||Péter Fankhauser 27.02.2017
Locomotion Concepts
Spring 2017
Péter Fankhauser, Marco HutterRoland Siegwart, Margarita Chli, Martin Rufli
1Autonomous Systems Lab
Locomotion Concepts Principles Found in Nature
§ Nature came up with a multitude of locomotion concepts
§ Concepts found in nature§ Difficult to imitate technically§ Do not employ wheels§ Sometimes imitate wheels (bipedal
walking)
2||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Locomotion Concepts Principles Found in Nature
§ Nature came up with a multitude of locomotion concepts
§ Concepts found in nature§ Difficult to imitate technically§ Do not employ wheels§ Sometimes imitate wheels (bipedal
walking)
§ Most technical systems today use wheels or caterpillars
2||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Locomotion Concepts Walking vs. Rolling
3||Péter Fankhauser 27.02.2017Autonomous Systems Lab
§ Legged systems can overcome many obstacles
Locomotion Concepts Walking vs. Rolling
3||Péter Fankhauser 27.02.2017Autonomous Systems Lab
§ Legged systems can overcome many obstacles
§ But it is hard to achieve this:§ Floating base (vs. fixed-base)§ Many DOFs must be controlled in a
coordinated way§ The robot must interact with (uncertain)
terrain
History of Legged RoboticsWalking Mechanism – First Patents
4||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Davis, 1878Rygg, 1893
History of Legged RoboticsWalking Mechanisms
5||Péter Fankhauser 27.02.2017Autonomous Systems Lab
General Electric’s WalkingTruck, ca. 1965
Shigley's WalkingMachines, 1957–60
History of Legged RoboticsWalking Mechanisms
6||Péter Fankhauser 27.02.2017Autonomous Systems Lab
OSU ASV (Adaptive Suspension Vehicle), 1984
History of Legged RoboticsLarge Scale Legged Locomotion and Manipulation
7||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Menzi Muck
History of Legged Robotics Phony Pony, GE Hardiman and many more…
8||Péter Fankhauser 27.02.2017Autonomous Systems Lab
§ More on http://cyberneticzoo.com § steam-actuated humans § mechanical elephants § …
History of Legged Robotics Humanoid Robots After 2000
9||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Honda Asimo Toyota Humanoid
History of Legged Robotics Humanoid Robots After 2000
10||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Fukushima, 2011 DARPA Robotics Challenge, 2012
DARPA Robotics Challenge … and a thing we learned after the DRC Finals
12||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Walking is still difficult
Modeling Legged Locomotion Inverted Pendulum
13||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Walking can be represented by an inverted pendulum
Modeling Legged Locomotion Passive Dynamic Walker
14||Péter Fankhauser 27.02.2017Autonomous Systems Lab
§ Exploit the inverted pendulum character in passive dynamic walkers
§ Forward falling combined with passive leg swing
§ Energetically very efficient
Modeling Legged Locomotion Passive Dynamic Walker
15||Péter Fankhauser 27.02.2017Autonomous Systems Lab
§ Add little actuation to walk on flat ground
Cornell Ranger, 2011 Total distance 65.24 km Total time 30:49:02 Power 16.0 WCOT 0.28
Modeling Legged Locomotion Cost of Transport (COT)
16||Péter Fankhauser 27.02.2017Autonomous Systems Lab
usedE m g h hCOTm g d m g d d
× ×= = =
× × × ×
Modeling Legged Locomotion Energy Expenditure for Different Gaits
17||Péter Fankhauser 27.02.2017Autonomous Systems Lab
2331Mechanics and energetics of horse locomotion
analyse the three-dimensional data and to calculate thebiomechanical variables for 209 strides.
Metabolic measurementsStandard open-circuit respirometry was used to determine
the metabolic power requirements. The expired gas, flowingthrough a mask firmly positioned around the nostrils (BRDL,Birmingham, UK), was collected in Douglas bags, andventilation rate was assessed by using a chronometer and a drygas meter (Collins, USA). O2 and CO2 partial pressures weremeasured using gas analysers (Binos 1, Leybold-Heraeus andOxynos 100, Rosemount, Germany). After converting valuesto STPD (laboratory air temperature was always within the range14–16 °C), the rate of O2 uptake was calculated. Values for thestanding horse were subtracted from the steady-state values,reached after approximately 4 min of exercise, to obtain the netmetabolic work rate during each trial. To express the metabolicwork (C) in the same units as mechanical work (J kg−1 m−1),we divided the net metabolic work rate by the speed (m s−1)and assumed that 1 ml of O2 is equivalent to 20.1 J (for a meanrespiratory quotient of approximately 0.95) to convertmetabolic into mechanical units.
The efficiency of locomotion, the ratio of mechanical tometabolic work, was calculated by dividing WTOT by C.
ResultsThe rate of energy consumption of standing horses was
1.94±0.48 ml O2 kg−1 min−1 (mean ± S.D., N=4). Fig. 2 shows,for each gait, the metabolic cost at different speeds. Becauseof technical problems related to air leakage through the mask,only a subset of all the measurements was considered. Themain criteria for rejecting some measurements were the lackof an increase in ventilation with increasing speed (within eachgait) and a respiratory quotient exceeding 1.0.
Typical changes in mechanical energy for walking, trottingand galloping are shown in Fig. 3. By summing all the energyincreases in ET over the stride period and expressing the resultper unit mass and distance, we obtained WEXT. Fig. 4 showsthese values (upper curve) and WINT (lower curve).
Table 1 reports the regression coefficients for parabolicregressions for the main variables discussed in the paper.Second-order regression was used for descriptive purposes,since it can fit most linear and non-linear data without furtherincreasing the complexity of the analysis.
DiscussionThe energetics of horses locomotion shows some
optimisation phenomena (Alexander, 1989). At each gait, aspeed exists at which C is minimal, as originally discovered ina study on small horses (Hoyt and Taylor, 1981). In addition,minima in C are comparable at the different gaits, showing anoverall constancy of the metabolic energy spent per unitdistance travelled, despite the difference in speed, although thevalue of C during walking is lower than that reported by Hoyt
and Taylor (1981). This is because the subtraction of basal(standing) metabolic rate from the metabolic measurements, asapplied here, has a greater effect at low speeds than at higherspeeds (for a review of different techniques for calculating C,see Full, 1991). While exceeding (by approximately 30 %) theallometric equation obtained for mammalian and bird specieswith masses ranging from 0.01 to 254 kg (Taylor et al., 1982),the present values of C are comparable with previousmeasurements on horses (Farley and Taylor, 1991; Eaton et al.,1995; Potard et al., 1998; Langsetmo et al., 1997; Butler et al.,1993; Wagner et al., 1989).
Mechanical workThe mechanical external work (Fig. 4, upper curves) during
walking and trotting resemble the values for humans atcorresponding gaits (Cavagna et al., 1976). Fig. 5 shows thethree-dimensional range of movement of the centre of mass ofthe horse during single strides at the different gaits and speeds.It is surprising how the lateral displacement (dz) for a 500 kghorse drops from 50 mm (slow walking) to only a fewmillimetres during fast galloping and how the verticaldisplacement (dy) more than doubles when passing fromtrotting to galloping.
As it is calculated (Cavagna et al., 1976), WEXT representsa good approximation of the mechanical work done by themuscles only for non-bouncing gaits such as walking. During
Fig. 2. Relationship between the metabolic energy cost (per unitmass and distance) and speed during walking (W), running (R) andgalloping (G). The thick curves refer to parabolic fits of theexperimental data (the regression coefficients are given in Table 1),while the thin curves represent the parabolic fits of the data of Hoytand Taylor (1981) for a 140 kg horse. Filled circles shows meangalloping cost and bars refer to standard deviations (S.D.) of datataken from the literature (Eaton et al., 1995; Potard et al., 1998;Langsetmo et al., 1997; Butler et al., 1993; Wagner et al., 1989).Open circles represent data for walking, filled squares for trottingand open diamonds for galloping.
0
0.05
0.10
0.15
0.20
Met
abol
ic c
ost (
ml O
2 kg-
1 m-1
)
Speed (m s-1)
W
T G
4
2
3
1
0
�
Met
abol
ic c
ost (
J kg-
1 m-1
)
0 2 4 6 8 10 12
Metabolic cost of transportation (normalized for body mass) for different gaits of horsesMinetti, 1999
§ Each gait has a specific velocity that minimizes energy expenditure
§ Explains why animals and humans change gait for different speeds
Walking
Trotting Galloping
Modeling Legged Locomotion Dynamic vs. Static Locomotion
18||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Statically stable § Bodyweight supported by at least three legs§ Even if all joints ‘freeze’ instantaneously, the
robot will not fall§ Safe and slow
CoGCoG
Dynamically stable § The robot will fall if not continuously moving § Less than three legs can be in ground contact§ Fast and demanding for actuation and control
Modeling Legged Locomotion Dynamic Locomotion
19||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Modeling Legged Locomotion Dynamic Locomotion: Leg Structure
20||Péter Fankhauser 27.02.2017Autonomous Systems Lab
§ Leg during running is not an inverted pendulum
§ Spring loaded inverted pendulum (SLIP)
Static locomotion (IP) Dynamic Locomotion (SLIP)
Modeling Legged Locomotion Dynamic Locomotion: Leg Structure
20||Péter Fankhauser 27.02.2017Autonomous Systems Lab
§ Leg during running is not an inverted pendulum
§ Spring loaded inverted pendulum (SLIP)
§ are robust against collisions § can better handle uncertainties§ can temporarily store energy§ reduce peak power
[Alexander 1988, 1990, 2002, 2003]McGuigan & Wilson, 2003 – J. Exp. Bio.
Dynamic Locomotion SLIP Principles in Robotics
21||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Early Raibert hoppers (MIT Leg Lab), 1983§ Pneumatic piston§ Hydraulic leg “angle” orientation
Dynamic Locomotion From Raibert Hopper to Humanoids and Quadrupeds
22||Péter Fankhauser 27.02.2017Autonomous Systems Lab
§ Boston Dynamics§ Founded 1992§ Big Dog V1 (2005)§ Big Dog V2 (2008)
Dynamic Locomotion From Raibert Hopper to Humanoids and Quadrupeds
23||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Dynamic Locomotion From Raibert Hopper to Humanoids and Quadrupeds
24||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Series Elastic Actuators From Position to Force Controlled Systems
25||Péter Fankhauser 27.02.2017Autonomous Systems Lab
motor gear
position
link
spring linkmotor gear
position force
§ Kinematic, position control
§ Dynamic, force control
Series Elastic Actuators Efficient Walking and Running
26||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Series Elastic Actuators StarlETH – A Complaint Quadrupedal Robot
27||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Hutter, 2012 & Gehring, 2013
Series Elastic Actuators ANYdrive – A Robust and Force Controllable Robot Joint
28||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Absolute position sensing
Fully integrated
Programmablecontroller
High load bearing & hollow shaft
Ingress-protection IP66
Accurate position &torque control
ANYmal Combining Dynamic Motion Skills with Large Mobility
29||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Outdoor operation Water-proof & ruggedized design
Extreme mobility 360° rotation of all joints
Fast locomotion Dynamic gaits at 1 m/s
Modular payload 10 kg payload
Full autonomy Laser sensors for navigation
Long endurance 3 h operation
Safe and lightweight 30 kg and force-controlled
Hutter, 2016, Gehring, 2013, Fankhauser, 2014
ANYmal System Overview
30||Péter Fankhauser 27.02.2017Autonomous Systems Lab
Locomotion PC
12× ANYdrive IMU 4× Foot contact sensor
State estimation Legged odometry, ground est.
Locomotion Control Trotting, crawling, Free Gait
ROS interface
Inspection PC
Zoom camera 2× MicrophoneVisible & IR lightThermal camera
Visual inspection Gauges, levers
Thermal inspection
Audio inspection Pump & alarm sounds
Navigation PC
2× Laser rangesensor
2× Wide-anglecamera
Localization ICP, Rovio, Tango etc.
Terrain mapping Elevation mapping, traversability est.
Motion planning Navigation, foothold selection
Mission execution
Ethe
rnet
Wireless Router
Safety operator
WiFi
Radio
Operator PC
IndustrialWireless
Remote control UI Manual, supervised control
Visualizations Sensors, robot state, environment
Mission Mission creating and protocol
RT
Fankhauser, 2016
Samuel BachmannDario BellicosoMichael BloeschRemo DiethelmChristian GehringMark HoepflingerGabriel HottigerMarco HutterJemin HwangboLinus Isler
Péter [email protected]
Thank You
32||Péter Fankhauser 27.02.2017
Dominic JudRalf KaestnerBruno KaufmannPhilipp KrüsiAndreas LauberPhilipp LeemannKonrad MeyerRoland SiegwartVassilios TsounisMartin Wermelinger
github.com/ethz-aslgithub.com/leggedrobotics
Open-Source Software
Autonomous Systems Labwww.rsl.ethz.ch www.asl.ethz.ch
||Autonomous Systems Lab 28/Feb/2017 1
Introduction to the V-REP robot simulator
||Autonomous Systems Lab
§ Introduction of V-REP scene§ Model browser§ Scene hierarchy (LUA child script)
§ MATLAB remote APIs§ Obtaining robot pose and sensor
measurements and sending control inputs.
§ Material can be found from http://www.asl.ethz.ch/education/lectures/autonomous_mobile_robots/spring-2017.html
§ You can build your own algorithms based on this.
2
Exercise #1 (14:15-16:00, 28/Feb/2017) in CAB G11
28/Feb/2017
V-REP scene
Remote API