A Hierarchical Framework for Long-term and Robust Deployment of Field Ground Robots in Large-Scale Farming

Eiffert, Stuart; Wallace, Nathan; Kong, He; Pirmarzdashti, Navid; Sukkarieh, Salah

doi:10.1109/case48305.2020.9216764

Cited by 8 publications

(22 citation statements)

References 25 publications

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“…During this interaction the robot turned on the spot without realising that a person was present in its 'blind spot'. This occurred both as a result of limited sensor field of view and inconsistent implementations of motor controller between prior simulated work in [4] and this experiment.…”

Section: Dynamic Collision Avoidancementioning

confidence: 93%

“…The hierarchical framework is composed of a few key components: an object detection and tracking module, a long-term planner, a local dynamic planner, a high level switching controller, and a path tracker. The multimodal perception solution used in this work utilises both LIDAR and RGB for object detection, as per our simulated work in [4]. However, due to limited sensor field of view, as shown in Fig.…”

Section: Technical Approachmentioning

confidence: 99%

“…In our previous work [4], we developed a hierarchical framework for off-road, multi-objective robot navigation in the presence of both moving individuals and static obstacles. The performance of this framework was validated using simulated trials, in which moving agents were modelled using the ORCA reciprocal collision avoidance model of motion [5].…”

Section: Technical Approachmentioning

confidence: 99%

“…Planning in the presence of moving individuals, however, requires an understanding of how they might respond to the planned motion of the robot, in order to avoid the 'frozen-robot problem' [3] which would otherwise occur in the presence of crowds and herds. To address the above challenges, in our prior work [4], we have proposed a hierarchical framework for long-term and robust deployment of field ground robots in large-scale farming. In this paper, we present a real world evaluation of the former framework which integrates a local, interaction-aware dynamic path planner with a longer term resource aware mission planner to achieve long-term autonomy in unstructured and dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Evaluation of a Hierarchical Operating Framework for Ground Robots in Agriculture

Eiffert

Wallace

Kong

et al. 2021

Experimental Robotics

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For mobile robots to be effectively applied to real world unstructured environments-such as large scale farming-they require the ability to generate adaptive plans that account both for limited onboard resources, and the presence of dynamic changes, including nearby moving individuals. This work provides a real world empirical evaluation of our proposed hierarchical framework for long-term autonomy of field robots, conducted on University of Sydney's Swagbot agricultural robot platform. We demonstrate the ability of the framework to navigate an unstructured and dynamic environment in an effective manner, validating its use for long-term deployment in large scale farming, for tasks such as autonomous weeding in the presence of moving individuals.

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Section: Dynamic Collision Avoidancementioning

confidence: 93%

Section: Technical Approachmentioning

confidence: 99%

Section: Technical Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Experimental Evaluation of a Hierarchical Operating Framework for Ground Robots in Agriculture

Eiffert

Wallace

Kong

et al. 2021

Experimental Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to its vast applications such as environmental monitoring [1], target/source motion tracking/localization [2]- [3], agriculture [4]- [5], sensor management has been studied extensively in robotics and automation literature, in terms of communication management [6]- [7] and sensor trajectory planning [8]- [14], etc. Closely related problems of sensor scheduling and sensor placement have also received much attention in the control community [15]- [19].…”

Section: Introductionmentioning

confidence: 99%

Active Information Acquisition under Arbitrary Unknown Disturbances

Jennifer¹,

Kong²,

Sukkarieh³

2021

Preprint

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Trajectory optimization of sensing robots to actively gather information of targets has received much attention in the past. It is well-known that under the assumption of linear Gaussian target dynamics and sensor models the stochastic Active Information Acquisition problem is equivalent to a deterministic optimal control problem. However, the abovementioned assumptions regarding the target dynamic model are limiting. In real-world scenarios, the target may be subject to disturbances whose models or statistical properties are hard or impossible to obtain. Typical scenarios include abrupt maneuvers, jumping disturbances due to interactions with the environment, anomalous misbehaviors due to system faults/attacks, etc. Motivated by the above considerations, in this paper we consider targets whose dynamic models are subject to arbitrary unknown inputs whose models or statistical properties are not assumed to be available. In particular, with the aid of an unknown input decoupled filter, we formulate the sensor trajectory planning problem to track evolution of the target state and analyse the resulting performance for both the state and unknown input evolution tracking. Inspired by concepts of Reduced Value Iteration, a suboptimal solution that expands a search tree via Forward Value Iteration with informativeness-based pruning is proposed. Concrete suboptimality performance guarantees for tracking both the state and the unknown input are established. Numerical simulations of a target tracking example are presented to compare the proposed solution with a greedy policy.

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Robots may transform the way we produce and prepare food

Lipson,

Sukkarieh

2023

Nat Rev Bioeng

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A Hierarchical Framework for Long-term and Robust Deployment of Field Ground Robots in Large-Scale Farming

Cited by 8 publications

References 25 publications

Experimental Evaluation of a Hierarchical Operating Framework for Ground Robots in Agriculture

Experimental Evaluation of a Hierarchical Operating Framework for Ground Robots in Agriculture

Active Information Acquisition under Arbitrary Unknown Disturbances

Robots may transform the way we produce and prepare food

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