Mobile Sensor Path Planning for Iceberg Monitoring using a MILP Framework

Albert, Anders; Imsland, Lars

doi:10.5220/0005521801310138

Cited by 3 publications

(6 citation statements)

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“…The inner workings of this module may vary depending on the nature of the ice features that are being tracked, but the resulting behaviour is based on the in-flight generated occupancy grid map. Often discrete optimization such as graph search (Travelling Salesman Problem [15]) and MILP [16] can be used as an alternative to find an optimal prioritization of the visiting sequence of locations of interest by some predefined criteria (e.g shortest total flight distance). However, a simple approach where the UAV is made to investigate the location of interest closest to the current position of the UAV can also be effective in many applications.…”

Section: Mpcmentioning

confidence: 99%

“…[9] and [10] both presents path planning algorithms for UAVs in ice management based on optimization techniques. [9] develops an algorithm to find a path pattern that covers a predefined region of interest.…”

Section: Introductionmentioning

confidence: 99%

“…The region is divided into a grid of cells and the UAV's visitation sequence for the grid cells is found by solving a Mixed Integer Linear Programming (MILP) problem seeking to minimize the time required to cover the total grid. [10] assumes that a set of n icebergs have been detected and located, and that the UAV is given an estimate of each of the iceberg's positions. It is further assumed that the uncertainty of the position estimate for the location of each iceberg is reset to 0 when the UAV is in its vicinity and able to observe its position.…”

Section: Introductionmentioning

confidence: 99%

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A UAV ice tracking framework for autonomous sea ice management

Leira

Johansen

Fossen

2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-This paper describes an unmanned aerial vehicle (UAV) ice tracking framework for use in sea ice management applications. The framework is intended to be used in an ice management scenario where the UAV should detect and track the movement of icebergs and ice floes in an Arctic environment, and seeks to enable the UAV to do so autonomously. This is achieved by using an occupancy grid map algorithm and a locations of interest generator coupled with a Model Predictive Control (MPC) UAV path planner. The main contribution of this paper is interfacing the occupancy grid map algorithm with a machine vision object detection module in order to enable the UAV to generate an occupancy grid map of a pre-defined search area in real-time using on-board processing of UAV sensor data. Further, the paper presents a locations of interest generator module which generates locations that the UAV should investigate based on the generated occupancy grid map. These locations of interest are then used by an MPC path planner in order to make the UAV autonomously investigate and track ice features at said locations. Furthermore, the paper verifies the use of the developed ice tracking framework for autonomously detecting and tracking ice features based on thermal images captured with a UAV, as well as verifying the usefulness and role of UAVs in ice management scenarios by conducting two flight experiments.

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Section: Mpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A UAV ice tracking framework for autonomous sea ice management

Leira

Johansen

Fossen

2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…In addition to assuming the number of objects known, we assume the object's positions are uniformly distributed and the UAV is not subjected to the nonholonomic constraints of equation (2).…”

Section: Area Size Estimatesmentioning

confidence: 99%

“…Examples of applications for searching and tracking of objects are: iceberg monitoring, fire detection and monitoring, border patrol, surveillance and reconnaissance, [2,3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Performance bounds for tracking multiple objects using a single UAV

Albert

Imsland

2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-In this paper we calculate probabilistic estimates for the size of an area a single unmanned aerial vehicle (UAV) can expect to monitor when tracking multiple objects. The objects are assumed to move according to a linear velocity model with Gaussian process noise. We use a Kalman filter to estimate the position of the objects. By using the covariance matrix of the Kalman filter, we can derive the necessary visitation period for a UAV to have a probability within a given confidence interval of redetecting the object at the estimated position. Then, we use this visitation period to calculate the probabilistic estimate for the area a single UAV can monitor. We demonstrate the results in Monte Carlo simulations.

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“Make no little plans”: Impactful research to solve the next generation of transportation problems

2020

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The transportation science research community has contributed to numerous practical and intellectual innovations and improvements over the last decades. Technological advancements have broadened and amplified the potential impacts of our field. At the same time, the world and its communities are facing greater and more serious challenges than ever before. In this paper, we call upon the transportation science research community to work on a research agenda that addresses some of the most important of these challenges. This agenda is guided by the sustainable development goals outlined by the United Nations and organized into three areas: (1) well‐being, (2) infrastructure, and, (3) natural environment. For each area, we identify current and future challenges as well as research directions to address those challenges.

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Mobile Sensor Path Planning for Iceberg Monitoring using a MILP Framework

Cited by 3 publications

References 0 publications

A UAV ice tracking framework for autonomous sea ice management

A UAV ice tracking framework for autonomous sea ice management

Performance bounds for tracking multiple objects using a single UAV

“Make no little plans”: Impactful research to solve the next generation of transportation problems

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