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  • 學位論文

以基礎幾何模型搭配具主被動自由度和掌內壓力與近接感測之夾爪達到快速低計算成本之多樣化物體夾取

A Fast and Low-Computation Object Grasping Method by Using Primitive-Shape Models and a Compliant Gripper with in-hand Proximity and Pressure Sensing

指導教授 : 林沛群
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摘要


本研究的目標是結合視覺定位、紅外線陣列、壓力陣列和自行設計的被動適應性夾爪,對欲抓取的目標物體進行於多障礙物之間的穩定夾取。 機械手臂和機械夾爪用於工業或自動化生產線上等制式化的控制已經非常普及,但若是場景假設在日常生活中,則需做較複雜、多變化、結合各式感測器的控制才可以達到對各種物體進行良好的夾取,此部分的困難之處在於,機械手臂不同於人體的皮膚,是柔軟但是富有彈性的,因此皮膚可以提供很好的包覆性,且能允許皮膚在碰觸物體後仍然能做夾取姿態的調整,因此能允許視覺判定上的誤差和輕易的夾取各式的形狀;相較於皮膚,機械夾爪材質堅硬且於控制上較難達到良好的包覆性,因此能容許的定位誤差小且需要在夾爪碰到物體前就決定好夾取姿態,無法在碰觸的過程中調整。 為了使機械夾爪能增加更多的包覆性,研究中設計了一具有被動包覆性的夾爪,其原理是利用行星齒輪的特性達到一輸入兩輸出的控制,且因其為機構驅動,因此可以透過一旋轉式電位計輕易的知道整個夾爪的姿態。除了夾爪的設計之外,為了增加包覆和適應性,預計在夾爪上安裝紅外線陣列和壓力陣列。安裝紅外線的原因是,不同於肌膚,機械夾爪能允許的定位誤差和所提供對於形狀的微調性很差,因此需要紅外線陣列於夾取前作適當的調整,校正定位誤差和讓夾爪更趨於物體的形狀。而壓力陣列是目前許多夾爪都會選擇安裝的感測器,原因是壓力陣列能於夾取的過程中可以判斷夾取狀態的好壞,例如物體是否有滑動的現象並藉此還調整夾取不同物體的力量、或夾取時力中心的位置是否恰當、又或者是可以透過力和壓力陣列受力面積之間的關係判斷物體的軟硬等,因其能提供的資訊多元,因此目前被大量的選擇安裝於機械夾爪上。 為了讓壓力陣列能有好的表現,壓力陣列上通常需要覆蓋一層軟墊,其功能有增加壓力陣列覆蓋於不同曲面的覆蓋能力和提供緩衝,也可以較好的分散受力以及提供夾取時所需的摩擦力。在選擇軟墊上面,雖然較厚的軟墊可以較好的均勻分散受力,但是較厚的軟墊同時也會吸收較多的力使壓力陣列的解析度下降。原先實驗室的壓力陣列使用的是1mm厚矽膠材質的軟墊,但是實驗後發覺其仍然無法很好的反映出接觸的面積形狀,因此決定嘗試製作軟墊,並在軟墊上設計突起點,材料選擇的是PDMS。 影像定位的部分,結合OpenCV、CUDA、SIFTGPU函式庫,完成可以自行建模,並對此模型的物體透過特徵點比對進行六軸姿態的定位。其允許於混亂的背景中和物體部分被遮蓋的情況進行定位。定位誤差在正負4mm以內。有了物體的模型,可以想像成機器人即了解這個東西的樣子、外觀,但是了解其外觀之後和要決定如何夾取此物體之間仍有一大段的距離。 許多的研究都指出,人在抓取各式的物體時,只有用少數的幾個主要姿態,再配和細微的調整即能抓取各種幾何形狀的物體。因次,相較於其他研究是將模型加以分析後去決定夾取的姿態和位置,本研究所選擇的方法是,將模型簡化至某幾個特並的幾何形狀,以此幾何形狀決定主要的夾取姿態,再搭配紅外線陣列以及夾爪設計上的被動包覆性在夾取前進行細微的調整後對目標物體進行夾取。簡化物體模型不只可以節省大量為不同物體設計夾取姿態的時間,除此之外,建立在這個假設之上,研究中還提出了一個低計算量的夾取姿態演算法,其將夾取姿態壓縮至二維空間中作運算因此減少了大量的計算時間。

並列摘要


For a robot to grasp a randomly placed irregular object is a challenging task. The process includes several steps. First, the robot needs to be capable of identifying the shape, position, and posture of the object. Second, the robot needs to determine a grasping posture. Next, even when the robot can achieve the previous two tasks, the success of grasping relies on adequate contact between the gripper and the object. This issue is especially crucial when the dimensions or positioning of the object have uncertainties or when the object is fragile or soft, because the mechanical gripper has less compliance unlike the human hand. In short, the grasping task requires delicate coordination between the hand, eye, and arm. This study reports on a novel low-computation object grasping method that can classify complex objects into primitive shapes and then select the object grasping posture based on predefined grasping postures associated with the approximated primitive shapes. In this approach, the object is not precisely modeled, and the grasping posture is selected from a small number of candidates without massive search; thus, the grasping posture for the object can be quickly derived. Because the object and primitive shape have geometrical discrepancy, the gripper is compliant and equipped with infrared proximity sensors on the fingers to compensate for the geometrical uncertainties and provide adequate contact between the object and the grippers. Furtheremore, the gripper also equipped with the pressure arry for detecting the slipping and adjustinh the grasping force. The methodology is experimentally evaluated with several types of objects in different postures.

參考文獻


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