Non-Linear Model Predictive Control for autonomous landing of a UAV on a moving platform

Pozzan, Beniamino; Elaamery, Badr; Cenedese, Angelo

doi:10.1109/ccta49430.2022.9966054

Cited by 4 publications

(1 citation statement)

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“…For agile robotic systems such as quadrotors, most research effort is focused on leveraging this agility by creating fast, aggressive maneuvers [3]. Cuttingedge agile motion planning often uses gradient-based optimization [4], mixed-intenger quadratic programming [1], sample-and search-based methods [5], or some mixture of these techniques [6]. One assumption implicit within these approaches is the ability of a low level controller to accurately track the aggressive trajectories.…”

Section: Related Workmentioning

confidence: 99%

A Model Predictive-based Motion Planning Method for Safe and Agile Traversal of Unknown and Occluding Environments

Higgins

Bezzo

2022

2022 International Conference on Robotics and Automation (ICRA)

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Current motion planning approaches for autonomous mobile robots often assume that the low level controller of the system is able to track the planned motion with very high accuracy. In practice, however, tracking error can be affected by many factors, and could lead to potential collisions when the robot must traverse a cluttered environment. To address this problem, this paper proposes a novel recedinghorizon motion planning approach based on Model Predictive Path Integral (MPPI) control theory -a flexible sampling-based control technique that requires minimal assumptions on vehicle dynamics and cost functions. This flexibility is leveraged to propose a motion planning framework that also considers a data-informed risk function. Using the MPPI algorithm as a motion planner also reduces the number of samples required by the algorithm, relaxing the hardware requirements for implementation. The proposed approach is validated through trajectory generation for a quadrotor unmanned aerial vehicle (UAV), where fast motion increases trajectory tracking error and can lead to collisions with nearby obstacles. Simulations and hardware experiments demonstrate that the MPPI motion planner proactively adapts to the obstacles that the UAV must negotiate, slowing down when near obstacles and moving quickly when away from obstacles, resulting in a complete reduction of collisions while still producing lively motion.

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