Doria, Alberto, Mauro Tognazzo, Gianmaria Cusimano, Vera Bulsink, Adrian Cooke, and Bart Koopman. “Identification of the mechanical properties of bicycle tyres for modelling of bicycle dynamics.” Vehicle System Dynamics ahead-of-print (2012): 1-16.

http://www.tandfonline.com/doi/abs/10.1080/00423114.2012.754048

The videos are loaded from youtube (but this can be very slow).
Here are the direct links to the videos:

Download (PDF, 408KB)

Here is his presentation from the conference:

SOFIE visits UC Davis

 sudo apt-get install python python-dev libatlas3-base-dev gcc gfortran g++
 sudo aptitude install liblapack-dev

import scipy
scipy.__version__

'0.12.0.dev-c4dcc00'

import bicycledataprocessor as bdp

dataset = bdp.DataSet()

#dataset.open(mode='a')
#dataset.create_task_table()

run = bdp.Run('00104', dataset,forceRecalc=True)

Initializing the run object.
Loading metadata from the database.
Loading the raw signals from the database.
Loading the bicycle and rider data for Jason on Rigid Rider
We have foundeth a directory named: /home/cooke/workspace/BicycleDataProcessor/../BicycleParameters/data/bicycles/Rigid.
Found the RawData directory: /home/cooke/workspace/BicycleDataProcessor/../BicycleParameters/data/bicycles/Rigid/RawData
Looks like you've already got some parameters for Rigid, use forceRawCalc to recalculate.
There is no rider on the bicycle, now adding Jason.
Loaded the precalculated parameters from /home/cooke/workspace/BicycleDataProcessor/../BicycleParameters/data/riders/Jason/Parameters/JasonRigidBenchmark.txt
Computing signals from raw data.
The minimun of the error landscape is 0.244489 and the provided guess is 0.240000
Optimization terminated successfully.
         Current function value: 3.070506
         Iterations: 10
         Function evaluations: 20
Optimization terminated successfully.
         Current function value: 3.070485
         Iterations: 11
         Function evaluations: 22
This is what came out of the minimization: [ 0.24279379]
Extracting the task portion from the data.
===============================================================================
Run # 104
Environment: Horse Treadmill
Rider: Jason
Bicycle: Rigid Rider
Speed:3.0
Maneuver: Track Straight Line With Disturbance
Notes: 
===============================================================================

print run

===============================================================================
Run # 104
Environment: Horse Treadmill
Rider: Jason
Bicycle: Rigid Rider
Speed:3.0
Maneuver: Track Straight Line With Disturbance
Notes: 
===============================================================================

In [7]:

run.verify_time_sync()

Out [7]:

run.rawSignals

{'AccelerationX': RawSignal([      nan,  3.657561,  3.633692, ...,  3.638674,  3.994708,
        3.660625]),
 'AccelerationY': RawSignal([       nan,  0.2808139,  0.376253 , ..., -1.773645 , -1.783415 ,
       -1.621154 ]),
 'AccelerationZ': RawSignal([      nan, -9.272774, -8.456081, ..., -9.246369, -9.099599,
       -9.268735]),
 'AngularRateX': RawSignal([             nan,  -5.63320900e-02,  -5.47738700e-02, ...,
        -2.24847600e-02,  -6.89434500e-03,  -3.65685400e-05]),
 'AngularRateY': RawSignal([        nan,  0.01581983,  0.01266712, ...,  0.00052244,
       -0.00380655, -0.00400798]),
 'AngularRateZ': RawSignal([        nan,  0.01341662,  0.01959944, ..., -0.4061159 ,
       -0.4121288 , -0.4193006 ]),
 'FiveVolts': RawSignal([ 4.98044591,  4.97995267,  4.98061032, ...,  4.97880178,
        4.97995267,  4.98011708]),
 'FrameAccelX': RawSignal([ 1.47268793,  1.47334558,  1.47120821, ...,  1.47170145,
        1.46561817,  1.47449648]),
 'FrameAccelY': RawSignal([ 1.83867164,  1.84722113,  1.8419599 , ...,  1.84557699,
        1.84541258,  1.8643201 ]),
 'FrameAccelZ': RawSignal([ 1.5761038 ,  1.56262192,  1.57577498, ...,  1.58531094,
        1.60536935,  1.59747751]),
 'MagX': RawSignal([      nan,  1.10222 ,  1.100719, ...,  1.262892,  1.261871,
        1.261935]),
 'MagY': RawSignal([       nan,  0.5896677,  0.5897546, ...,  0.4864934,  0.4874919,
        0.4897254]),
 'MagZ': RawSignal([       nan, -0.3653959, -0.3641196, ..., -0.3035113, -0.3049648,
       -0.3069487]),
 'PullForceBridge': RawSignal([ 5.51558331,  5.51656958,  5.51558331, ...,  5.51689834,
        5.51755585,  5.51788461]),
 'PushButton': RawSignal([  4.97649999e+00,   4.97649999e+00,   4.97633557e+00, ...,
         1.84891819e-03,   9.90515113e-03,  -4.23435969e-03]),
 'RollPotentiometer': RawSignal([ 2.49336511,  2.48859712,  2.48876154, ...,  2.50832671,
        2.50602493,  2.50405197]),
 'SteerPotentiometer': RawSignal([ 2.3157988 ,  2.31760734,  2.31925147, ...,  1.87665109,
        1.89111945,  1.90723194]),
 'SteerRateGyro': RawSignal([ 2.47478641,  2.46952518,  2.47626613, ...,  2.19363976,
        2.15878416,  2.13494424]),
 'SteerTorqueSensor': RawSignal([-0.83041416, -0.80838747, -0.78504575, ..., -2.29633901,
       -2.26970975, -2.26543592]),
 'Temperature': RawSignal([      nan,  317.8788,  317.8788, ...,  317.9444,  317.9444,
        317.9445]),
 'ThreeVolts': RawSignal([ 3.14904532,  3.14871649,  3.14822325, ...,  3.14855208,
        3.1488809 ,  3.14871649]),
 'WheelSpeedMotor': RawSignal([ 2.25168109,  2.25858498,  2.2546399 , ...,  2.21157272,
        2.2523386 ,  2.20401131])}

run.rawSignals['SteerRateGyro'].units
run.rawSignals['AccelerationY'].units

'meter/second/second'

run.calibratedSignals

{'AccelerationX': RawSignal([      nan,  3.657561,  3.633692, ...,  3.638674,  3.994708,
        3.660625]),
 'AccelerationY': RawSignal([       nan,  0.2808139,  0.376253 , ..., -1.773645 , -1.783415 ,
       -1.621154 ]),
 'AccelerationZ': RawSignal([      nan, -9.272774, -8.456081, ..., -9.246369, -9.099599,
       -9.268735]),
 'AccelerometerAccelerationX': Signal([-3.3299956 , -3.30311405, -3.3649416 , ..., -3.35418899,
       -3.55848877, -3.26547988]),
 'AccelerometerAccelerationY': Signal([ 8.63767194,  8.92261645,  8.75863896, ...,  8.8715415 ,
        8.86078887,  9.48175285]),
 'AccelerometerAccelerationZ': Signal([ 0.05170346, -0.38377767,  0.05439163, ...,  0.36084132,
        1.01137487,  0.75600013]),
 'AngularRateX': RawSignal([             nan,  -5.63320900e-02,  -5.47738700e-02, ...,
        -2.24847600e-02,  -6.89434500e-03,  -3.65685400e-05]),
 'AngularRateY': RawSignal([        nan,  0.01581983,  0.01266712, ...,  0.00052244,
       -0.00380655, -0.00400798]),
 'AngularRateZ': RawSignal([        nan,  0.01341662,  0.01959944, ..., -0.4061159 ,
       -0.4121288 , -0.4193006 ]),
 'FiveVolts': RawSignal([ 4.98044591,  4.97995267,  4.98061032, ...,  4.97880178,
        4.97995267,  4.98011708]),
 'ForkRate': Signal([-0.02693987, -0.03569136, -0.02450093, ..., -0.51616254,
       -0.57799684, -0.61974575]),
 'MagX': RawSignal([      nan,  1.10222 ,  1.100719, ...,  1.262892,  1.261871,
        1.261935]),
 'MagY': RawSignal([       nan,  0.5896677,  0.5897546, ...,  0.4864934,  0.4874919,
        0.4897254]),
 'MagZ': RawSignal([       nan, -0.3653959, -0.3641196, ..., -0.3035113, -0.3049648,
       -0.3069487]),
 'PullForce': Signal([ 0.50915384,  0.52900773,  0.50915384, ...,  0.53562569,
        0.54886161,  0.55547957]),
 'PushButton': RawSignal([  4.97649999e+00,   4.97649999e+00,   4.97633557e+00, ...,
         1.84891819e-03,   9.90515113e-03,  -4.23435969e-03]),
 'RearWheelRate': Signal([ 9.34469315,  9.37338086,  9.35698788, ...,  9.17803125,
        9.34742531,  9.14661139]),
 'RollAngle': Signal([ 0.43753619,  0.14035059,  0.12955648, ...,  1.47536364,
        1.28594489,  1.15082085]),
 'SteerAngle': Signal([  2.45804995,   2.41625603,   2.38881799, ...,  11.4506724 ,
        11.16284592,  10.83369557]),
 'SteerTubeTorque': Signal([-12.50940189, -12.17864079, -11.82813274, ..., -34.52228414,
       -34.12240913, -34.05823166]),
 'Temperature': RawSignal([      nan,  317.8788,  317.8788, ...,  317.9444,  317.9444,
        317.9445]),
 'ThreeVolts': RawSignal([ 3.14904532,  3.14871649,  3.14822325, ...,  3.14855208,
        3.1488809 ,  3.14871649])}

run.truncatedSignals

{'AccelerationX': Signal([ 2.47077461,  3.657561  ,  3.633692  , ...,  3.211428  ,
        3.133757  ,  2.965047  ]),
 'AccelerationY': Signal([ 0.32030702,  0.2808139 ,  0.376253  , ..., -1.591935  ,
       -1.481616  , -1.783708  ]),
 'AccelerationZ': Signal([-9.02043158, -9.272774  , -8.456081  , ..., -9.536515  ,
       -9.220658  , -9.00966   ]),
 'AccelerometerAccelerationX': Signal([-4.00748813, -4.00464501, -3.97744755, ..., -3.48347678,
       -3.46834316, -3.39476767]),
 'AccelerometerAccelerationY': Signal([ 9.30015633,  8.97047881,  8.38874647, ...,  9.09809172,
        8.86553338,  9.20775755]),
 'AccelerometerAccelerationZ': Signal([-1.84970437, -1.80003605, -1.99404212, ...,  0.01093398,
        0.72433224,  0.86868216]),
 'AngularRateX': Signal([-0.09674107, -0.05633209, -0.05477387, ..., -0.4765857 ,
       -0.4696014 , -0.4629069 ]),
 'AngularRateY': Signal([ 0.05289091,  0.01581983,  0.01266712, ...,  0.03231768,
        0.02531269,  0.02697365]),
 'AngularRateZ': Signal([ 0.02313052,  0.01341662,  0.01959944, ...,  0.3932629 ,
        0.3801239 ,  0.3574072 ]),
 'FiveVolts': Signal([ 4.9801364 ,  4.98018963,  4.98011708, ...,  4.9793096 ,
        4.97944485,  4.98004454]),
 'ForkRate': Signal([ 0.2198569 ,  0.21641052,  0.21563719, ..., -0.48229508,
       -0.55071296, -0.60132438]),
 'MagX': Signal([ 1.09781256,  1.10222   ,  1.100719  , ...,  1.220719  ,
        1.222653  ,  1.218221  ]),
 'MagY': Signal([ 0.58482581,  0.5896677 ,  0.5897546 , ...,  0.559401  ,
        0.5539194 ,  0.5525444 ]),
 'MagZ': Signal([-0.36923584, -0.3653959 , -0.3641196 , ..., -0.3147601 ,
       -0.3151211 , -0.3126837 ]),
 'PullForce': Signal([ 0.52238977,  0.51869193,  0.51946964, ...,  0.54146593,
        0.54302137,  0.55255945]),
 'PushButton': Signal([  4.97620506e+00,   4.97630167e+00,   4.97638880e+00, ...,
         1.05091372e-03,   6.35040393e-03,   2.00458434e-03]),
 'RearWheelRate': Signal([ 9.55659323,  9.44547727,  9.4245589 , ...,  9.34198529,
        9.27268156,  9.2352189 ]),
 'RollAngle': Signal([ 0.1222388 ,  0.11095325,  0.10729716, ...,  1.48238981,
        1.36952437,  1.21044323]),
 'SteerAngle': Signal([  0.97649343,   1.05862673,   1.12049643, ...,  11.58701244,
        11.28984701,  10.97893048]),
 'SteerTubeTorque': Signal([  7.34323336,   7.32655139,   7.11486284, ..., -34.6812994 ,
       -34.29885072, -34.08654945]),
 'Temperature': Signal([ 317.8788,  317.8788,  317.8788, ...,  317.9431,  317.9431,
        317.9432]),
 'ThreeVolts': Signal([ 3.14897277,  3.14913718,  3.14875039, ...,  3.14847953,
        3.14873581,  3.14878903])}

run.computedSignals

{'FiveVolts': Signal([ 4.9801364 ,  4.98018963,  4.98011708, ...,  4.9793096 ,
        4.97944485,  4.98004454]),
 'ForwardSpeed': Signal([-3.17783509, -3.14088592, -3.13392997, ..., -3.10647194,
       -3.08342651, -3.07096912]),
 'PitchRate': Signal([ 0.05277959,  0.01576109,  0.01260023, ...,  0.01879908,
        0.01317432,  0.01674585]),
 'PullForce': Signal([ 2.32370545,  2.30725664,  2.3107161 , ...,  2.40856046,
        2.41547939,  2.4579069 ]),
 'PushButton': Signal([  4.97620506e+00,   4.97630167e+00,   4.97638880e+00, ...,
         1.05091372e-03,   6.35040393e-03,   2.00458434e-03]),
 'RearWheelRate': Signal([ 9.55659323,  9.44547727,  9.4245589 , ...,  9.34198529,
        9.27268156,  9.2352189 ]),
 'RollAngle': Signal([ 0.00213347,  0.0019365 ,  0.00187269, ...,  0.02587258,
        0.02390271,  0.02112622]),
 'RollRate': Signal([-0.08468524, -0.04932831, -0.0459178 , ..., -0.32999739,
       -0.32746391, -0.32819573]),
 'SteerAngle': Signal([ 0.01704303,  0.01847652,  0.01955635, ...,  0.20223152,
        0.197045  ,  0.19161849]),
 'SteerRate': Signal([ 0.19672639,  0.2029939 ,  0.19603775, ..., -0.87555798,
       -0.93083686, -0.95873158]),
 'SteerTorque': Signal([ 0.61580208,  0.60018495,  0.62660908, ..., -4.88180098,
       -4.64762224, -4.40787578]),
 'ThreeVolts': Signal([ 3.14897277,  3.14913718,  3.14875039, ...,  3.14847953,
        3.14873581,  3.14878903]),
 'YawRate': Signal([ 0.05217546,  0.03033202,  0.03571939, ...,  0.52256494,
        0.50787241,  0.4841641 ])}

run.taskSignals

{'FiveVolts': Signal([ 4.98017031,  4.98004454,  4.98011708, ...,  4.98009776,
        4.9793096 ,  4.97944485]),
 'ForwardSpeed': Signal([-3.08804774, -3.07821709, -3.09392892, ..., -3.14504474,
       -3.10647194, -3.08342651]),
 'LateralFrontContact': Signal([ 0.04783112,  0.04799592,  0.04817749, ...,  0.16907404,
        0.17046786,  0.17169972]),
 'LateralRearContact': Signal([ 0.05313131,  0.05314765,  0.05316148, ...,  0.05693038,
        0.05516465,  0.05337492]),
 'LateralRearContactRate': Signal([ 0.0147595 ,  0.01421775,  0.01375049, ..., -0.36196549,
       -0.36609941, -0.37157731]),
 'LongitudinalFrontContact': Signal([  1.06377034,   1.04834531,   1.03291666, ..., -89.22539833,
       -89.24148688, -89.25750375]),
 'LongitudinalRearContact': Signal([  0.00000000e+00,  -1.54154918e-02,  -3.08456983e-02, ...,
        -9.02882936e+01,  -9.03038160e+01,  -9.03191805e+01]),
 'LongitudinalRearContactRate': Signal([-3.08801247, -3.07818425, -3.09389836, ..., -3.12414587,
       -3.084824  , -3.06095562]),
 'PitchRate': Signal([ 0.0025994 ,  0.00428815,  0.01215716, ...,  0.017302  ,
        0.01879908,  0.01317432]),
 'PullForce': Signal([ 2.39557112,  2.39903058,  2.41201993, ...,  2.45444743,
        2.40856046,  2.41547939]),
 'PushButton': Signal([-0.01183782,  0.00048039,  0.00078989, ..., -0.00503783,
        0.00105091,  0.0063504 ]),
 'RearWheelRate': Signal([ 9.28657885,  9.25701546,  9.30426511, ...,  9.45798395,
        9.34198529,  9.27268156]),
 'RollAngle': Signal([-0.00360557, -0.01114409, -0.00998905, ...,  0.0262769 ,
        0.02587258,  0.02390271]),
 'RollRate': Signal([-0.01591406, -0.00859876, -0.0129135 , ..., -0.32698925,
       -0.32999739, -0.32746391]),
 'SteerAngle': Signal([ 0.00378915,  0.00418438,  0.00449817, ...,  0.2074194 ,
        0.20223152,  0.197045  ]),
 'SteerRate': Signal([ 0.08357694,  0.08925328,  0.08380447, ..., -0.83027144,
       -0.87555798, -0.93083686]),
 'SteerTorque': Signal([ 0.31410154,  0.23392595,  0.04609619, ..., -4.98081321,
       -4.88180098, -4.64762224]),
 'ThreeVolts': Signal([ 3.14913718,  3.14939346,  3.14898735, ...,  3.14846021,
        3.14847953,  3.14873581]),
 'YawAngle': Signal([-0.00477958, -0.00461884, -0.00444436, ...,  0.11534634,
        0.11812506,  0.12080151]),
 'YawRate': Signal([ 0.01667843,  0.01786812,  0.02217558, ...,  0.54878437,
        0.52256494,  0.50787241])}

run.plot?

In [15]:

run.plot('RollRate')

Out [15]:

The goal of a conference is not only to present and share the knowledge that SOFIE creates but also to see what is happening in the rest of the research and industrial world within our field. Thus a brief summary of the conference is now given, with some links to some interesting papers from the conference. The analysis is divided into three areas, a subset of the technical papers and systems engineering papers observed and a summary of trends and information learned at the conference.

Technical analysis

Situation Recognition for Hospital Robots, Tamas Szeci and Md. Kabir Al Mamum.

An interesting presentation about the IWARD hospital robot, specifically the ability for the robot to determine if a patient is lying or standing. A number of different techniques were used during the image processing to recognize the patient. OpenCV used for much of the processing.

Detection of sub-surface defects: Mechatronic innovation at voestalpine Stahl Gmbh, Rudol Cihal et al.

A Magnetic flux leakage sensor (GMR sensor) was used to find faults in steel plates of 0.5mm in size.

Developing an Anatomically Based Robotic Hand, Kenneth Rotter and Jacobus van Schalkwyk.

This paper described an impressive robotic project that a student performed for a bachelors thesis. A prototype hand was created from scratch by the student and results created. They used rapid development techniques such as 3D printing and control systems created using Anduino, specifically the Seeduino.

Kinematic Analysis of Cooperative Underwater Intervention Systems Based on Screw Theory, Carlos R. Rocha
Presented a paper about using screw theory in underwater vehicles, with an open source python framework for working with the screw theory.

Systems engineering analysis

The big buzz term for the conference was ‘model-based systems engineering’. This is defined as: ‘the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases’ (from Tomiyama’s paper). Thus everyone was presenting methods to achieve this, also how to move many engineering steps forward in the development process using ‘front loading’ (the other buzz term). A few notes on some of the presentations is now given:

Architecture-Centric Model-Based Product Development, Tetsuo Tomiyama

The keynote paper from Professor Tomiyama was well presented and interesting. The main crux that I received from the presentation is the fact that problems occur in multi-disciplinary projects and they are universal, I even notice them in my small project. He lists and describes these problems as: communication, decomposition, integration, traceability, interface, iteration and change of management problems.

He proposes that the higher-level view to represent the structure and requirements of the project must be built into the model-based engineering process (ie. that’s why it is architecture centric).

Mechatronical Maturity in Machine and Plant Construction – Needs and Benefits, Rainer Stetter
This presentation from industry was controversial and interesting. Dr Stetter made a huge point throughout his presentation that mechatronic engineering in essence has changed enormously. Traditionally it was mechanical and electronic (mechatronic) engineering, but now it is more realistically software engineering as well as mechatronic. He believes that the software engineering is inherently not recognised sufficiently in many mechatronic companies, while it has an equal (and arguably) a larger role in modern day multi-disciplinary projects in terms of complexity and importance.

He believes the processes to get these domains to work together is still under construction and big changes need to happen within in companies to get the distribution of the importance of the different systems realistically balanced. They are also working on  new research with Eureka to investigate Agile software development method’s use in mechatronic companies. More infomation.

From model to reality – successful implementation of a robotic solution at a continuous casting machine, Pfeil, Simon.

Another interesting presentation from industry, Siemans VAI, which creates huge steel plants. They performed extensive ‘front-loading’ in the design of a robot that works in dangerous conditions close to the casting machine. Full virtual design of the robot was created, then tested using a test bench and finally installed in the production for use, with no time for on-site testing. It was created on time and performed correctly from time zero on-site and shows how the engineering process is changing dramatically.

Towards Seamless Model-Based Design of Complex Systems with Early Consideration of Automation Software, Jan Christoph Wehrstedt et al.

Showed yet another complex tool to do the modeling process during product development using concepts from software engineering in mechatronic projects (such as state vector diagrams). It was very interesting, but one wonders where it will go. There seems to be a multitude of competing systems designed for engineers. The speaker was asked, well what happens when you show this tool to the software engineers in the team. The result is, we have our tools, they work, so please leave us alone to get our job done.

Model based design brought to practice via Virtual Components for energy modeling, G. Bianchi et al.

The author proposed that component manufacturers should start creating virtual components of their devices instead or in addition to data sheets. That way engineers can start using these components in their modeling systems which sounds like a great advance in current practice, but how to actually get that done?

Overcoming current mechatronic design challenges: a
discussion, Ahsan Qamar
The author proposed a need for a system to act as an adaptation layer between all the different type of modelling systems. A tool to make it possible to connect SysML to Modelica (for example) to the other multitude of tools available. It will provide a way to perform: version control and integration testing amongst other things.

Mechatronic Design – Success Strategy for the Challenges in the Steel Industry, Andreas Flick

This talk from the CTO of Siemens VAI was interesting. He described how requirement engineering is one of the most important things in the development process. He described the four different strategies that govern their design processes for modern steel plants.

1. Complete redesigns, the most inefficient for the company but possibly the best for the client.
2. Identical designs, they use the same system in different plants.
3. Type designs, they have a few different products of different performance values that they use to build custom plants.
4. Parametric design, the can scale and change parts for different requirements.

He also stressed how front-loading of the design process is very important to try minimize the amount of integration problems and re-design cycles.

Credibility of Simulation Results – A Philosophical Perspective on Virtual Manufacturing, Leo J de Vin
An interesting presentation about simulation results and how to interpret them. He stated that you should never trust simulation results until you have a reason to believe them.

He also stressed that the person who tests the results should not be the person who creates the simulation. This is common practice in software engineering but should also be applied to mechatronic engineering. Testing should be separate to take away bias’s in the test process.

Semantic Top-Down Modeling of Mechatronics Systems for Sustainable Product Data and Lifecycle Management, Ossi Nykänen
Provided an in depth description of another tool to help in the top-down system design process. Mathematically based with checks in place to make sure functions are fulfill requirements. They are attempting to digitize the entire design process, which is very important in Finland.
These tools are hoping to improve communicatoin and collaboration which are some of the core problems in large multi-disciplinary projects.

Summary

So what do I bring from it. Everyone mentions the same issues that occur in the design process. Most importantly how to take implicit design decision and make them explicit to help people understand system that is being created.

In this conference alone there were several different digital tools presented to help digitize the design process. Some of them well known, such as Modelica and SysML, and some new concepts from full blown software packages, to small XML based research projects.

The tools are multiplying and this is on top of the already detail based design tools such as FEM, CAD and simulation environments. The core problem that I see is how to connect all these tools together to make a seamless design process, but also make a compelling connection that is easy to understand and use to enable all the different disciplines to understand and convert to such a system.

This is an incredibly hard task, and most likely not ever realizable. Thus I still believe the design processes are some of the most important ways to achieve this, be this formal top-down design or agile development strategies. The tools should develop to help in this process and not make it harder to understand and implement.

So in many ways, I believe that software engineering can create a useful way to achieve this. Continuous integration, for example, has been around for a long time. With systems such as Jenkins, Git and automatic documentation a core part of many development teams. These kind of tools need to spread across the whole design process, especially to the architecture level design phase. And they need to be easy to use!

My presentation at the conference dealt with the use of Agile development techniques in a multi-disciplinary project to help in the process of the spread of these principles across all domains.

The presentation was in the Mini-Symposium about Mechatronic Design, which as I mentioned previously is very structured formal methods. The full paper for the conference is shown below and is available for download.

Download (PDF, 428KB)

Footnote

Unfortunately like the whole of the conference, this topic was only a footnote. The mechatronics 2012 conference had many people from academia and industry, big and small. These are the people that can make a difference to the world.

I heard the terms social responsibility and sustainability maybe once or twice during the whole conference. The world has many problems and the drive to improve it needs to come from somewhere, and big industry is a core driver behind change in the world, and unfortunately their does not seem to be a serious driver for change coming from this area if I take this conference as an example.

But, for once, I can say that the software world could learn from the mechatronics world. Mechatronic companies know how to optimize production processes for efficiency, these concepts need to be applied to massive server farms that drive the information age, which are so in-efficient that some of them run on an average CPU efficiency of 12% and consume enough energy to power a small to medium sized town. Thus to close this article, lets hope the Footnote becomes part of the main crux of such a conference, before its too late.

One can easily be caught up in all the media propaganda, and crazy stunts, but we were there with a goal. To find out what is happening with the latest bio-mechanical cycling sensors and e-bike solutions to tie in with our research into the stability of cycling behaviour.

Thus we will show you the following categories in this post:

1. E-bike innovations.
2. Bio-mechanics, bicycle analysis and ergonomics.
3. Smartphone integration.
4. Other innovations and interesting things.

So lets get down to the nitty gritty.

E-bike innovations

The e-bike market is still growing if you look at the number of companies and solutions available. The old players are still there, such as Bosch, Bionx and TransX. But now some of the new big players are entering the marker. Most notably AEG presented their system with a new center mounted motor.

The enclosed bottom bracket system can be added to any bicycle. Unfortunately I was not able to get a ride on one of the bikes, but it looks clean and simple. Smart was also aggressively marketing their new heavily designed E-bike. But it was only a nice looking packaged Bionx system. Their promotional video:

As you can see the big names are jumping into this growing market, making it harder for the small companies to compete.

Speaking to most of the manufacturers most of the control systems for the bikes either use a magnetic reed sensor at the crank or a torque sensor, which still leads to some jerky behaviour amongst many of the rides.

The one outstanding, but sadly lacking the marketing capacity of the big players, was the Safeway bicycle system designed by an innovative French company.

The e-bike that uses a patented system of measuring the difference between rotational position of the front cog and rear sprockets and then adjusts the power that the motor delivers to the bicycle. With 96 measurements positions and an obviously well designed control system this bicycle gave an incredible riding experience, unfortunately the design is not eye-catching and it has a great possibility of falling into obscurity.

In stark contrast, the overly designed and award winning Go-Cycle bicycle did wow the industry.

I also managed to take one of these for a ride and it performed well, amongst the best, except that my back got sprayed with mud and water in the rain, because the mud guard was not big enough. This on a £3000 plus style icon.

Bio-mechanics, bicycle analysis and ergonomics.

I have an interest in finding a good pedaling power meter, because we would like to measure this information as part of our bicycle stability test bench. Here is a brief synopsis of what I saw:

QuarQ

The quarq team were at Eurobike again. They now have added an accelerometer to the crank and use it to estimate the left and right leg power. The system is fully ANT+ compliant, and should fit a huge variety of bicycles. The price tag is still high at over €1500. They say that it has +-2% accuracy.

Power2Max

The similar system from the small German manufacturer has the same specs (now also with an accelerometer) but comes in a lot cheaper. Both systems only update the power output once a second so are probably not quick enough for proper bio-mechanical research.

Shimano

The Shimano guru’s from Japan provided the benchmark system. A test bench with accurate 3D measurements on both the left and right cranks. Of course this is not a consumer product and only used to help in the development of better shoes, pedals and cranks for the Shimano R&D team.

Note the beautiful Panasonic toughbook driving the sensor logic and screen… I had one of those once, superb Japanese quality.

The Polar stall was also showcasing their Polar Kea power measurement system which they say also has +-2% accuracy. Unfortunately Polar uses a propriety closed-source communication protocol, thus one has to buy their display unit as well. One could get the information from the Polar display unit, but the other systems seem more appealing for research because one can get the raw measurement data instead.

The much anticipated Garmin Vector pedal based power measurement system does not even appear to be making it to production. There was nothing shown at the Garmin store about this product. Last year it was the most taunted product, this year its not even there.

BodyScanningCRM.de

The BodyScanningCRM team were providing a system that can measure the dimensions of your body and then through propriety software determine the correct bicycle setup for you using this data. They also have a research wing that can measure the power in each foot using the TEKSCAN foot scanning system. There is no dynamic setup of the bicycling occurring or easy evaluation of bicycle behaviour.

gebioMized

This company provides advanced bio-mechanical services for the rider. Varying from foot pressure output and seat pressure output to bicycle setup and ergonomic evaluation. Their systems are expensive but are a good solution for research projects where you need high frequency measurements and accurate results. Definitely not for the consumer market though, there clients are mostly research institutes, universities and professional cycling teams.

ERGON

Ergon creates a range of ergonomic products for bicycles. The PC2 pedal looks very interesting and it would be good to see how it performs in our research about elderly cyclists.

Smartphone innovation

I am quite surprised how slow this is happening. The new e-bikes have batteries, power and loads of juicy data that the consumer would like to see. Very few companies are providing systems to access this information from the Smartphone, let alone using the smartphone to control the bicycle.

Garmin seems to be seeing the potential of the smartphone and the bicycle with a Smartphone App.

A good step in the right direction. A small group from www.sitael.com were the only people I saw who were able to access information from the ebike with their Smartphone. The information was accessed via Bluetooth and they were expecting users to start using the system by creating an ‘Eco’ app to make it fun to use the software. This would then allow the engineers to get information about the usage of the system. But its a closed system connected to a limited amount of E-bike controller manufacturers, so I do not expect it to be much of a success.

The Go-Cycle system had a method to access information about the e-bike from an Android smartphone, but this is only used for diagnostics and servicing of the bicycle. The bicycle also logs telemetry data about the bicycle’s use which the technicians can then use to help repair the bicycle or also see abuse of the bicycle. (The power control system for the bicycle can also be tweaked for different laws in different countries. In the EU a e-bike can only go to 25km/h and one has to pedal to get assistance. In the UK the bike can act like a scooter and just give power up to 25km/h. It was even possible to put the Go-Cycle bike in unlimited mode where you can go 45km/h plus for about 10 minutes which is illegal on the streets of Europe and the UK!)

Even the smart e-bike is not smart enough to connect the smart phone up to the control system of the bike. They provide a method to mount the smartphone on the handle bar, but nothing else.

Other innovations and interesting things.

Last year, one of the most interesting things I saw was the Braid industrial process to create Carbon fibre frames. Unfortunately I did not see this here again. This year I think the most outstanding thing I saw was the Pinion gearbox system that sits in the bottom bracket of the bicycle. From my experience of riding rear wheel driven, bottom bracket driven and front wheel driven E-bikes, I find the bottom bracket mounted systems to have the best handling due to the good position of the center of mass, thus I expect this bicycle to have a great ride.

This exceptional gearbox with a huge gear range reportably gives excellent reliability that should rival the renowned Rohloff system. Lets see what happens with this company, at the moment they are unable to fulfill their orders due to high demand.

The trend that seems to be growing more popular is the belt drive system. Last year it was only found on high end tourers but now the belt drives are found on city bikes to fixies and childrens bikes. They offer an oil free reliable transmission in all conditions. Combined with the Pinion or Rohloff and you can ride around the world with hopefully zero transmission problems.

Its also great to see simple solutions to age old problems. Where do you put your delicate bicycle when its raining through the dark winters? Why not display it on your wall with this simple bicycle mount from cycloc.

I am becoming a Titanium bicycle junkie after purchasing an old Basso race bike in Italy. Moots from the US make some beautiful, simple bicycles with a sand-blasted matte titanium finish. Then of course I am a cycle touring fan and Surley provides another minimalist method to achieve this, with small bags tucked away on different parts of the frame:

Then the chopper, modification buzz is starting to filter through to the bicycle world. This downhill Santa Cruz bike has been modfied with a set of huge Surley wheels, to create a downhilling demon that can also ride on the beach.

Lastly there is one thing I do not understand about modern cycling. I have spent hours, months and years on bicycles in my life, and the seat has never been an issue. Nowadays, seats like this,

have become the rage. And if they are not bare bones like this then they have a minimal amount of padding. On the beautiful Titanium bike I bought, there was one of these saddles, and it was incredibly uncomfortable. I then bought my self a nice plush ladies saddle and now have no issues.

I wondered if this is just me and asked Lloyd Thomas from cyclefit.de about this phenomenon. He says that the professionals do not even use these light saddles. Its just a weird masochist market driven trend. So do not feel obligated to get one of these painful experiences, go out and buy a plush saddle and enjoy the ride much more.

Noordhollands Dagblad, 20 juni 2012 woensdag

Wetenschappers van de Universiteit Twente (UT) starten een onderzoek naar elektrisch vervoer in Nederland. Met het zogenoemde Mobility Lab Twente brengt de UT de mogelijkheden van elektrisch vervoer binnen steden en op regionaal niveau in beeld. De gebruiker staat in het onderzoek centraal

Universiteit Twente is de eerste universiteit in Nederland waarvoor de elektrische voertuigen beschikbaar zijn gesteld. Munsterhuis, Ebretti en Renault geven de universiteit de uitgelezen mogelijkheid om de voertuigen in een werkelijke omgeving (de campus en de Twentse regio) te onderzoeken. Marc Beusenberg, coördinator van het lab, vertelt: “Nu we de voertuigen tot onze beschikking hebben, krijgen we een veel beter inzicht in hoe gebruikers reageren op de voertuigen en hoe ze ermee omgaan. Gaan ze ermee naar hun werk of gaan ze ook een weekendje weg?”

De Twentse aanpak is bijzonder. In plaats van uit te gaan van de mogelijkheden van het product, stellen de wetenschappers  de behoefte van de gebruikers centraal. Beusenberg: “Voor ons is elektrisch rijden iets anders dan het ombouwen van een bestaande auto. Wij gaan uit van het vervoer van mensen en goederen. Hoe kan dat het beste en wat heb je daarvoor nodig. Door deze zienswijze kunnen nieuwe voertuigen ontstaan. Een voorbeeld hiervan is de Renault Twizy, die officieel geregistreerdstaat als eenvierwielige driewielig motorvoertuig.”

Uiteenlopende onderzoeken vallen onder het Mobility Lab Twente.Zo gaat een belangrijke onderzoekslijn over de mobiliteit van ouderen. Hierbij wordt gekeken naar hoe elektrisch rijden het beste kan aansluiten bij de behoeften van ouderen. Belangrijk hierbij is dat integraal gekeken wordt naar dienstverlening en het beste bijpassende vervoersconcept. In een proeftuin in Friesland wordt al een elektrisch autootje ingezet met een lage instap,genoeg ruimte en een efficiënt energieverbruik van belang. Een andere grote onderzoekslijn richt zich op de stabiliteit van elektrische fietsen.

Weer een ander deel van het onderzoek richt zich op de perceptie van elektrisch rijden. Er zijn veel vooroordelen, zoals: het vervoer is niet snel, je kunt nergens opladen, het opladen duurt lang. Wij proberen deze percepties te beïnvloeden. Er zijn al auto’s die je binnen 3,5 uur volledig kunt opladen zonder speciale oplaadvoorziening, dus door hem gewoon thuis in het stopcontact te doen. Een belangrijk aandachtsgebied zijn de voertuigen die ideaal geschikt zijn voor redelijk korte ritjes in de stad of op provinciale wegen. 80% van onze bewegingen vindt plaats op deze wegen waar je tussen de 30 en 80 km per uur mag rijden. Wij noemen dit ook wel “normaal verkeer”, als tussengroep tussen het bekende snelverkeer en langzaam verkeer.

Indien u meer informatie wilt over de vijf elektrische voertuigen, of over hoe deze ingezet worden voor onderzoek kunt u contact opnemen met Mobility Lab Twente via:

info@mobilitylabtwente.nl

foto: Gijs van Ouwerkerk

foto: Gijs van Ouwerkerk

foto: Gijs van Ouwerkerk

foto: Gijs van Ouwerkerk

foto: Gijs van Ouwerkerk

The article can be found here: http://science.kqed.org/quest/video/the-science-of-riding-a-bicycle/