Authors:
Mohamed Soliman
1
and
Rolf Findeisen
2
Affiliations:
1
Laboratory for System Theory and Automatic Control, Otto-von-Guericke Universitat Magdeburg, Germany
;
2
Control and Cyber-physical Systems Laboratory, TU Darmstadt, Darmstadt, Germany
Keyword(s):
Active Exploration, Dual Control, Model Predictive Control, Obstacle Avoidance, Autonomous Vehicles.
Abstract:
Navigating autonomous vehicles within a partially known environment to achieve a specific goal is an impor- tant yet challenging problem. It necessitates ensuring the safety of the vehicle along its trajectory, accounting for potentially unknown obstacles while maintaining the vehicle’s capability to navigate the path at all times. Conventionally, a safe path is devised based on the available offline information. This does not exploit ad- ditional environmental information that can be obtained during movement. In a hierarchical moving horizon planning and control framework, we recast the lower-level vehicle control problem as a dual control prob- lem, where the objective extends beyond merely following a given path to include active exploration. This exploration involves acquiring additional information to reduce the uncertainty about obstacles encountered, potentially improving overall performance. Recognizing that active exploration can incur additional costs or lead the vehicle in
to situations where obstacles impede the traveled path, we propose a fallback strategy that involves returning to a known, possibly suboptimal, path. The approach is illustrated through simulations.
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