Path planning of a Dubins vehicle for sequential target observation with ranged sensors

Hanson, Clarence B.; Richardson, Jeremy O.; Girard, Anouck

doi:10.1109/acc.2011.5990964

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…Navigable trajectories are often computed from the high level paths using Dubins curves [19]. For example, the algorithm in [20] investigates using Dubins curves to design paths to view partially occluded targets and solving the travelling salesman problem with a Dubins vehicle is rigorously treated in [21]. However, decoupling waypoint planning and trajectory generation can be problematic from a sensor coverage standpoint.…”

Section: B Uav Path Planningmentioning

confidence: 99%

See 1 more Smart Citation

Sensor-Driven Area Coverage for an Autonomous Fixed-Wing Unmanned Aerial Vehicle

Paull

Thibault

Nagaty

et al. 2014

IEEE Trans. Cybern.

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Area coverage with an onboard sensor is an important task for an unmanned aerial vehicle (UAV) with many applications. Autonomous fixed-wing UAVs are more appropriate for larger scale area surveying since they can cover ground more quickly. However, their non-holonomic dynamics and susceptibility to disturbances make sensor coverage a challenging task. Most previous approaches to area coverage planning are offline and assume that the UAV can follow the planned trajectory exactly. In this paper, this restriction is removed as the aircraft maintains a coverage map based on its actual pose trajectory and makes control decisions based on that map. The aircraft is able to plan paths in situ based on sensor data and an accurate model of the on-board camera used for coverage. An information theoretic approach is used that selects desired headings that maximize the expected information gain over the coverage map. In addition, the branch entropy concept previously developed for autonomous underwater vehicles is extended to UAVs and ensures that the vehicle is able to achieve its global coverage mission. The coverage map over the workspace uses the projective camera model and compares the expected area of the target on the ground and the actual area covered on the ground by each pixel in the image. The camera is mounted on a two-axis gimbal and can either be stabilized or optimized for maximal coverage. Hardware-in-the-loop simulation results and real hardware implementation on a fixed-wing UAV show the effectiveness of the approach. By including the already developed automatic takeoff and landing capabilities, we now have a fully automated and robust platform for performing aerial imagery surveys.

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Section: B Uav Path Planningmentioning

confidence: 99%

“…Assume that following the track will result in a series of K pictures Z = {Z 1 , Z 2 , ..., Z K }. Then, (20) can be used to compute the expected entropy at location c i in the workspace as a result of any individual measurement Z k , k = 1..K…”

Section: B Information Gain Objective Functionmentioning

confidence: 99%

Sensor-Driven Area Coverage for an Autonomous Fixed-Wing Unmanned Aerial Vehicle

Paull

Thibault

Nagaty

et al. 2014

IEEE Trans. Cybern.

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show abstract

“…Taking an investigation on current path planning algorithm in non-holonomic and car-like robot [13,[15][16][17], multiple UAVs [18][19][20] and Dubins vehicles [21,22], it can be seen that all of them are designed to plan the path by the current states and waypoints information, rather than by storing all the planned path on on-board computer. The main advantages are that, on one hand, it reduces the storage requirement of the on-board computer; on the other hand, it can adjust route in real-time when the waypoints are changed.…”

Section: The Structure Of Algorithmmentioning

confidence: 99%

The Shortest Path Planning for Manoeuvres of UAV

Gao

Hou

Zhu

et al. 2013

APH

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It is important to find the shortest path for manoeuvres of UAV, since the power consumed during manoeuvres is tightly coupled with the length of the flight path. In this paper, an algorithm that can find the shortest path during manoeuvres and improve the performance of UAV to follow waypoints is described. The shortest path for UAV during manoeuvres is derived firstly by the theory of Dubins curve. Secondly, in order to improve the ability of UAV to follow the derived optimal path, a real-time path planning algorithm is designed by transforming the constraints of Dubins curve into a dynamic equation. To demonstrate the applicability and performance of the proposed path planning algorithm, two numerical examples are presented. The results show that the proposed algorithm is promising to be applied in the path planning for manoeuvres of UAV.

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“…A vehicle that follows Dubins paths is often termed a Dubins car. There have been a wide variety of path-planning techniques proposed for mobile robots based on the Dubins car model (e.g., Hanson et al 2011;Balluchi et al 1996;Anderson and Milutinovic 2011;Karaman and Frazzoli 2011;Cowlagi and Tsiotras 2009;Yong and Barth 2006). The Dubins car model has also been used extensively for UAV applications by constraining the air vehicle to fly at a constant altitude (e.g., Yu and Beard 2013;Sujit et al 2007;Yang and Kapila 2002;Shima et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Implementing Dubins Airplane Paths on Fixed-Wing UAVs*

McLain

Beard

Owen

2014

Handbook of Unmanned Aerial Vehicles

116

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A well-known path-planning technique for mobile robots or planar aerial vehicles is to use Dubins paths, which are minimum-distance paths between two configurations subject to the constraints of the Dubins car model. An extension of this method to a three-dimensional Dubins airplane model has recently been proposed. This chapter builds on that work showing a complete architecture for implementing Dubins airplane paths on small fixed-wing UAVs. The existing Dubins airplane model is modified to be more consistent with the kinematics of a fixed-wing aircraft. The chapter then shows how a recently proposed vector-field method can be used to design a guidance law that causes the Dubins airplane model to follow straight-line and helical paths. Dubins airplane paths are more complicated than Dubins car paths because of the altitude component. Based on the difference between the altitude of the start and end configurations, Dubins airplane paths can be classified as low, medium, or high altitude gain. While for medium and high altitude gain there are many different Dubins airplane paths, this chapter proposes selecting the path that maximizes the average altitude throughout the maneuver. The proposed architecture is implemented on a six degree-of-freedom Matlab/Simulink simulation of an Aerosonde UAV, and results from this simulation demonstrate the effectiveness of the technique.

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Path planning of a Dubins vehicle for sequential target observation with ranged sensors

Cited by 12 publications

References 7 publications

Sensor-Driven Area Coverage for an Autonomous Fixed-Wing Unmanned Aerial Vehicle

Sensor-Driven Area Coverage for an Autonomous Fixed-Wing Unmanned Aerial Vehicle

The Shortest Path Planning for Manoeuvres of UAV

Implementing Dubins Airplane Paths on Fixed-Wing UAVs*

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