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

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

doi:10.1007/978-3-030-71151-1_14

Cited by 5 publications

(5 citation statements)

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“…Simulated trials, building upon the analysis presented in (Eiffert et al, 2020b), have been used to allow comparison of varied dynamic planners within the hierarchical framework during extended navigation. Subsequent real-world trials, building upon our prior trial in (Eiffert et al, 2020c), have been used to validate performance on physical hardware deployed in a real operating environment.…”

Section: Methodsmentioning

confidence: 99%

“…Testing was performed at the University's Arthursleigh Farm and involved two separate trials. The first trial, detailed in (Eiffert et al, 2020c), involved extended navigation between mission waypoints across an unstructured field 2 ha in size for the purposes of weeding, whilst in the presence of dynamic agents and unknown obstacles. The second trial focused exclusively on the robot's interaction with dynamic agents and was conducted on a smaller scale with denser crowds in order to comprehensively evaluate the behaviour of our proposed framework during crowd and herd interactions.…”

Section: Methodsmentioning

confidence: 99%

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Untitled

2022

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Achieving long-term autonomy for mobile robots operating in real-world, unstructured environments, such as farms, remains a significant challenge. Such tasks are made increasingly complex when undertaken in the presence of moving humans or livestock. These dynamic environments require a robot to be able to adapt its immediate plans, accounting for the state of nearby agents and possible responses they may have to the robot's actions. Additionally, in order to achieve longer-term goals, consideration of the limited on-board resources available to the robot is required, especially for extended missions, such as weeding agricultural fields. To achieve efficient long-term autonomy, it is thus crucial to understand the impact that dynamic updates to an energy-efficient plan might have on resource usage whilst navigating through crowds or herds. To address these challenges, we present a hierarchical planning framework that integrates an online, dynamic path-planner with a longer-term, offline, objective-based planner. This framework acts to achieve long-term autonomy through awareness of both dynamic responses of agents to a robot's motion and the limited resources available. This paper details the hierarchical approach and its integration on a robotic platform, including a comprehensive description of the planning framework and associated perception modules. The approach is evaluated in real-world trials on farms, requiring both consideration of limited battery capacity and the presence of nearby moving agents. These trials additionally demonstrate the ability of the framework to adapt resource use through variation of the dynamic planner, allowing adaptive behaviour in changing environments. A summary video is available at https://youtu.be/DGVTrYwJ304.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Untitled

2022

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“…Simulated trials, building upon the analysis presented in [Eiffert et al, 2020b], have been used to allow comparison of varied local dynamic planners within the hierarchical framework during extended navigation. Subsequent real world trials, building upon our prior trial in [Eiffert et al, 2020c], have been used to validate performance on physical hardware deployed in a real operating environment.…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we propose a hierarchical path planning framework to enable the long-term autonomy of mobile ground robots in unstructured and dynamic environments, subject to resource constraints of energy and time. We build upon the contributions of our prior work [Eiffert et al, 2020b] and [Eiffert et al, 2020c], extending the description of the overall framework as well as detailing the perception modules used for object detection, tracking, and static mapping. This framework uses a resource-aware long-term planner for the formation of strategic-level plans to allow for navigation between goal locations subject to energy constraints.…”

Section: Introductionmentioning

confidence: 99%

Resource and Response Aware Path Planning for Long-term Autonomy of Ground Robots in Agriculture

Eiffert¹,

Wallace²,

Kong³

et al. 2021

Preprint

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Achieving long-term autonomy for mobile robots operating in real-world unstructured environments such as farms remains a significant challenge. This is made increasingly complex in the presence of moving humans or livestock. These environments require a robot to be adaptive in its immediate plans, accounting for the state of nearby individuals and the response that they might have to the robot's actions. Additionally, in order to achieve longer-term goals, consideration of the limited on-board resources available to the robot is required, especially for extended missions such as weeding an agricultural field. To achieve efficient long-term autonomy, it is thus crucial to understand the impact that online dynamic updates to an energy efficient offline plan might have on resource usage whilst navigating through crowds or herds. To address these challenges, a hierarchical planning framework is proposed, integrating an online local dynamic path planner with an offline longer-term objective-based planner. This framework acts to achieve long-term autonomy through awareness of both dynamic responses of individuals to a robot's motion and the limited resources available. This paper details the hierarchical approach and its integration on a robotic platform, including a comprehensive description of the planning framework and associated perception modules. The approach is evaluated in real-world trials on farms, requiring both consideration of limited battery capacity and the presence of nearby moving individuals. These trials additionally demonstrate the ability of the framework to adapt resource use through variation of the local dynamic planner, allowing adaptive behaviour in changing environments. A summary video is available at https://youtu.be/DGVTrYwJ304.

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

Cited by 5 publications

References 9 publications

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Resource and Response Aware Path Planning for Long-term Autonomy of Ground Robots in Agriculture

Active Information Acquisition under Arbitrary Unknown Disturbances

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