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Research Papers: Design of Mechanisms and Robotic Systems

Design of a Passive Self-Regulating Gravity Compensator for Variable Payloads

[+] Author and Article Information
Dexter X. H. Chew

Engineering Product Development,
Singapore University of Technology and Design,
Singapore 487372, Singapore
e-mail: dexter_chew@mymail.sutd.edu.sg

Kristin L. Wood

Engineering Product Development,
Singapore University of Technology and Design,
Singapore 487372, Singapore
e-mail: kristinwood@sutd.edu.sg

U.-Xuan Tan

Engineering Product Development,
Singapore University of Technology and Design,
Singapore 487372, Singapore
e-mail: uxuan_tan@sutd.edu.sg

1Corresponding author.

Contributed by the Mechanisms and Robotics Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received April 18, 2018; final manuscript received February 26, 2019; published online May 23, 2019. Assoc. Editor: Joo H. Kim.

J. Mech. Des 141(10), 102302 (May 23, 2019) (11 pages) Paper No: MD-18-1323; doi: 10.1115/1.4043582 History: Received April 18, 2018; Accepted April 10, 2019

Most passive gravity balancing mechanisms (GBMs) require manual adjustment or actuators to alter its parameters for different payloads. The few balancers that passively self-regulate employ regulation at the end-effector, which makes the end-effector bulky. Additionally, there is a lack of systematic approach to design such compensators. Hence, this paper provides a review of current work which serves as the basis for a systematic design approach to solve the problem. Unlike previous designs, an independent self-regulating mechanism is mounted onto the proximal link of the GBM achieving better safety, larger range of motion, and loading at intermediate angles. The GBM is designed using design tools like functional modeling and morphological analysis with existing literature. This approach reveals design considerations of current GBMs and areas for innovation. Design approaches from the literature are organized and serve as a reference for innovation. A prototype is developed, and experiments are performed to illustrate the capability.

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Figures

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Fig. 1

FBD of a fully passive adjustable gravity compensation device

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Fig. 2

Fully passive automatic balancers: (a) dependent regulator—the compensating spring is used for weight sensing—and (b) independent regulator—separate adjustment springs are used for weight sensing [29,30]

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Fig. 3

Spring-and-lever balancer (left) and torque–angular displacement graph of payload (right)

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Fig. 4

Types of 1 DOF passive GBMs: (a) counterweight, (b) zero-length spring, (c) spring-cable-pulley (emulates zero-length spring), (d) parallelogram, (e) torsion spring, (f) spring-cable-cam, (g) orthogonal springs, (h) compliant beam, (i) extension and compression springs, and (j) spring-cable-pulley-gear [1,14,18,23,2528,31,32]

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Fig. 5

Design considerations: (a) functional block of self-regulating GBM showing three key functions and (b) FBD of wall and rotating arm in mechanical equilibrium

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Fig. 6

Payload changing at φadj = 135 deg via the SD adjustment concept: (a) loaded state 1 (mass A), (b) loaded state 2 (mass B) where change in (a) is a function of the output displacement, △zadj, from the self-regulating mechanism

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Fig. 7

Graphs of lifting force, Flift, as a function of a at the respective adjustment angles, φadj. To fairly illustrate the different behaviors, all graphs have a common maximum Fliftmax (where L = △smax = 300 mm and kA = 0.458 Nmm−1) [21].

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Fig. 8

Graph of lifting force, Flift, against a at ψadj = 135 deg: linear approximation of region 200 mm ≤ a ≤ 277 mm

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Fig. 9

RPS self-regulating mechanism operating with SD adjustment: (a) loaded state 1 (mass A) and (b) loaded state 2 (mass B) where mass A ≥ mass B

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Fig. 10

CAD model of prototype (top): alphabetical references correspond to Fig. 6. Actual prototype (bottom) at different views: (i) front view, (ii) side view in an adjustment state, and (iii) side view in an operational state

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Fig. 11

Graph of lifting force, Flift, as a function of a and △a at φadj = 135 deg showing linear approximation for 0mm≤∆a≤40mm with Fliftmax = 34.3 N

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Fig. 12

Experimental setup: (a) overview, (b) force sensor assembly, and (c) sensors: (i) magnetic rotary sensor to record the angular position of arm AD and (ii) triaxis force sensor to measure tangential force

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Fig. 13

(a) Graphs of lifting force against angular displacements for each value of a. Graphs generally show iso-elastic behavior for angular displacements φ≥40 deg. For φ≤40 deg, Flift appears to be increasing linearly. (b) Graphs of lifting force against a and △a for both experimental and theoretical graphs. The linear graph represents the theoretical weight-sensing behavior of the adjustment springs (it references the right y-axis).

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Fig. 14

Graph of weight of payload, Wload, as a function of average vertical displacement, h. The higher experimental spring constant compared to theoretical constant suggests energy losses.

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Fig. 15

Graph of weight of payload, Wload, against mean △a showing spool offset at a = 248.05 mm upon self-regulation and initial position of a = 256 mm, depicting the GBM’s self-regulation behavior

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Fig. 16

Graph of lifting force Flift against weight of payload, Wload, representing the overall compensating behavior of the system. The ideal full compensation is marked as solid straight line where Flift = Wload. The maximum and minimum absolute and percentage errors are marked.

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Fig. 17

Experimental setup: (a) measured muscle groups on the upper limb using the EMG sensors (brachioradialis, flexor carpi ulnaris, biceps brachii, and triceps brachii) and (b) four measurable conditions for muscle activation. From left to right: At rest, no load, no compensation with load, and compensation with load.

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Fig. 18

Graph of root-mean-square amplitude of muscle excitations (mV) against time (s) at different loads for most significant sensor, EMG 15. Conditions: (a) at rest and no load, (b) m = 1.17 kg, (c) m = 2.50 kg, and (d) m = 1.80 kg.

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Fig. 19

Bar plots showing the mean and standard deviation of the amplitude RMS for the EMG activity of the bicep brachii, averaged over five repetitions. Bar plots for the two cycles: (a) upward cycle and (b) downward cycle.

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