Challenges and Opportunities in Robotic Food Handling: A Review

Wang, Zhongkui; Hirai, Shinichi; Kawamura, Sadao

doi:10.3389/frobt.2021.789107

Cited by 71 publications

(23 citation statements)

References 72 publications

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“…The Equation (21) to Equation ( 23) approximate the friction torque: 6 From the left: total Joint torque requirement for simultaneous joint movements, Predicted torque, Proportional Derivative (PD) controller's contribution The employed friction model is shown in Figure 6 as a function of angular velocity.…”

Section: Hasanmentioning

confidence: 99%

“…Robotic uses are not limited to a specific field. Currently, robots are being employed in industrial automation [1], space exploration [2], agriculture [3], healthcare [4], oceanographic research [5], food pre-and post-processing applications [6], manufacturing technology [7], military applications [8] and human power assistance or rehabilitation applications [9][10][11]. Robots are a key piece of Industry 4 Smart factory [12].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, robots are widely used in industrial automation [1], healthcare system [2], space exploration [3], agriculture [4], underwater exploration [5], food pre-post-processing applications [6], manufacturing technology [7,8], military application [9], human power assistance and rehabilitation applications [10][11][12]. Industry 4 Smart factory technology heavily relies on robots [13].…”

Section: Introductionmentioning

confidence: 99%

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Radial basis function‐based exoskeleton robot controller development

Hasan

2022

IET Cyber-Syst and Robotics

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The realisation of a model-based controller for a robot with a higher degree of freedom requires a substantial amount of computational power. A high-speed CPU is required to maintain a higher sampling rate. Multicore processors cannot boost the performance or reduce the execution time as the programs are sequentially structured. The neural network is a great tool to convert a sequentially structured program to an equivalent parallel architecture program. In this study, a radial basis function (RBF) neural network is developed for controlling 7 degrees of freedom of the human lower extremity exoskeleton robot. A realistic friction model is used for modelling joint friction. High trajectory tracking accuracies have been obtained. Evidence of computational efficiency has been observed. The stability analysis of the developed controller is presented. Analysis of variance is used to assess the controller's resilience to parameter variation. To show the effectiveness of the developed controller, a comparative study was performe between the developed RBF network-based controller and Sliding Mode Controller, Computed Torque Controller, Adaptive controller, Linear Quadratic Regulator and Model Reference Computed Torque Controller.

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Section: Hasanmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radial basis function‐based exoskeleton robot controller development

Hasan

2022

IET Cyber-Syst and Robotics

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“…Food is a central element of human life and has various industrial demands. In the context of population aging, robotics applications such as home cooking assistance [1] and foodpreparing robots in factories and restaurants [2], [3] have high expectations; however, food manipulation is non-trivial because food is edible and thus fragile.…”

Section: Introductionmentioning

confidence: 99%

Learning by Breaking: Food Fracture Anticipation for Robotic Food Manipulation

et al. 2022

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This study aimed to anticipate fractures of fragile food during robotic food manipulation. Anticipating fractures allows a robot to manipulate ingredients without irreversible failure. Food fracture models investigated in food texture fields explain the properties of fragile objects well. However, they may not directly apply to robot manipulation due to the variance in physical properties even within the same ingredient. To this end, we developed a fracture-anticipation system with a tactile sensing module and a simple recurrent neural network. The key idea was to allow the robot to break ingredients during training-sample collection. The timing of fractures was identified via simple signal processing and used for supervision. We performed real robot experiments with three typical fragile foods: tofu, potato chips, and bananas. As the first step toward flexible fragile-object manipulation, we evaluated the proposed method for the fundamental task of object picking. The method successfully grasped the fragile foods without fractures in an online demonstration. In an offline evaluation, the method predicted the fractures with a recall of approximately 80% for all ingredients with 60 breaking trials. We believe that our method can be used to avoid breakage in other types of food manipulation, e.g., holding, pressing, and rolling.

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“…In recent years, much study has concentrated on using robots in the food industry, especially research on manipulating ingredients and foods [1]- [4]. However, most previous studies have focused on robustness [2], [3] and accuracy [5] of the food manipulation; only a few have addressed eye appeal [4].…”

Section: Introductionmentioning

confidence: 99%

Physically Consistent Preferential Bayesian Optimization for Food Arrangement

Kwon

Tsurumine

Shimmura³

et al. 2022

IEEE Robot. Autom. Lett.

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This paper considers the problem of estimating a preferred food arrangement for users from interactive pairwise comparisons using Computer Graphics (CG)-based dish images. As a foodservice industry requirement, we need to utilize domain rules for the geometry of the arrangement (e.g., the food layout of some Japanese dishes is reminiscent of mountains). However, those rules are qualitative and ambiguous; the estimated result might be physically inconsistent (e.g., each food physically interferes, and the arrangement becomes infeasible). To cope with this problem, we propose Physically Consistent Preferential Bayesian Optimization (PCPBO) as a method that obtains physically feasible and preferred arrangements that satisfy domain rules. PCPBO employs a bi-level optimization that combines a physical simulation-based optimization and a Preference-based Bayesian Optimization (PbBO). Our experimental results demonstrated the effectiveness of PCPBO on simulated and actual human users.

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Challenges and Opportunities in Robotic Food Handling: A Review

Cited by 71 publications

References 72 publications

Radial basis function‐based exoskeleton robot controller development

Radial basis function‐based exoskeleton robot controller development

Learning by Breaking: Food Fracture Anticipation for Robotic Food Manipulation

Physically Consistent Preferential Bayesian Optimization for Food Arrangement

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