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Toward Robot Co-Labourers for Intelligent Farming
... It is important to note that these numbers refer to the intended agricultural site rather than the sites where these projects were conducted. Many technological projects occurred in a lab or experimental farm settings rather than in a representative agricultural site that reflects where the technologies are meant to be used [85,86,87,161]. Many of the papers did not declare an intended site type or location [47, 86,87,91,97]. ...
... Many technological projects occurred in a lab or experimental farm settings rather than in a representative agricultural site that reflects where the technologies are meant to be used [85,86,87,161]. Many of the papers did not declare an intended site type or location [47, 86,87,91,97]. 1 For the five missing papers: three were in forest environments [119,125,28] and two did not specify a kind of farming site [55,117]. Organic farms papers represent 47% of programmatic papers. ...
... Urban gardeners follow a similar trend to small-scale organic farmers: most are farming or gardening on a voluntary basis [82,83,84,85] or in an experimental setting such as an office [62], home [93] or publicly accessible fruit trees [51]. The 10 papers labeled with "actor replacement" [3,10,47,86,87,91,132,147,172], which occurs when the actor is only referred to in the context of labor automation, all address commercial farm settings. These papers are published in two main venue types: applied computing venues dedicated to machine learning or HCI venues focused on development such as ICTD. ...
... HCI projects on agriculture commonly address 4 farm types: subsistence farming, organic farming, urban gardening, and commercial farming [34]. Across these types, HCI projects tend to emphasise the farmer as the primary actor, and focus on bridging knowledge gaps related to weather, crops, and pricing for subsistence farmers through the development of accessible ICTs [22,24,46,78], aligning with organic farmers and urban gardeners' needs and values through creating design principles or tools [6,10,51,65,67,79,80,110], designing prototypes to help farmers offset labour shortages, train workers, and improve crop production [30,54,69,85], and developing more flexible and configurable systems and tools that are more attuned with farmers' technological capacities and lifestyles [63,67,100,111]. ...
This mixed method study situated in Ontario, Canada, investigates
how migrant farmworkers’ experiences with agricultural technologies
(agtech) affect their attitudes, conditions, and expectations of
work, and how workers envision technologies serving as supportive
interventions. Through a survey and interviews, we identify
that surveillance and tracking agtech (chequeadoras) affect workers, imparting negative health and safety consequences. Workers’ interactions with chequeadoras reveal three major impacts: performance expectations engender stress, surveillance causes fears of disciplinary action, and performance tracking heightens competition. These impacts demonstrate how chequeadoras erode workers’ capacity to build sociality and solidarity. In response to these impacts and to support workers’ desired workplace changes, which aim for safer environments with technical skill development opportunities, we examine tactics from HCI, critical design, and migrant justice movements. Our findings lead us to contemplate what qualifies as agtech and how we may support marginalised workers with divergent opinions regarding workplace technologies, and desired collective change.
... Ergonomics plays a relevant role since many agricultural practices consist of mostly unergonomic postures and the carrying of substantial loads for long periods of time. [51] conducted experiments in HRC for agricultural tasks. They investigated a collaborative task which consisted in a human and robot sharing decision making about the readiness of strawberries for harvesting. ...
Technological developments through the fourth industrial revolution (Industry 4.0) jointly with the global COVID-19 pandemic have brought about a change in life and work of almost all existing professionals. Some researchers argue how the fifth industrial revolution will approach the fourth much sooner than all previous industrial revolutions. In fact, several sources start talking about the fifth industrial revolution, although the paradigms that will define its context and areas of development are still unclear. This research study focuses on one of the foundational aspects of the fourth industrial revolution and which already has the basis for being projected into the fifth industrial revolution and thus the possibility to be taken to the next level: Human-Robot Collaboration. A state-of-the-art study and a bibliometric analysis have been carried out in order to identify research trends related to this paradigm. In addition, the authors present a definition of HRC trying to describe its main characteristics and the key aspects that identify it. In the second part of the paper, a summary of the main research trends is presented, reporting some significant case studies.
In the pursuit of optimizing the efficiency, flexibility, and adaptability of agricultural practices, human–robot interaction (HRI) has emerged in agriculture. Enabled by the ongoing advancement in information and communication technologies, this approach aspires to overcome the challenges originating from the inherent complex agricultural environments. Τhis paper systematically reviews the scholarly literature to capture the current progress and trends in this promising field as well as identify future research directions. It can be inferred that there is a growing interest in this field, which relies on combining perspectives from several disciplines to obtain a holistic understanding. The subject of the selected papers is mainly synergistic target detection, while simulation was the main methodology. Furthermore, melons, grapes, and strawberries were the crops with the highest interest for HRI applications. Finally, collaboration and cooperation were the most preferred interaction modes, with various levels of automation being examined. On all occasions, the synergy of humans and robots demonstrated the best results in terms of system performance, physical workload of workers, and time needed to execute the performed tasks. However, despite the associated progress, there is still a long way to go towards establishing viable, functional, and safe human–robot interactive systems.
The marketability and adoption of robotic systems in agriculture is largely limited by economic and technology barriers that prevent highly efficient autonomous operations at a cost that justifies the generally low commodity values. From the technology perspective, autonomous systems exhibit brittleness in uncontrolled, unforeseen, and unlearned situations, prevalent in complex agricultural environments, which leads to inefficient operations and production losses. In this setting, human workers can complement autonomous systems through cooperation to not only minimize the total production cost by reducing the burden on autonomy, but also improve productivity by overcoming the shortfalls of autonomous systems. To this end, the focus of this work is on developing a systematic approach for human-robot collaboration in robotic fruit harvesting. Specifically, the main contribution of this work is in the development of stochastic optimization models for human-robot collaboration using a risk-averse framework to identify optimal harvester servicing policies that minimize the risk of economic losses all while guaranteeing a desired level of financial return. The solution of the models provide optimal policies for a human collaborator to service various sub-systems of a robotic harvester to improve performance in light of any operational inefficiencies in the sub-systems. The developed risk-averse optimization solution is validated in a simulated grove environment using data for various citrus varieties. Economic gains through human collaboration are confirmed through increase in the value of production and the net profit. The simulation results also provide insights into the temporal variation in the required human collaboration during the length of harvesting operation, which can benefit agricultural operations management in effective labor allocation. Furthermore, the intrinsic self-diagnosing ability of the developed model identifies component-level inefficiencies to provide an optimal servicing policy, thereby a human collaborator is not required to maintain situational awareness of the harvesting operation. Finally, it is demonstrated that the models can be solved to optimality in a relatively short time (⩽2s) using standard commercial optimization packages.
There has been much research on the use of auction-based methods to provide a distributed approach to task allocation for robot teams. Team members bid on tasks based on their locations, and the allocation is based on these bids. The focus of prior work has been on the optimality of the allocation, establishing that auction-based methods perform well in comparison with optimal allocation methods, with the advantage of scaling better. This paper compares several auction-based methods not on the optimality of the allocation, but on the efficiency of the execution of the allocated tasks giving a fuller picture of the practical use of auction-based methods. Our results show that the advantages of the best auction-based methods are much reduced when the robots are physically dispersed throughout the task space and when the tasks themselves are allocated over time.
The work presented here investigates how environmental features can be used to help select a task allocation mechanism from a portfolio in a multi-robot exploration scenario. In particular, we look at clusters of task locations and the positions of team members in relation to cluster centres. In a data-driven approach, we conduct experiments that use two different task allocation mechanisms on the same set of scenarios, providing comparative performance data. We then train a classifier on this data, giving us a method for choosing the best mechanism for a given scenario. We show that selecting a mechanism via this method, compared to using a single state-of-the-art mechanism only, can improve team performance in certain environments, according to our metrics.
Abstract— This paper gives an overview of ROS, an open- source robot operating,system. ROS is not an operating,system in the traditional sense of process management,and scheduling; rather, it provides a structured communications layer above the host operating,systems,of a heterogenous,compute,cluster. In this paper, we discuss how ROS relates to existing robot software frameworks, and briefly overview some of the available application software,which,uses ROS.
Joseph Redmon and Ali Farhadi
- Joseph Redmon
- Ali Farhadi
- Redmon Joseph