Wind-energy based path planning for Unmanned Aerial Vehicles using Markov Decision Processes

Al-Sabban, Wesam H.; Gonzalez, Felipé; Smith, Ryan N.

doi:10.1109/icra.2013.6630662

Cited by 94 publications

(54 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, rapid changes in orientation can cause unstable behaviours. A recent study by Al-Sabban et al suggests that these changes, as well as incorporating a six-degree of freedom model affect path planning performance [8]. The model developed in this paper includes several significant differential constraints inherent to airships and other non-holonomic UAVs.…”

Section: Vehicle Modelmentioning

confidence: 99%

“…This has been especially important in the path planning of static soaring UAVs that use the vertical component of wind for lift, or dynamic soaring UAVs that exploit the vertical gradients in the horizontal wind [9][10][11][12][13]. Other sources of wind energy that can be exploited are horizontal shear layers [8]. For example, unperturbed wind velocity creates a boundary layer with the Earth's surface as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…If the strength of the local wind is larger than the maximum vehicle velocity, impassible zones are created which many planners do not account for [6]. Moreover, while many planners consider wind as a disturbance whose influence is suppressed by the vehicle's autopilot [7], the wind vector field has the potential to be used as a source for powering the UAV to further reduce energy consumption and travel time [8]. This has been especially important in the path planning of static soaring UAVs that use the vertical component of wind for lift, or dynamic soaring UAVs that exploit the vertical gradients in the horizontal wind [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…More recently, biologically-inspired tau-based path planning strategies such as the decentralized receding horizon optimization in [20] were developed to include velocities and time dependencies in the trajectory generation. Many path planners including evolutionary algorithms are based on uniform WVFs [21][22][23][24][25][26] or constant vehicle velocities [6,8,15,[27][28][29][30]. The few planners that account for variable wind conditions simplify the environment to two dimensions by assuming a fixed cruise altitude [6,27,28] or assume that the WVF is horizontally planar [5,31,32] with coarse resolution discretization of the flight space (≥1 km).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A High-Fidelity Energy Efficient Path Planner for Unmanned Airships

2017

View full text Add to dashboard Cite

This paper presents a comparative study of the effects of grid resolution, vehicle velocity and wind vector fields on the trajectory planning of unmanned airships. A wavefront expansion trajectory planner that minimizes a multi-objective cost function consisting of flight time, energy consumption and collision avoidance while respecting the differential constraints of the vehicle is presented. Trajectories are generated using a variety of test environments and flight conditions to demonstrate that the inclusion of a high terrain map resolution, a temporal vehicle velocity and a spatial wind vector field yields significant improvements in trajectory feasibility and energy economy when compared to trajectories generated using only two of these three elements.

show abstract

Section: Vehicle Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A High-Fidelity Energy Efficient Path Planner for Unmanned Airships

2017

View full text Add to dashboard Cite

show abstract

“…In [2], the author integrates the uncertainty of the wind field into the wind model, and uses a Markov Decision Process for path planning. The goal was to minimize the power consumption of the aircraft and minimize time-to-goal.…”

Section: Introductionmentioning

confidence: 99%

Contingency path planning for hybrid-electric UAS

Hovenburg

Andrade

Rodin

et al. 2017

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)

View full text Add to dashboard Cite

Abstract-This article presents a path planning optimization method which aims to mitigate the risks in the event of a critical engine or generator failure in hybrid-electric UAS. This is achieved through continuous determination of the optimum flight path, based on the remaining battery range and expected local wind conditions. The result is a dynamically adjusting flight path which ensures the aircraft to remain within range of pre-specified safe landing spots. The developed algorithm uses the particle swarm optimization technique to optimize the flight path, and incorporates regional wind information in order to increase the accuracy of the expected in-flight performance of the aircraft.

show abstract

External Force Field Modeling for Autonomous Surface Vehicles

Moulton

Karapetyan

et al. 2020

Springer Proceedings in Advanced Robotics

View full text Add to dashboard Cite

Operating in the presence of strong adverse forces is a particularly challenging problem in field robotics. In most robotic operations where the robot is not firmly grounded, such as aerial, surface, and underwater, minimal external forces are assumed as the standard operating procedures. The first action for operating in the presence of non-trivial forces is modeling the forces and their effect on the robots motion. In this work an Autonomous Surface Vehicle (ASV), operating on lakes and rivers with varying winds and currents, collects wind and current measurements with an inexpensive custom-made sensor suite setup, and generates a model of the force field. The modeling process takes into account depth, wind, and current measurements along with the ASVs trajectory from GPS. In this work, we propose a method for an ASV to build an environmental force map by integrating in a Gaussian Process the wind, depth, and current measurements gathered at the surface. We run extensive experimental field trials for our approach on real Jetyak ASVs. Experimental results from different locations validate the proposed modeling approach.

show abstract

Wind-energy based path planning for Unmanned Aerial Vehicles using Markov Decision Processes

Cited by 94 publications

References 14 publications

A High-Fidelity Energy Efficient Path Planner for Unmanned Airships

A High-Fidelity Energy Efficient Path Planner for Unmanned Airships

Contingency path planning for hybrid-electric UAS

External Force Field Modeling for Autonomous Surface Vehicles

Contact Info

Product

Resources

About