scholarly journals MP-RRT#: a Model Predictive Sampling-based Motion Planning Algorithm for Unmanned Aircraft Systems

2021 ◽  
Vol 103 (4) ◽  
Author(s):  
Stefano Primatesta ◽  
Abdalla Osman ◽  
Alessandro Rizzo
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Dynamic Constraints ◽  
Aircraft Systems ◽  
Planning Algorithm ◽  
Planning Problems ◽  
Optimal Graph

AbstractThis paper introduces a kinodynamic motion planning algorithm for Unmanned Aircraft Systems (UAS), called MP-RRT#. MP-RRT# joins the potentialities of RRT# with a strategy based on Model Predictive Control to efficiently solve motion planning problems under differential constraints. Similar to other RRT-based algorithms, MP-RRT# explores the map constructing an asymptotically optimal graph. In each iteration the graph is extended with a new vertex in the reference state of the UAS. Then, a forward simulation is performed using a Model Predictive Control strategy to evaluate the motion between two adjacent vertices, and a trajectory in the state space is computed. As a result, the MP-RRT# algorithm eventually generates a feasible trajectory for the UAS satisfying dynamic constraints. Simulation results obtained with a simulated drone controlled with the PX4 autopilot corroborate the validity of the MP-RRT# approach.

scholarly journals Anti-rollover Motion Planning for Automated Heavy Trucks

2020 ◽  
Author(s):  
Zhilin Jin ◽  
Jingxuan Li ◽  
Hong Wang ◽  
Jun Li ◽  
Chaosheng Huang
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Degrees Of Freedom ◽  
Optimal Path ◽  
Front Axle ◽  
Dynamic Constraints ◽  
Rollover Risk ◽  
Heavy Trucks ◽  
Planning Algorithm

Abstract Anti-rollover is an important performance for automated heavy trucks, which has been seldomly considered in the motion planning. This paper proposes an anti-rollover motion planning based on model predictive control (MPC) for automated heavy trucks. Taking the coupling of roll motion of sprung mass of the front axle with that of the drive axle into consideration, a seven degrees of freedom rollover dynamics model is established, and an evaluation index that can accurately describe the rollover motion is derived for heavy trucks. Then, a model predictive control strategy is designed for motion planning that combines the rollover dynamics, the artificial potential field for obstacle avoidance, and the trajectory tracking. In addition, the optimal path is calculated that considers collision avoidance, anti-rollover and vehicle dynamic constraints. Furthermore, three typical scenarios are applied to validate the performance of the proposed motion planning algorithm. The obtained results demonstrate that the proposed anti-rollover motion planning can effectively avoid collisions and reduce the rollover risk simultaneously when confronting edge scenarios.

FLIGHT SAFETY OF UNMANNED AIRCRAFT SYSTEMS IN NON-SEGREGATED AIRSPACE

2011 ◽  
Vol 42 (6) ◽  
pp. 801-815 ◽  
Author(s):  
Boris Sergeevich Alyoshin ◽  
Valeriy Leonidovich Sukhanov ◽  
Vladimir Mikhaylovich Shibaev
Keyword(s):  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Flight Safety ◽  
Aircraft Systems

Assuring Learning-Enabled Components in Small Unmanned Aircraft Systems

2021 ◽  
Author(s):  
Krishna Muvva ◽  
Justin M. Bradley ◽  
Marilyn Wolf ◽  
Taylor Johnson
Keyword(s):  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Aircraft Systems
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