Robotics for Improving Quality, Safety and Productivity in Intensive Agriculture: Challenges and Opportunities

Belforte, Guido; Aimonino, Davide Ricauda

doi:10.5772/4923

Cited by 10 publications

(9 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the robot can autonomously discard the slave cart in special slave park areas in order to be free to drive along the horizontal plane as a normal mobile robot would. Envisioned applications of this type of architecture include highly compact storage environments such as greenhouses [32] or pharmaceutical warehouses, where complex transfer operations need to be carried out [33]. In these environments, pick-and-place operations and subsequent transfer to remote locations in the industrial floor are usually needed.…”

Section: Robot Architecturementioning

confidence: 99%

Upside-Down Robots: Modeling and Experimental Validation of Magnetic-Adhesion Mobile Systems

et al. 2019

View full text Add to dashboard Cite

In this paper, we present the modeling and validation of a new family of climbing robots that are capable of adhering to vertical surfaces through permanent magnetic elements. The robotic system is composed of two modules, the master and the follower carts, which are arranged in a sandwich configuration, with the surface to climb interposed between them. Thanks to this configuration, the mobile robot can climb even nonferromagnetic and curved surfaces; moreover, the master cart is capable of freely moving on the floor by detaching from the follower. In this paper, we propose the mathematical modeling, simulation, and experimental validation of this kind of robots, with particular focus on the transitions between floor and climbing motion.

show abstract

Section: Robot Architecturementioning

confidence: 99%

Upside-Down Robots: Modeling and Experimental Validation of Magnetic-Adhesion Mobile Systems

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The term “artificial intelligence” was proposed in a series of workshops at the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in 1956 [ 4 ]. Academia and industry have applied AI to solve various problems such as decision making [ 5 ], environmental monitoring [ 6 , 7 ], lower operational costs [ 8 ], and increase productivity [ 9 ]. The advent of technological advances in robotics, sensors, data management, and computer technology on one hand, and powerful machine learning (ML) algorithms, on the other hand, have opened vast opportunities to apply AI in various fields ( Figure 2 ).…”

Section: Introductionmentioning

confidence: 99%

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Pishgar

Issa

Sietsema

et al. 2021

IJERPH

View full text Add to dashboard Cite

Introduction: The field of artificial intelligence (AI) is rapidly expanding, with many applications seen routinely in health care, industry, and education, and increasingly in workplaces. Although there is growing evidence of applications of AI in workplaces across all industries to simplify and/or automate tasks there is a limited understanding of the role that AI contributes in addressing occupational safety and health (OSH) concerns. Methods: This paper introduces a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) that highlights the role that AI plays in the anticipation and control of exposure risks in a worker’s immediate environment. Two hundred and sixty AI papers across five sectors (oil and gas, mining, transportation, construction, and agriculture) were reviewed using the REDECA framework to highlight current applications and gaps in OSH and AI fields. Results: The REDECA framework highlighted the unique attributes and research focus of each of the five industrial sectors. The majority of evidence of AI in OSH research within the oil/gas and transportation sectors focused on the development of sensors to detect hazardous situations. In construction the focus was on the use of sensors to detect incidents. The research in the agriculture sector focused on sensors and actuators that removed workers from hazardous conditions. Application of the REDECA framework highlighted AI/OSH strengths and opportunities in various industries and potential areas for collaboration. Conclusions: As AI applications across industries continue to increase, further exploration of the benefits and challenges of AI applications in OSH is needed to optimally protect worker health, safety and well-being.

show abstract

“…However, it had a successful harvest rate of less than 42 percent, and an average harvesting time of 32.3 seconds, which is 2.5 to 3 times greater than the time required for manual harvesting (Hayashi, Shigematsu, & Yamamoto, 2010). Lower-cost robotic systems based on fixed-position systems have been developed for precision fertilization and spraying (Belforte, Deboli, Gay, Piccarolo, & Ricauda Aimonino, 2006), harvesting (Foglia & Reina, 2006), and mechanical weed control (Belforte, Gay, & Ricauda Aimonino, 2007). The published reports of these systems do not specify how well they performed in a practical sense.…”

Section: Indoor Robotic Systemsmentioning

confidence: 99%

“…This type of robotic platform would be the most ideal for the plant inspection robot since plants are commonly placed in a rectangular area and the camera system does not require a large range of motion to sufficiently inspect the plants. Components and subsystems for Cartesian robots are also readily available, reducing the development cost and time (Belforte, Gay, & Ricauda Aimonino, 2007).…”

Section: Fixed-position Systemsmentioning

confidence: 99%

“…A robotic arm by itself would not efficiently move across a rectangular space. Since the robotic arm's range of motion is non-rectangular, complex computations are required to calculate the individual joint motions required to create a smooth linear trajectory (Belforte, Gay, & Ricauda Aimonino, 2007). In addition, there are typically several ways to achieve specific positions, leading to multiple solutions to the movement dynamics which must be sorted through in the control system.…”

Section: Fixed-position Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Design of a controlled environment agricultural plant inspection robot

Chen¹

View full text Add to dashboard Cite

Robotics for Improving Quality, Safety and Productivity in Intensive Agriculture: Challenges and Opportunities

Cited by 10 publications

References 34 publications

Upside-Down Robots: Modeling and Experimental Validation of Magnetic-Adhesion Mobile Systems

Upside-Down Robots: Modeling and Experimental Validation of Magnetic-Adhesion Mobile Systems

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Design of a controlled environment agricultural plant inspection robot

Contact Info

Product

Resources

About