Neset Unver Akmandor’s PhD Proposal Review

“Improving Computational Efficiency of Motion Planning Algorithms for Mobile and Time-Dependent Robotic Tasks in Dynamic Environments”

Abstract:

Robots will become a part of our lives at home as personal assistants. Although their current functionality is highly restricted to specific tasks and environments, their practicality encourages robotics engineers for further advancement. Especially, mobile robots with manipulation capabilities have a huge potential to support humans in physically demanding workplaces, such as warehouses and hospitals. Considering the complexity of the human level tasks and the dynamic settings, the state-of-the-art robot motion planning methods need to be improved in terms of their computational efficiency. To contribute on closing the gap, this proposal presents three novelties whose applications focus on mobile robots in dynamic environments. First, we introduce a reactive navigation framework in 3D workspaces. The proposed approach does not rely on the global map information and achieves fast navigation by employing motion primitives and their heuristic evaluations on the-fly. Second, we present a Deep Reinforcement Learning based navigation approach in which we define the occupancy observations as heuristic evaluations of motion primitives, rather than using raw sensor data. It utilizes occupancy observations in different data structures to analyze their effects on both training process and navigation performance. We train and test our methodology on two different robots within challenging physics-based simulation environments including static and dynamic obstacles. Finally, we propose a computationally efficient framework for trajectory planning for robots with high degrees-of freedom while adapting its system model, constraints and time-dependent target state using the latest information from the dynamic environment.

Committee:

Dr. Taskin Padir (Advisor)
Dr. Pau Closas
Dr. Michael Everett
Dr. Erdal Kayacan