Autonomous Guidance, Navigation and Control of Large Parafoils

Carter, David; George, Sean; Hattis, Phillip; Singh, Leena; Tavan, Steven

doi:10.2514/6.2005-1643

Cited by 32 publications

(28 citation statements)

References 6 publications

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“…A rst-order lag is used to model the dierential toggle control input mechanism with τ = 5 s [18], while the controller is a PID with feedforward gains tuned to achieve comparable performance [17]. In total, this yields a 5th order state s and dynamics (A, B, C, D), augmented to the state vector x and dynamics (1), respectively.…”

Section: A Parafoil Modelmentioning

confidence: 99%

“…where the saturation function sat(a, b, c) bounds a between b and c, and ω max = π/15 = 0.2094 rad/s [18] (such that the vehicle's minimum turning radius R min equals v 0 /ω max = 85 m). In this formulation, only the position states p are aected by the wind disturbance uncertainty w, including possible eects on altitude p z by updrafts and downdrafts via w z .…”

Section: A Parafoil Modelmentioning

confidence: 99%

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Wind Uncertainty Modeling and Robust Trajectory Planning for Autonomous Parafoils

Luders

Ellertson

How

et al. 2016

Journal of Guidance, Control, and Dynamics

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A key challenge facing modern airborne delivery systems, such as parafoils, is the ability to accurately and consistently deliver supplies into dicult, complex terrain. Robustness is a primary concern, given that environmental wind disturbances are often highly uncertain and time-varying. This paper presents a new on-line trajectory planning algorithm that enables a large, autonomous parafoil with under-actuated dynamics to robustly execute collision avoidance and precision landing on mapped terrain, even with signicant wind uncertainties. This algorithm is designed to handle arbitrary initial altitudes, approach geometries, and terrain surfaces, and is robust to wind disturbances that may be highly dynamic throughout terminal approach. Real-time wind modeling and classication is used to anticipate future disturbances, while a novel uncertainty-sampling technique ensures that robustness to future variation is eciently maintained. The designed cost-to-go function enables selection of partial paths which intelligently trade o between current and reachable future states, while encouraging upwind landings. Simulation results demonstrate that this algorithm reduces the worst-case impact of wind disturbances relative to state-of-the-art approaches.

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Section: A Parafoil Modelmentioning

confidence: 99%

Section: A Parafoil Modelmentioning

confidence: 99%

Wind Uncertainty Modeling and Robust Trajectory Planning for Autonomous Parafoils

Luders

Ellertson

How

et al. 2016

Journal of Guidance, Control, and Dynamics

View full text Add to dashboard Cite

show abstract

“…Analysis is conducted through several Monte Carlo simulation experiments using realistic valley terrain, and Draper Laboratory's high fidelity nonlinear parafoil simulator [48]. Specific parameter settings for the CC-BLG objective function and reachability set approximation are evaluated, leading to the selection of a final configuration.…”

Section: * Chaptermentioning

confidence: 99%

Analytic Chance Constraints for the Robust Guidance of Autonomous Parafoils

Ellertson

2016

AIAA Infotech @ Aerospace

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Autonomously guided parafoil systems can deliver supplies and aid to remote, geographically diverse locations, while providing important safety and logistical advantages over ground-based transportation methods. A key challenge facing modern airborne delivery systems, such as parafoils, is the ability to accurately and consistently deliver supplies into difficult, complex terrain. Parafoil guidance algorithms must be able to generate feasible trajectory solutions to the target location within highly constrained terrain environments and from a wide range of initial conditions. Robustness is critical for successful payload delivery in the presence of uncertain atmospheric wind disturbances.This thesis presents two online trajectory planning algorithms for autonomous parafoil guidance in complex terrain and wind environments. These -algorithms are capable of operating from arbitrary initial conditions, including altitude, and are robust to wind disturbances that may be highly dynamic throughout terminal descent. The first algorithm, known as Analytic CC-RRT, builds upon the framework of chance-constrained rapidly-exploring random trees (CC-RRT). This planner enables fast incremental trajectory construction in cluttered, non-convex environments, while using chance constraints to ensure probabilistic feasibility. The designed costto-go function prioritizes target accuracy and upwind landings through the selection of partial paths that intelligently consider current and reachable future states. A trained multi-class wind uncertainty model is introduced to classify and anticipate the effect of future wind disturbances online. Utilizing this model, robustness to wind variations is achieved via a novel analytic uncertainty sampling technique, allowing the probability of constraint violation to be efficiently evaluated against arbitrary and aggressive terrain.The second algorithm, known as CC-BLG, incorporates the Analytic CC-RRT proactive wind model and uncertainty sampling technique into the optimized BandLimited Guidance (BLG) framework. Through the design of a novel risk-based objective function, CC-BLG trajectories efficiently balance the parafoil performance metrics of landing accuracy and landing speed with the risk of off-nominal terrain 3 collisions caused by future wind disturbances. Proposed extensions to the analytic uncertainty sampling technique are shown to yield enhanced planning robustness by refining the estimation of trajectory risk. Multi-phase CC-BLG path planning enables initialization of parafoil terminal guidance from potentially high altitudes, while discrete reachability set approximation is used to maintain robust obstacle avoidance over disjoint planning horizons.Extensive Monte Carlo simulation analysis demonstrates that the Analytic CC-RRT and CC-BLG algorithms achieve significant improvements in mean and worstcase landing accuracy within complex terrain scenarios relative to the state-of-the-art Band-Limited Guidance (BLG) algorithm. Flight test experiments conducted with a full-scal...

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“…In contrast to these specialized platforms, fielded airdrop systems have only a single GPS receiver [12][13][14]. This is desirable to reduce cost and complexity, especially because the GPS receiver does not require any preflight calibration.…”

mentioning

confidence: 99%

“…A number of techniques for deriving particular aspects of the flight dynamic characteristics of a parafoil and payload system using only GPS data have been described [12][13][14]. The restricted amount of sensor information available leads to a very restricted dynamic model of the system, normally a linear 3-DOF representation.…”

mentioning

confidence: 99%

Specialized System Identification for Parafoil and Payload Systems

Ward

Costello

Slegers

2012

Journal of Guidance, Control, and Dynamics

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There are a number of peculiar aspects to parafoil and payload systems that make it difficult to apply conventional system identification procedures used for aerospace systems. Parafoil and payload systems are unique because typically there is very little sensor information available, the sensors that are available are separated from the canopy by a complex network of flexible rigging, the systems are very sensitive to wind and turbulence, the systems exhibit a number of nonlinear behaviors, and the systems exhibit a high degree of variability from flight to flight. The current work describes a robust system identification procedure developed to address the specific difficulties posed by airdrop systems. By employing a two-phase approach that separately considers atmospheric winds estimation and aerodynamic coefficient estimation, a nonlinear, 6-degree-of-freedom dynamic simulation model is generated using only Global Positioning System data from the flight test. The key to this approach is the use of a simplified aerodynamic representation of the canopy, which requires identification of only the steady-state response to control input to completely define the dynamic model. The proposed procedure is demonstrated by creating a simulation model using Global Positioning System data from actual flight tests. To validate the procedure, the dynamic response of the simulation model is then compared to inertial measurement unit data that were not used in any way to develop the simulation model, with excellent results.

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Autonomous Guidance, Navigation and Control of Large Parafoils

Cited by 32 publications

References 6 publications

Wind Uncertainty Modeling and Robust Trajectory Planning for Autonomous Parafoils

Wind Uncertainty Modeling and Robust Trajectory Planning for Autonomous Parafoils

Analytic Chance Constraints for the Robust Guidance of Autonomous Parafoils

Specialized System Identification for Parafoil and Payload Systems

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