Initial experiments on learning-based randomized bin-picking allowing finger contact with neighboring objects

Harada, Kensuke; Wan, Weiwei; Tsuji, Tokuo; Kikuchi, K.; Nagata, Kazuyuki; Onda, Hiromu

doi:10.1109/coase.2016.7743541

Cited by 20 publications

(23 citation statements)

References 16 publications

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“…(5) We are exploring cutting-edge technique like machine learning to solve difficult problems like picking from clutter [7]. The technique in items (1) to (4) was conventional and the goal was to find collisionfree and kinematic available robot and hand configurations.…”

Section: Development Of Intelligent Robots For Assembly At Aistmentioning

confidence: 99%

“…(4) Collecting training data for realworld robots is difficult. Although some inspiring examples have been done in [7,17], it is still difficult to allow failures and program robots to learn from failures online in real factories. (5) Force sensing and state analyzing are highly dependent on specific objects.…”

Section: Challengesmentioning

confidence: 99%

“…The assembly planner could automatically generate assembly sequences (order of assembly), assembly directions, and available grasps. The assembly planner is a high-level planning component and could work together with middle and low-level planning components discussed in item (3), and the vision system discussed in item (1), to perform integrated assembly and motion planning.(5) We are exploring cutting-edge technique like machine learning to solve difficult problems like picking from clutter [7]. The technique in items (1) to (4) was conventional and the goal was to find collisionfree and kinematic available robot and hand configurations.…”

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confidence: 99%

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Using Intelligent Robots to Assemble Automobile Parts

Wan¹

2017

Adv Automob Eng

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This paper presents the available technique and discusses the difficulties to implement intelligent robots in nextgeneration automobile assembly. First, it presents the status of automobile assembly line, analyzes the problems and difficulties of current industrial robots-based assembly systems, and summarizes the technology needed to overcome the problems and difficulties. Then, it presents the new technology for intelligent assembly developed at National Institute of Advanced Science and Technology (AIST), which can be directly used to implement intelligent robots for automobile assembly. Next, the paper analyzes the drawbacks of the newly developed technology and discusses remaining challenges, and presents our view of next-generation automobile assembly systems. Using Intelligent Robots to Assemble Automobile PartsWeiwei Wan* National Institute of Advanced Industrial Science and Technology (AIST), Japan Keywords: Intelligent robots; Automobile assembly systems; Sensors; Databases States in Automobile Assembly LineIndustrial robots are widely used in automobile assembly lines. These robots are called industrial robots since their goal is to follow some pre-taught paths or way points, which is essentially the same as automation machines. The tasks performed by these robots include welding, painting, pick-and-place in structured environment (e.g., glass installation, door installation, and nut/bolt fastening), etc.The tasks performed by industrial robots are exciting. On the other hand, there are lots of tasks remained to be done by human workers. These tasks include (1) picking parts from cluster, (2) reorienting parts to certain poses, and (3) force-based assembly. These tasks cannot be done using industrial robots and teach pendants since: (1) The parts shapes and physical properties are varying. It is difficult to manually specify the grasping strategies for infinite number of parts. (2) The initial poses and goal poses of manipulated parts are changing. It is difficult to teach the robots all motions to reorient different parts. (3) The first two processes lead to variety in force control and assembly, which also make pre-teaching difficult.One advisable way to automate the remaining tasks is to develop intelligent robots. The fundamental technique includes (1) computer vision for object recognition, (2) object analyzer for grasp planning, (3) motion planning, (4) force analysis and assembly planning, and (5) machine learning. Development of Intelligent Robots for Assembly at AISTThe manipulation group at National Institute of Advanced Industrial Science and Technology (AIST), Japan, is developing intelligent robots to assembly objects. Over the years, we have developed software for all fundamental technique.(1) We developed object recognition technique by using point clouds collected from structure light-based depth sensors. The developed platform has high precision as it uses point clouds and geometric constraints in the robot execution phase to avoid unexpected noises [1]. We also developed mult...

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Section: Development Of Intelligent Robots For Assembly At Aistmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

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confidence: 99%

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Using Intelligent Robots to Assemble Automobile Parts

Wan¹

2017

Adv Automob Eng

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“…This paper focuses on the arrangement of the objects. Previous researches on robot picking, which depends on the arrangement of the objects, have mainly handled random bin picking problems (e.g., [10][11][12][13][14][15][16][17][18][19][20][21][22][23]). The random picking indicates the problem in which randomly arranged objects, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Detection of object arrangement patterns using images for robot picking

Asaoka¹,

Nagata²,

Nishi³

et al. 2018

Robomech J

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This paper focuses on robot picking of objects in warehouses and stores. Objects are often regularly stacked or aligned in specific arrangement patterns to increase storage efficiency. There are typical patterns in arrangement patterns. A specific picking strategy set is often linked to specific arrangement patterns. By linking the arrangement patterns of various object categories to picking strategy sets, the picking performance of a robot is expected to improve. In this paper, we propose a method in which groups of regularly arranged objects are detected from an image, and the arrangement pattern of each group is identified. In this paper, we describe the effectiveness of the proposed method based on experiment results for "book" as the target.

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“…Although a number of researches have been done on randomized bin-picking [1,2,3,4], randomized bin-picking is still difficult and is not widely introduced to production processes. Since one of the main reasons is its low success rate of the pick, we have proposed a learning based approach which can automatically increase the success rate [5]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Iterative Visual Recognition for Learning Based Randomized Bin-Picking

Harada

Wan

Tsuji

et al. 2017

Springer Proceedings in Advanced Robotics

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This paper proposes a iterative visual recognition system for learning based randomized bin-picking. Since the configuration on randomly stacked objects while executing the current picking trial is just partially different from the configuration while executing the previous picking trial, we consider detecting the poses of objects just by using a part of visual image taken at the current picking trial where it is different from the visual image taken at the previous picking trial. By using this method, we do not need to try to detect the poses of all objects included in the pile at every picking trial. Assuming the 3D vision sensor attached at the wrist of a manipulator, we first explain a method to determine the pose of a 3D vision sensor maximizing the visibility of randomly stacked objects. Then, we explain a method for detecting the poses of randomly stacked objects. Effectiveness of our proposed approach is confirmed by experiments using a dual-arm manipulator where a 3D vision sensor and the two-fingered hand attached at the right and the left wrists, respectively.

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Initial experiments on learning-based randomized bin-picking allowing finger contact with neighboring objects

Cited by 20 publications

References 16 publications

Using Intelligent Robots to Assemble Automobile Parts

Using Intelligent Robots to Assemble Automobile Parts

Detection of object arrangement patterns using images for robot picking

Iterative Visual Recognition for Learning Based Randomized Bin-Picking

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