Image-based control of delta parallel robots via enhanced LCM-CSM to track moving objects

Lopez-Lara, J. Guillermo; Maya, Mauro; González, Alejandro; Cárdenas, Antonio; Félix, Liliana

doi:10.1108/ir-09-2019-0197

Cited by 5 publications

(14 citation statements)

References 22 publications

(25 reference statements)

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“…The proposed parallel robot was comprised of a fixed main supporting frame and a moving platform linked by a rotating shaft and three parallel kinematic chains. [31,32] Servo motor and reducer were installed on static platform, and end-effector was installed on a moving platform. The servo motor was controlled by the servo drive and transmitted the power to the drive arm through the reducer.…”

Section: Experimental Materials and Mechanismsmentioning

confidence: 99%

Machine vision on the positioning accuracy evaluation of poultry viscera in the automatic evisceration robot system

Chen

Kong

et al. 2021

International Journal of Food Properties

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In the poultry slaughtering, accurate viscera positioning is essential to reduce the damage of internal organs. The introduction of machine vision technique can help to locate the viscera and can provide new direction for the poultry evisceration. After a midline abdominal incision of the poultry, the internal organs are taken out from the poultry that placed on the conveyor line by the parallel robot. Based on machine vision, the recognition accuracy of opened poultry viscera directly affects the level of visceral damage and residue caused by gripping manipulator. However, visceral positioning is often influenced by different noise in the abdominal cavity, such as mucous membranes and blood stains. Thus, the image segmentation of poultry viscera is a complex process, and it is challenging to remove the noise. In general, existing image segmentation methods can hardly segment visceral regions well. To strengthen the anti-noise ability, we proposed an improved regionbased active contour method with the level-set formulation. This method combined with several operations of color space conversion and top-low-hat transformation, which could extract the viscera contour and precisely removed the noise. The results showed that recognition accuracy of the heart-liver area and fat area in the viscera are 98.98% and 99.75%, respectively, while the overall viscera for poultry was 98.96%. The results of this experiment suggested that the proposed image segmentation algorithm could achieve the required accuracy for poultry viscera detection. Thus, the proposed visceral contour recognition method could be applied in poultry processing, providing critical information to guide the robot for automated evisceration.

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Section: Experimental Materials and Mechanismsmentioning

confidence: 99%

Machine vision on the positioning accuracy evaluation of poultry viscera in the automatic evisceration robot system

Chen

Kong

et al. 2021

International Journal of Food Properties

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“…In the experiment, the wheel will move forward at a uniform speed and the manipulator needs to move according to the position feedback of the visual sensor to complete the online refueling action. To test the performance of the algorithm, we also use the existing IRL algorithm, Jacobian matrix (Qin et al , 2018), the LCM-CSM method (Lopez-Lara et al , 2020), hybridized sine-cosine algorithm (Kumar et al , 2020) and the DL algorithm (Levine et al , 2017) for comparative experiments.…”

Section: Experiments and Verificationmentioning

confidence: 99%

“…Howbeit, this method hardly deals with the wrong links in the process of high-speed learning, which means that it is difficult to ensure the stability of continuous work. From the paper (Lopez-Lara et al , 2020), a new vision-based control method has been proposed, which enables delta-type parallel robots to track and manipulate objects moving in arbitrary trajectories. A new variant of the linear camera model-camera space manipulation (LCM-CSM) method, traditionally used only for static positioning of a robot’s end-effector, was applied to parallel robots enabling the manipulation of objects moving along unknown trajectories.…”

Section: Introductionmentioning

confidence: 99%

Robot hand-eye cooperation based on improved inverse reinforcement learning

Lin

2021

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Purpose How to make accurate action decisions based on visual information is one of the important research directions of industrial robots. The purpose of this paper is to design a highly optimized hand-eye coordination model of the robot to improve the robots’ on-site decision-making ability. Design/methodology/approach The combination of inverse reinforcement learning (IRL) algorithm and generative adversarial network can effectively reduce the dependence on expert samples and robots can obtain the decision-making performance that the degree of optimization is not lower than or even higher than that of expert samples. Findings The performance of the proposed model is verified in the simulation environment and real scene. By monitoring the reward distribution of the reward function and the trajectory of the robot, the proposed model is compared with other existing methods. The experimental results show that the proposed model has better decision-making performance in the case of less expert data. Originality/value A robot hand-eye cooperation model based on improved IRL is proposed and verified. Empirical investigations on real experiments reveal that overall, the proposed approach tends to improve the real efficiency by more than 10% when compared to alternative hand-eye cooperation methods.

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“…Throughout the years, computer vision research has seen an evolution of the camera calibration algorithms with new proposals dramatically improving the robustness for different scenarios [39,54]. Most of the calibration methods make use of a calibration pattern to find the camera model [55].…”

Section: Linear-camera Model Camera Space Manipulationmentioning

confidence: 99%

“…Moreover, they are low cost, easy to implement, and several tools for image processing and analysis are available [36][37][38]. Vision-based control techniques have also been widely applied to control manipulators for the ease of controlling the relative position of the robot with respect to other objects in motion [39]. The control is robust to the distinctive uncertainties of the dynamic environment [40].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Control of a Mobile Manipulator With an Adaptable-Passive Suspension for Unstructured Environments

Cárdenas

Quiroz

Hernandez

et al. 2021

Journal of Mechanisms and Robotics

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The kinematic design and navigation control of a new autonomous mobile manipulator for uneven terrain is presented in this work. An innovative suspension system's design is based on the kinematic synthesis of an adaptable, passive mechanism that compensates for irregularities in the terrain and facilitate the control of the robotic platform using cameras. The proposed mobile robot suspension consists of two pairs of bogies joined by a crank-slider mechanism that allows the robot to adapt to the terrain irregularities. The mobile robot is also equipped with a robotic manipulator, of which a synthesis, simulation, and experimental validation are presented while manipulation is accomplished during movements on rough terrain. The proposed mobile robot has been fabricated using additive manufacturing (AM) techniques. A linear camera space manipulation (LCSM) control system has been developed and implemented to conduct experimental tests along uneven terrain. This mobile manipulator has been designed to transverse uneven terrain so that the loading platform is kept horizontal while crossing obstacles up to one-third of the size of its wheels. This feature allows the onboard cameras to stay oriented towards the target. The vision-based paradigm that enables the control of this mobile manipulator allows to estimate the position and orientation of its end effector and update the trajectory of the manipulator along the path towards the target. The experiments show a final precision for engagement of a pallet within +/− 2.5 mm in position and +/− 2 degrees in orientation.

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Image-based control of delta parallel robots via enhanced LCM-CSM to track moving objects

Cited by 5 publications

References 22 publications

Machine vision on the positioning accuracy evaluation of poultry viscera in the automatic evisceration robot system

Machine vision on the positioning accuracy evaluation of poultry viscera in the automatic evisceration robot system

Robot hand-eye cooperation based on improved inverse reinforcement learning

Vision-Based Control of a Mobile Manipulator With an Adaptable-Passive Suspension for Unstructured Environments

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