Towards Optimal Solution of Robotic Routing Problems

Duration: 2022–2024

Description

In the project, we aim to establish theoretical foundations for solving robotic routing problems with continuous optimization. We plan to leverage existing theoretical results on lower bound estimations of the close enough traveling salesman problem and model-based multi-goal motion planning for the Dubins vehicle model towards a general solution to combinatorial routing with continuous optimization problems arising in robotic scenarios. We target to establish algorithmic foundations for solution quality estimations based on tight lower bounds that can be employed in efficient pruning of search space and thus support finding optimal solutions. We target to 1) optimal solutions of routing problems with cost corresponding to the continuous optimization of multivariable functions arising from limitations of robotic systems; 2) design new efficient algorithms with machine learning-enabled scalability to solve large practical instances of realistic problems; 3) establish complex analysis and empirical evaluation of the developed solutions in experimental scenarios with robotic systems.

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