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Model Predictive Path Integral Control Framework for Partially Observable Navigation: A Quadrotor Case Study

Abstract : Recently, Model Predictive Path Integral (MPPI) control algorithm has been extensively applied to autonomous navigation tasks, where the cost map is mostly assumed to be known and the 2D navigation tasks are only performed. In this paper, we propose a generic MPPI control framework that can be used for 2D or 3D autonomous navigation tasks in either fully or partially observable environments, which are the most prevalent in robotics applications. This framework exploits directly the 3D-voxel grid acquired from an on-board sensing system for performing collision-free navigation. We test the framework, in realistic RotorS-based simulation, on goal-oriented quadrotor navigation tasks in a cluttered environment, for both fully and partially observable scenarios. Preliminary results demonstrate that the proposed framework works perfectly, under partial observability, in 2D and 3D cluttered environments.The supplementary video attached to this work is available at: https://bit.ly/2PAbESO
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Submitted on : Wednesday, November 4, 2020 - 3:23:47 PM
Last modification on : Wednesday, October 6, 2021 - 11:38:01 AM

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Ihab Mohamed, Guillaume Allibert, Philippe Martinet. Model Predictive Path Integral Control Framework for Partially Observable Navigation: A Quadrotor Case Study. ICARCV 2020 - International Conerence on Control, Automation, Robotics and Vision, Dec 2020, Shenzhen, China. ⟨hal-02545951v2⟩

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