Integration of a Human-aware Risk-based Braking System into an Open-Field Mobile Robot

Mayoral, Jose C.; Grimstad, Lars; From, Pål Johan; Cielniak, Grzegorz

doi:10.1109/icra48506.2021.9561522

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Risk estimation for the environmental obstacles can be modelled as a function of time, position, the robot's orientation, and risk layers around the robot. Mayoral et al [36] provided a framework for risk assessment using, as a test case, human detection in an open-field environment, and its conceptual idea can be observed in Figure 4. This method is used as a risk-based braking system, and its concept utilizes human 3D position sampling to evaluate the 2D projected position in the map using a degradation function.…”

Section: Braking System Functionmentioning

confidence: 99%

Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method

2023

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Safe navigation is a key objective for autonomous applications, particularly those involving mobile tasks, to avoid dangerous situations and prevent harm to humans. However, the integration of a risk management process is not yet mandatory in robotics development. Ensuring safety using mobile robots is critical for many real-world applications, especially those in which contact with the robot could result in fatal consequences, such as agricultural environments where a mobile device with an industrial cutter is used for grass-mowing. In this paper, we propose an explicit integration of a risk management process into the design of the software for an autonomous grass mower, with the aim of enhancing safety. Our approach is tested and validated in simulated scenarios that assess the effectiveness of different custom safety functionalities in terms of collision prevention, execution time, and the number of required human interventions.

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Section: Braking System Functionmentioning

confidence: 99%

Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method

2023

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“…Algunos investigadores también utilizan métricas que cuantifican el riesgo, buscando garantizar la seguridad de los robots que operan en condiciones de incertidumbre (Majumdar and Pavone, 2020), (Mayoral et al, 2021).…”

Section: Colisionesunclassified

Criterios de desempeño para evaluar algoritmos de navegación de robots móviles: una revisión

Ceballos

Suarez-Rivera

2022

Rev. iberoam. autom. inform. ind.

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En este artículo se presenta una revisión de literatura sobre criterios de desempeño para evaluar la navegación de un robot móvil, los cuales ayudan a comparar cuantitativamente diferentes características, como: el sistema de control, la navegación en diferentes entornos de trabajo, el desempeño energético, etc. El interés en criterios de desempeño y procedimiento de comparación (benchmarks) ha crecido en los últimos años, principalmente por investigadores y fabricantes de robots que buscan satisfacer la creciente demanda de aplicaciones en el mercado global, cada vez más competido. El conjunto de criterios está compuesto por métricas, índices, mediciones y benchmarks, desde el más básico como contabilizar el éxito en alcanzar la meta, pasando por otros más elaborados como los de seguridad en la trayectoria generada en la evasión de obstáculos, hasta criterios que comparan aspectos más complejos de la navegación como el consumo energético. Finalmente, se describen algunos benchmarks y software para simulación y comparación de algoritmos de navegación. Estos criterios se constituyen en una importante herramienta para diseñadores e investigadores en robótica móvil.

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Implementation of a human‐aware robot navigation module for cooperative soft‐fruit harvesting operations

Guevara

Hanheide

Parsons

2023

Journal of Field Robotics

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In the last decades, robotic solutions have been introduced in agriculture to improve the efficiency of tasks such as spraying, plowing, and seeding. However, for a more complex task like soft‐fruit harvesting, the efficiency of experienced human pickers has not been surpassed yet by robotic solutions. Thus, in the immediate future, human labor will probably be still necessary for picking tasks while robotic platforms could be used as collaborators, supporting the pickers in the transportation of the harvested fruit. This cooperative harvesting strategy creates a human–robot interaction (HRI) that requires significant further development in human‐aware safe navigation and effective bidirectional communication of intent. In fact, although agricultural robots are considered small/medium size machinery, they still represent a risk of causing injuries to human collaborators, especially if people are not trained to work with robots or robot operations are not intuitive. Avoiding such injury is the aim of this work which contributes to the development, implementation, and evaluation of a human‐aware navigation (HAN) module that can be integrated into the autonomous navigation system of commercial agricultural robots. The proposed module is responsible for the detection and monitoring of humans working around the robot and uses this information to activate safety actions depending on whether the human presence is considered at risk or not. Apart from ensuring a physically safe HRI, the proposed module deals with the comfort level and psychological safety of human coworkers. The latter is possible by using an explicit human–robot communication strategy that lets both know of the other's intentions, increasing the level of trust and reducing inefficient pauses triggered by unnecessary safety actions. The proposed HAN solution was integrated into a commercial agricultural robot and tested in several situations that are expected to happen during cooperative harvesting operations. The results of an usability assessment illustrated the benefits of the proposal in terms of safety, efficiency, and ergonomics.

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Integration of a Human-aware Risk-based Braking System into an Open-Field Mobile Robot

Cited by 4 publications

References 21 publications

Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method

Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method

Criterios de desempeño para evaluar algoritmos de navegación de robots móviles: una revisión

Implementation of a human‐aware robot navigation module for cooperative soft‐fruit harvesting operations

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