Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments
| Autor: | Javier Alonso-Mora, Bruno Brito, Dariu M. Gavrila, Oscar de Groot, Laura Ferranti |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Mathematical optimization Control and Optimization Computer science 0211 other engineering and technologies Biomedical Engineering 02 engineering and technology Workspace Vehicle dynamics Computer Science::Robotics Computer Science - Robotics 020901 industrial engineering & automation Artificial Intelligence Motion and Path Planning 021103 operations research Mechanical Engineering Probabilistic logic Optimization and Optimal Control Uncertainty Trajectory optimization Autonomous robot Computer Science Applications Dynamics Human-Computer Interaction Collision Avoidance Planning Control and Systems Engineering Obstacle Robot Probability distribution Computer Vision and Pattern Recognition Safety Robots Robotics (cs.RO) |
| Zdroj: | IEEE Robotics and Automation Letters, 6(3) |
| ISSN: | 2377-3766 |
| Popis: | We present an optimization-based method to plan the motion of an autonomous robot under the uncertainties associated with dynamic obstacles, such as humans. Our method bounds the marginal risk of collisions at each point in time by incorporating chance constraints into the planning problem. This problem is not suitable for online optimization outright for arbitrary probability distributions. Hence, we sample from these chance constraints using an uncertainty model, to generate "scenarios", which translate the probabilistic constraints into deterministic ones. In practice, each scenario represents the collision constraint for a dynamic obstacle at the location of the sample. The number of theoretically required scenarios can be very large. Nevertheless, by exploiting the geometry of the workspace, we show how to prune most scenarios before optimization and we demonstrate how the reduced scenarios can still provide probabilistic guarantees on the safety of the motion plan. Since our approach is scenario based, we are able to handle arbitrary uncertainty distributions. We apply our method in a Model Predictive Contouring Control framework and demonstrate its benefits in simulations and experiments with a moving robot platform navigating among pedestrians, running in real-time. 8 pages, 4 figures. To be published in IEEE Robotics and Automation Letters |
| Databáze: | OpenAIRE |
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