Design and control algorithm of a motion sensing-based fruit harvesting robot
In response to the demand of automatic fruit identification and harvesting, this paper presents a human-robot collaborative picking robot based on somatosensory interactive servo control. The robot system mainly consisted of four parts: picking execution mechanism, hand information acquisition syste...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Higher Education Press
2025-06-01
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| Series: | Frontiers of Agricultural Science and Engineering |
| Subjects: | |
| Online Access: | https://journal.hep.com.cn/fase/EN/PDF/10.15302/J-FASE-2024588 |
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| Summary: | In response to the demand of automatic fruit identification and harvesting, this paper presents a human-robot collaborative picking robot based on somatosensory interactive servo control. The robot system mainly consisted of four parts: picking execution mechanism, hand information acquisition system, human-machine interaction interface, and human-robot collaborative picking strategy. A six-degree-of-freedom robotic arm was designed as the picking execution mechanism. The D-H method is employed for both forward and inverse kinematic modeling of the robotic arm. A four-step inverse kinematic optimal solution selection method, including mechanical interference, correctness, rationality, and smoothness of motion, is proposed. The working principle and use of the Leap Motion controller for hand information acquisition were introduced. Data from three types of hand movements were collected and analyzed. Spatial mapping method between the Leap Motion interaction space and operating range of the robotic arm was proposed to achieve a direct correspondence between the cubic interaction box and the cylindrical space of the fan ring of the robotic arm. The test results demonstrated that the average response time of the double-click picking command was 332 ms. The average time consumption for somatosensory control targeting was 6.5 s. The accuracy rate of the picking gesture judgment was 96.7%. |
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| ISSN: | 2095-7505 |