A Minimum Risk Approach for Path Planning of UAVs

Filippis, Luca De; Guglieri, Giorgio; Quagliotti, Fulvia

doi:10.1007/978-94-007-1110-5_14

Cited by 20 publications

(24 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…h(w k ) = N * risk min (4) and N is the number of waypoints to the goal from waypoint w k assuming the glider uses the minimum number of surfacings along the straight line path between w k and the goal waypoint, such that it satisfies the constraint from Equation 2. Equation 4 is an admissible heuristic because it never overestimates the cost to reach the goal.…”

Section: Proposed Solutionmentioning

confidence: 99%

See 1 more Smart Citation

Toward risk aware mission planning for Autonomous Underwater Vehicles

Pereira

Binney

Jones

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Long range and high endurance Autonomous Underwater Vehicles such as gliders enable sustained oceanographic sampling at larger time-scales and much lower operational costs compared to traditional ship-based sampling methods. While most path-planning methods for AUVs optimize paths with respect to efficiency, obstacle avoidance, and control they do not explicitly address the issue of finding the safest possible path when considering risks such as shipping traffic and bathymetry. In coastal regions with high shipping traffic, reducing collision risk at the path planning stage, at the expense of efficiency, is a worthwhile trade-off. We propose a method of building risk maps using historical data from the Automated Information System. These are used to plan minimum risk paths between a specified start and goal location, while avoiding obstacles, using an algorithm based on A * search. Our planner incorporates the uncertainty in dead-reckoning without explicitly considering the effect of ocean currents. We compare the relative risk of paths produced by our method when compared to a shortest-path planner which does not take risk into account, and show that our methods performs significantly better, while producing competitive paths lengths.

show abstract

Section: Proposed Solutionmentioning

confidence: 99%

“…In [4], Filippis et al, discuss the construction of risk maps based upon ground orography, which is then used to plan minimum-risk paths for UAVs using A * and a genetic algorithm. They also perform smoothing on their paths so they can be used for trajectory control on an UAV.…”

Section: B Related Workmentioning

confidence: 99%

Toward risk aware mission planning for Autonomous Underwater Vehicles

Pereira

Binney

Jones

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

show abstract

“…[8] proposed risk as an accumulative parameterized function based on distance to threats. A similar approach was taken by [9], where a risk map was generated based on ground orography and the risk of each location could be looked up from the map. Those works based risk only on the distance to some ad hoc hazards.…”

Section: Related Workmentioning

confidence: 99%

Explicit Motion Risk Representation

Xiao

Dufek

Murphy

2019

2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

View full text Add to dashboard Cite

This paper presents a formal definition and explicit representation of robot motion risk. Currently, robot motion risk has not been formally defined, but has already been used in motion and path planning. Risk is either implicitly represented as model uncertainty using probabilistic approaches, where the definition of risk is somewhat avoided, or explicitly modeled as a simple function of states, without a formal definition. In this work, we provide formal reasoning behind what risk is for robot motion and propose a formal definition of risk in terms of a sequence of motion, namely path. Mathematical approaches to represent motion risk are also presented, which is in accordance with our risk definition and properties. The definition and representation of risk provide a meaningful way to evaluate or construct robot motion or path plans. The understanding of risk is even of greater interest for the search and rescue community: the deconstructed environments cast extra risk onto the robot, since they are working under extreme conditions. A proper risk representation has the potential to reduce robot failure in the field.

show abstract

“…This work focusses on risk avoidance, a well‐studied problem in robotics, spanning from unmanned ground vehicles (Soltani, Tawfik, Goulermas, & Fernando, ) to unmanned aerial vehicles (De Filippis, Guglieri, & Quagliotti, ). Most of this work uses deterministic strategies for risk minimization using heuristic search techniques.…”

Section: Related Workmentioning

confidence: 99%

Learning Uncertainty in Ocean Current Predictions for Safe and Reliable Navigation of Underwater Vehicles

Hollinger

Pereira²,

Binney

et al. 2015

J. Field Robotics

View full text Add to dashboard Cite

Operating autonomous underwater vehicles (AUVs) near shore is challenging—heavy shipping traffic and other hazards threaten AUV safety at the surface, and strong ocean currents impede navigation when underwater. Predictive models of ocean currents have been shown to improve navigation accuracy, but these forecasts are typically noisy, making it challenging to use them effectively. Prior work has explored the use of probabilistic planners, such as Markov decision processes (MDPs), for planning in these scenarios, but prior methods have lacked a principled way of modeling the uncertainty in ocean model predictions, which limits applicability to cases in which high fidelity models are available. To overcome this limitation, we propose using Gaussian processes (GPs) augmented with interpolation variance to provide confidence measures on predictions. This paper describes two novel planners that incorporate these confidence measures: (1) a stationary risk‐aware GPMDP (for low‐variability currents), and (2) a nonstationary risk‐aware NS‐GPMDP (for faster and high‐variability currents). Extensive simulations indicate that the learned confidence measures allow for safe and reliable operation with uncertain ocean current models. Field tests of the planners on Slocum gliders over several weeks in the ocean demonstrate the practical efficacy of our approach.

show abstract

A Minimum Risk Approach for Path Planning of UAVs

Cited by 20 publications

References 11 publications

Toward risk aware mission planning for Autonomous Underwater Vehicles

Toward risk aware mission planning for Autonomous Underwater Vehicles

Explicit Motion Risk Representation

Learning Uncertainty in Ocean Current Predictions for Safe and Reliable Navigation of Underwater Vehicles

Contact Info

Product

Resources

About