Optimal path planning for SUAS waypoint following in urban environments

Applications of a quadrotor with payload, particularly for chemical spraying, have increased in recent times. The variation in payload mass over time causes a change in the moments of inertia (MOI). Moreover, large tilt angles are required for fast reference tracking and external disturbance rejection. These variations in plant parameters (i.e., mass and inertia) and large tilt angles can degrade the control scheme’s performance and stability. This article proposes a linear matrix inequalities (LMIs)-based linear parameter varying (LPV) control scheme for a quadrotor subject to time-varying mass, time-varying inertia, mass flow rate, and large tilt angles. The control strategy is designed by solving LMIs derived from quadratic H∞ performance and D-stability. The robust stability and quadratic H∞ performance are assessed by LMIs. The efficacy of the proposed methodology is established using numerical simulations, and its performance is compared to the linear time-invariant (LTI) H∞ design with pole placement constraints. The results obtained show that the LPV control scheme gives better tracking performance in the presence of time-varying parameters, noise, and external disturbances without actuator saturation. In comparison to the LTI design technique, the proposed LPV scheme improves the rise time (tr), settling time (ts), and mean squared error (MSE) by up to 14%, 15%, and 30%, respectively. Moreover, smooth transitions are observed in the tilt angles and control signals with the LPV scheme, contrary to the LTI controller, which exhibits significant oscillations.

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“…. , N) represent the barycentric weights (for details, see [28,29]). The timevarying parameter vector ρ can be expressed as…”

Section: P(ρ) K(ρ)mentioning

confidence: 99%

LMIs-Based LPV Control of Quadrotor with Time-Varying Payload

2023

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“…It has also been shown that quantity (9) relates to the speed loss due to induced drag for aerodynamically controlled vehicles, e.g., fixed-wing UAVs [46]. Therefore, minimizing the quadratic energy consumption is a worthy goal for the guidance law design.…”

Section: Problem Formulationmentioning

confidence: 99%

Energy-Optimal Waypoint-Following Guidance Considering Autopilot Dynamics

Shin

Tsourdos

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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This article addresses the problem of energy-optimal waypointfollowing guidance for an unmanned aerial vehicle, considering a general autopilot dynamics model. The proposed guidance law is derived as a solution of a linear-quadratic optimal control problem in conjunction with a linearized kinematics model. The algorithm developed integrates path planning and path following into a single step, and can be applied to a general waypoint-following mission. Theoretical analysis reveals that previously suggested optimal pointto-point guidance laws are special cases of the proposed approach. Nonlinear numerical simulations clearly demonstrate the effectiveness of the proposed formulations.

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“…Other examples of conflict and collision avoidance techniques which have been developed for sUAS integration into the NAS include pre-planning algorithms. Pre-planning algorithms are typically based on optimization algorithms such as genetic algorithms, optimal control (Filippis and Guglieri, 2012), A* and its variations (Zollars et al, 2018), mixed integer linear programming (Galea et al, 2018), or dynamic programming. These algorithms cannot guarantee collision free paths because of inaccuracies within the models such as wind, dynamic conditions, and unexpected events.…”

Section: Introductionmentioning

confidence: 99%

Predicting sUAS conflicts in the national airspace with interacting multiple models and Haversine-based conflict detection system

Wells

Kumar

2023

Front. Aerosp. Eng.

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In this paper, a conflict detection system for small Unmanned Aerial Vehicles (sUAS), composed of an interacting multiple model state predictor and a Haversine-distance based conflict detector, is proposed. The conflict detection system was developed and tested via a random recursive simulation in the ROS-Gazebo physics engine environment. The simulation consisted of ten small unmanned aerial vehicles flying along randomly assigned way-point navigation missions within a confined airspace. Way-points are generated from a uniform distribution and then sent to each vehicle. The interacting multiple model state predictor runs on a ground-based system and only has access to current vehicle positional information. It does not have access to the future way-points of individual vehicles. The state predictor is based on Kalman filters that utilize constant velocity, constant acceleration, and constant turn models. It generates near-future position estimates for all vehicles operating within an airspace. These models are probabilistically fused together and projected into the near-future to generate state predictions. These state predictions are then passed to the Haversine distance-based conflict detection algorithm to compare state estimates and identify probable conflicts. The conflicts are detected and flagged based on tunable threshold values which compare distances between predictions for the vehicles operating within the airspace. This paper discusses the development of the random recursive simulation for the ROS-Gazebo framework and the derivation of the interacting multiple model along-with the Haversine-based future conflict detector. The results are presented via simulation to highlight mid-air conflict detection application for sUAS operations in the National Airspace.

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Optimal path planning for SUAS waypoint following in urban environments

Cited by 7 publications

References 11 publications

LMIs-Based LPV Control of Quadrotor with Time-Varying Payload

LMIs-Based LPV Control of Quadrotor with Time-Varying Payload

Energy-Optimal Waypoint-Following Guidance Considering Autopilot Dynamics

Predicting sUAS conflicts in the national airspace with interacting multiple models and Haversine-based conflict detection system

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