A κITE in the wind: Smooth trajectory optimization in a moving reference frame

Dugar, Vishal; Choudhury, Sanjiban; Scherer, Sebastian

doi:10.1109/icra.2017.7989017

Cited by 11 publications

(11 citation statements)

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“…In this section, we present

κ

ITE (Curvature (

κ

) parameterization Is very Time Efficient), our approach for solving the optimization problem to generate the route guide trajectory (Dugar et al, , 2017b). As we discussed in Section , the route guide trajectory is the solution to the optimization problem with the additional relaxation of the safety constraint

σ (t) {\in 𝚺}_{valid}

.…”

Section: Route Optimizer—the Kite Algorithmmentioning

confidence: 99%

“…The sequence of events are essentially triggered by the distance to the landing site as illustrated in Figure 12. (Dugar et al, 2017a(Dugar et al, , 2017b). As we discussed in Section 4, the route guide trajectory is the solution to the optimization problem 1 with the additional relaxation of the safety constraint σ ( ) ∈ Σ t valid .…”

Section: (Takeoff)mentioning

confidence: 99%

“…This paper builds upon our previous works on most of the individual components of our framework: using an ensemble of planners (Choudhury, Arora, & Scherer, , ), dynamics projection filter (Choudhury & Scherer, ), guaranteeing safety (Arora, Choudhury, Althoff, & Scherer, , ), training a dynamic list of planners (Tallavajhula & Choudhury, ), and smooth route optimization (Dugar, Choudhury, & Scherer, , 2017b). In this paper, for the first time we present a description of the unified architecture, details of the trajectory planning framework and extensive flight test evaluation of the whole system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

Choudhury

Dugar

Maeta

et al. 2019

Journal of Field Robotics

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Autonomous flight of unmanned full‐size rotor‐craft has the potential to enable many new applications. However, the dynamics of these aircraft, prevailing wind conditions, the need to operate over a variety of speeds and stringent safety requirements make it difficult to generate safe plans for these systems. Prior work has shown results for only parts of the problem. Here we present the first comprehensive approach to planning safe trajectories for autonomous helicopters from takeoff to landing. Our approach is based on two key insights. First, we compose an approximate solution by cascading various modules that can efficiently solve different relaxations of the planning problem. Our framework invokes a long‐term route optimizer, which feeds a receding‐horizon planner which in turn feeds a high‐fidelity safety executive. Secondly, to deal with the diverse planning scenarios that may arise, we hedge our bets with an ensemble of planners. We use a data‐driven approach that maps a planning context to a diverse list of planning algorithms that maximize the likelihood of success. Our approach was extensively evaluated in simulation and in real‐world flight tests on three different helicopter systems for duration of more than 109 autonomous hours and 590 pilot‐in‐the‐loop hours. We provide an in‐depth analysis and discuss the various tradeoffs of decoupling the problem, using approximations and leveraging statistical techniques. We summarize the insights with the hope that it generalizes to other platforms and applications.

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“…In this section, we present

κ

ITE (Curvature (

κ

σ (t) {\in 𝚺}_{valid}

.…”

Section: Route Optimizer—the Kite Algorithmmentioning

confidence: 99%

Section: (Takeoff)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

Choudhury

Dugar

Maeta

et al. 2019

Journal of Field Robotics

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“…the nominal straight flight. Solutions for the timeoptimal problem with free or constant airspeed flight are found in [9], [14], [25]- [27], as well as solutions based on Pursuit or Dubins [11], [28], and other guidance algorithms based on numerical procedures [10], [12], [15], [29], [30]. The classic Zermelo-Markov-Dubins problem has been studied in [28], but again using sharp turns and assuming constant speed.…”

Section: Introductionmentioning

confidence: 99%

Optimal Aerial Guidance in General Wind Fields

Paiva¹,

Pereira²

2020

Preprint

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<div>This paper presents an optimal guidance approach</div><div>for a UAV point-to-point navigation in 2D, under wind perturbation, with a general desired airspeed profile. A cost function weighting the travel time and the control effort is minimized through the Pontryagin’s Minimum Principle, involving the derivatives of the airspeed velocities. Two iterative procedures for a guidance algorithm under general wind fields were developed, including an analytical solution for the optimal heading in minimum-time paths. Different cases from the literature are compared and a hard wind scenario test is presented.</div>

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“…Examples are given. In the context of model predictive control, real‐time solution of the DOP is required, applications prone to varying environmental conditions need to adapt their optimal signal trajectories correspondingly, and furthermore, in the context of mechatronic design optimization, both optimal trajectories and design parameters need to be determined in a limited time span for economic reasons …”

Section: Introductionmentioning

confidence: 99%

A trajectory‐based sampling strategy for sequentially refined metamodel management of metamodel‐based dynamic optimization in mechatronics

Lefebvre

Belie

Crevecoeur

2018

Optim Control Appl Methods

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Dynamic optimization problems based on computationally expensive models that embody the dynamics of a mechatronic system can result in prohibitively long optimization runs. When facing optimization problems with static models, reduction in the computational time and thus attaining convergence can be established by means of a metamodel placed within a metamodel management scheme. This paper proposes a metamodel management scheme with a dedicated sampling strategy when using computationally demanding dynamic models in a dynamic optimization problem context. The dedicated sampling strategy enables to attain dynamically feasible solutions where the metamodel is locally refined during the optimization process upon satisfying a feasibility-based stopping condition. The samples are distributed along the iterate trajectories of the sequential direct dynamic optimization procedure. Algorithmic implementation of the trajectory-based metamodel management is detailed and applied on two case studies involving dynamic optimization problems. These numerical experiments illustrate the benefits of the presented scheme and its sampling strategy on the convergence properties. It is shown that the acceleration of the solution time of the dynamic optimization problem can be achieved when evaluating the metamodel that is lower than 90% compared to the computationally expensive model. KEYWORDSmetamodel-based optimization, metamodel management, sequential dynamic optimization INTRODUCTIONA recurring task within the area of mechatronics is the identification of a set of time-dependent signals (ie, the optimal state trajectory and the corresponding optimal control [OC] policy) to achieve improved system behavior with respect to a well-defined task or objective. 1-3 Reduction in energy consumption, minimization of the execution time, increased safety of operation, and robust tracking are only a few examples of such tasks. A rigorous framework to formulate such dynamic optimization problems (DOPs) is provided by the OC framework, 1 which requires a dynamic model of the system. Having a high-fidelity dynamic model is key to assure close correspondence between optimized and actual system behavior. Dynamic models may incorporate multidomain physics and subsystem interaction, rendering simulation times that can be much slower than real time (see, eg, other works 4-9 ).

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A κITE in the wind: Smooth trajectory optimization in a moving reference frame

Cited by 11 publications

References 20 publications

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

Optimal Aerial Guidance in General Wind Fields

A trajectory‐based sampling strategy for sequentially refined metamodel management of metamodel‐based dynamic optimization in mechatronics

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