation learning performance cannot surpass the expert, considering the time and the number of successes in finding the target. © ESM 2022. All rights reserved.