Sampling-based motion planning algorithms like Rapidly-exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM) have proven to be effective in high-dimensional configuration spaces and can handle complex, real-world environments. However, this approach can suffer from the curse of dimensionality, where the number of samples needed to cover the configuration space grows exponentially with the number of dimensions.
We are seeking a motivated and talented student to join our research team as a Masterthesis Intern in robot motion planning: Leveraging statistical machine learning to increase the efficiency of motion planning through identification of the environment state. The successful candidate will have the opportunity to work on cutting-edge projects in the field of robotics and contribute to the advancement of motion planning algorithms.
Your responsibilities will be:
Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalleled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (f/m/x).Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.
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