Robotics for Sustainable Broad-Acre Agriculture

Ball, David; Ross, Patrick; English, Andrew; Patten, Timothy; Upcroft, Ben; Sukkarieh, Salah; Wyeth, Gordon; Corke, Peter

doi:10.1007/978-3-319-07488-7_30

Cited by 39 publications

(29 citation statements)

References 17 publications

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“…A number of works have demonstrated that novelty‐based obstacle detection is suitable for many field environments . This is because the significant assumption of novelty‐based obstacle detectors is that the environmental appearance is uniform, and that obstacles are infrequent.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, appearance‐based methods look for appearance differences to define an obstacle. A number of works have demonstrated that this concept holds for many field environments . Secondly, environmental observations are plentiful during operation.…”

Section: Novelty‐based Obstacle Detectionmentioning

confidence: 99%

“…A promising solution is novelty‐based obstacle detection . These methods build a model of the environmental appearance, and define an obstacle as any observation that is novel with respect to these observations.…”

Section: Introductionmentioning

confidence: 99%

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Online covariance estimation for novelty‐based visual obstacle detection

Ross

English

Ball

2017

Journal of Field Robotics

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Robust obstacle detection remains a challenge for mobile robots traversing outdoor field environments. Obstacle detection systems that combine multiple cues can potentially overcome deficiencies in individual cues. A key challenge in designing multi‐sensor obstacle detection systems is to automatically and appropriately combine these cues in an unsupervised manner. This paper presents a method for obstacle detection, which continuously adapts its obstacle definition and the weighting of each cue for the current conditions. The key contribution of this paper is a method for online covariance estimation for Parzen windows probability density estimation, which in this application determines the relative importance of each descriptor dimension. By iteratively estimating the covariance using small subsets of the available data, the proposed method is capable of converging to an approximate solution order of magnitude faster than standard optimizers, making it suitable for use online. The application of this covariance estimation method to our novelty‐based obstacle detection system improves obstacle detection precision and reduces learning duration after environmental transitions. It also removes all environment‐specific parameters from the method, and allows the descriptor to contain arbitrary data without time‐consuming hand‐tuning.

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Section: Discussionmentioning

confidence: 99%

Section: Novelty‐based Obstacle Detectionmentioning

confidence: 99%

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Online covariance estimation for novelty‐based visual obstacle detection

Ross

English

Ball

2017

Journal of Field Robotics

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“…. However, this case will be realised by a path ( v i , v a , v a , v j ) where ω i,a and ω a,j comply with (5)…”

Section: ) Infeasible -An Edge Is Included If and Only If The Vertexmentioning

confidence: 99%

“…There are many examples of such tasks, including undersea surveys [38,21], monitoring our natural environment [18], autonomous farming [5] and planetary exploration [33]. Monitoring allows for rapid response to failures and to important information that the robot may discover during the progress of its mission [22,23,7].…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Optimal Stopping Problem for Mission Monitoring with Stationary Viewpoints

Best

Martens

2015

Robotics: Science and Systems XI

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Abstract-We consider an optimal stopping formulation of the mission monitoring problem, where a monitor vehicle must remain in close proximity to an autonomous robot that stochastically follows a pre-planned trajectory. This problem arises when autonomous underwater vehicles are monitored by surface vessels, and in a diverse range of other scenarios. The key problem characteristics we consider are that the monitor must remain stationary while observing the robot, and that the robot motion is modelled in general as a stochastic process. We propose a resolution-complete algorithm for this problem that runs in polynomial time. The algorithm is based on a sweepplane approach and generates a motion plan that maximises the expected observation time. A variety of stochastic models may be used to represent the expected robot trajectory. We present results drawn from real AUV trajectories and Monte Carlo simulations that validate the correctness of our algorithm and its feasibility in practice.

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Vision‐based Obstacle Detection and Navigation for an Agricultural Robot

et al. 2016

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This paper describes a vision-based obstacle detection and navigation system for use as part of a robotic solution for the sustainable intensification of broad-acre agriculture. To be cost-effective, the robotics solution must be competitive with current human-driven farm machinery. Significant costs are in high-end localization and obstacle detection sensors. Our system demonstrates a combination of an inexpensive global positioning system and inertial navigation system with vision for localization and a single stereo vision system for obstacle detection. The paper describes the design of the robot, including detailed descriptions of three key parts of the system: novelty-based obstacle detection, visually-aided guidance, and a navigation system that generates collision-free kinematically feasible paths. The robot has seen extensive testing over numerous weeks of field trials during the day and night. The results in this paper pertain to one particular 3 h nighttime experiment in which the robot performed a coverage task and avoided obstacles. Additional results during the day demonstrate that the robot is able to continue operating during 5 min GPS outages by visually following crop rows. C 2016Wiley Periodicals, Inc.This section describes the field environment, the robot (including the physical and software robot platform), and the algorithmic details of our navigation system.

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Robotics for Sustainable Broad-Acre Agriculture

Cited by 39 publications

References 17 publications

Online covariance estimation for novelty‐based visual obstacle detection

Online covariance estimation for novelty‐based visual obstacle detection

A Spatiotemporal Optimal Stopping Problem for Mission Monitoring with Stationary Viewpoints

Vision‐based Obstacle Detection and Navigation for an Agricultural Robot

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