Dynamic grasp and trajectory planning for moving objects

Marturi, Naresh; Kopicki, Marek; Rastegarpanah, Alireza; Rajasekaran, Vijaykumar; Adjigble, Maxime; Stolkin, Rustam; Leonardis, Aleš; Bekiroglu, Yasemin

doi:10.1007/s10514-018-9799-1

Cited by 78 publications

(41 citation statements)

References 29 publications

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“…Overall, we define the following conditions as trial failures: F1) If the gripper fingers or any used equipment knocks-off the object from its place while reaching-to-grasp; F2) If the object slips or rolls away while executing the grasp or while lifting the grasped object; F3) If the designed rotational test is failed; F4) If the designed shaking test is failed; F5) If no feasible hypotheses are found, e.g. due to robot kinematics or object placement (which only applies to planners with integrated reachability search, [3], [26]); F6) If the hardware failed to respond due communication drops, process timeouts, etc. For each object the results can be summarized using the format specified in Tables I and II for experiments using single and a group of objects, respectively.…”

Section: B Grasp Execution and Scoringmentioning

confidence: 99%

Benchmarking Protocol for Grasp Planning Algorithms

Bekiroglu

Marturi

Roa

et al. 2020

IEEE Robot. Autom. Lett.

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Numerous grasp planning algorithms have been proposed since the 1980s. The grasping literature has expanded rapidly in recent years, building on greatly improved vision systems and computing power. Methods have been proposed to plan stable grasps on: known objects (exact 3D model is available), familiar objects (e.g. exploiting a-priori known grasps for different objects of the same category), or novel object shapes observed during task execution. Few of these methods have ever been compared in a systematic way, and objective performance evaluation of such complex systems remains problematic. Difficulties and confounding factors include: different assumptions and amounts of a-priori knowledge in different algorithms; different robots, hands, vision systems and setups in different labs; different choices or application needs for grasped objects. Also, grasp planning can use different grasp quality metrics (including empirical or theoretical stability measures), or other criteria, e.g. computational speed, or combination of grasps with reachability considerations. While acknowledging and discussing the outstanding difficulties surrounding this complex topic, we propose a methodology for reproducible experiments to compare the performance of a variety of grasp planning algorithms. Our protocol attempts to improve the objectivity with which different grasp planners are compared by minimising the influence of key components in the grasping pipeline, e.g. vision and pose estimation. The protocol is demonstrated by evaluating two different grasp planners: a state-of-the-art model-free planner, and a popular open-source model-based planner. We show results from real-robot experiments with a 7-DoF arm and 2-finger hand, and simulation-based evaluations.

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Section: B Grasp Execution and Scoringmentioning

confidence: 99%

Benchmarking Protocol for Grasp Planning Algorithms

Bekiroglu

Marturi

Roa

et al. 2020

IEEE Robot. Autom. Lett.

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“…Similarly, Kim [ 41 ] developed an Opti-Track system to track the target ball, load by objects, and quickly predict the trajectory of the target and capture it, where the trajectory of the target was learned and trained by machine-learning algorithms. Furthermore, in [ 42 ], the authors proposed a method of dynamic switching between local and global planners to track and grasp moving objects. The local planner maintained the end-effector with a steady grasping posture to smoothly follow the object motion.…”

Section: Related Workmentioning

confidence: 99%

Mobile Manipulation Integrating Enhanced AMCL High-Precision Location and Dynamic Tracking Grasp

Zhou

Chou

Tuo

et al. 2020

Sensors

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Mobile manipulation, which has more flexibility than fixed-base manipulation, has always been an important topic in the field of robotics. However, for sophisticated operation in complex environments, efficient localization and dynamic tracking grasp still face enormous challenges. To address these challenges, this paper proposes a mobile manipulation method integrating laser-reflector-enhanced adaptive Monte Carlo localization (AMCL) algorithm and a dynamic tracking and grasping algorithm. First, by fusing the information of laser-reflector landmarks to adjust the weight of particles in AMCL, the localization accuracy of mobile platforms can be improved. Second, deep-learning-based multiple-object detection and visual servo are exploited to efficiently track and grasp dynamic objects. Then, a mobile manipulation system integrating the above two algorithms into a robotic with a 6-degrees-of-freedom (DOF) operation arm is implemented in an indoor environment. Technical components, including localization, multiple-object detection, dynamic tracking grasp, and the integrated system, are all verified in real-world scenarios. Experimental results demonstrate the efficacy and superiority of our method.

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“…The other is to combine machine learning or deep learning to achieve a more intelligent grasp and better completion. Accordingly, a combination of identification and tracking [6,7] is highly required. As we all know, people are able to recognize the type and location of objects at first glance.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to object recognition, tracking is also indispensable for the robotic arm to make a precise grasp [6]. In order to provide the robotic arm with the position of the next moment, the current measurement, the observation measurement, and the system model in the dynamic system should be known [19].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid YOLOv4 and Particle Filter Based Robotic Arm Grabbing System in Nonlinear and Non-Gaussian Environment

et al. 2021

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In this paper, we propose a robotic arm grasping system suitable for complex environments. For a robotic arm, in order to achieve its accurate grasp of the target object, not only the vision but also a certain tracking ability should be provided. To improve the grasp quality, we propose a robotic arm grasping system using YOLOv4 combined with a particle filter (PF) algorithm, which can be applied in a nonlinear and non-Gaussian environment. Firstly, the coordinates of the bounding box in the image can be obtained through the YOLOv4 object detection algorithm. Secondly, the coordinates in the world system can be obtained through the eye-to-hand calibration system. Thirdly, a PF model can be established based on the coordinate changes of the target object. Finally, according to the predicted output of the PF, the robotic arm and the target object can reach the specific position at the same time and complete the grab. As the target object, the bowl is applied to experiments for the sake of achieving a more convincing demonstration. The experimental results show that the robotic arm grasping system proposed in this paper can accomplish the successful grasp at a rate of nearly 88%, even at a higher movement speed, which is of great significance to robot applications in various fields.

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Dynamic grasp and trajectory planning for moving objects

Cited by 78 publications

References 29 publications

Benchmarking Protocol for Grasp Planning Algorithms

Benchmarking Protocol for Grasp Planning Algorithms

Mobile Manipulation Integrating Enhanced AMCL High-Precision Location and Dynamic Tracking Grasp

A Hybrid YOLOv4 and Particle Filter Based Robotic Arm Grabbing System in Nonlinear and Non-Gaussian Environment

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