Less computationally intensive fuzzy logic (type-1)-based controllerfor humanoid push recovery

1. Less computationally intensive
fuzzy logic (type-1)-based
controller
for humanoid push recovery
IIIT-Allahabad
Reference paper: Vijay Bhaskar Semwal, Pavan Chakraborty, G.C.
Nandi, Less computationally intensive fuzzy logic (type-1) based
controller for humanoid push recovery, Robotics and
Autonomous Systems, Available online 16 September 2014.

2. Point to covers:
 Why Fuzzy?
 What is Humanoid Push Recovery?
 Why Bipedal?
 Scientific Investigation of Push recovery
 Fuzzy set, member ship function, rules
 Fuzzy Logic Controller
 Performance
 Result
 Conclusion
 References

3. Objective- Learning based model
 Developing a mathematical model of a bipedal robot for
push recovery is extremely difficult task due to:
inherently unstable architecture,
higher degree of nonlinearity and freedom
hybrid dynamics
 The objective of this study is to develop an intelligent
controller and to implement biologically inspired push
recovery for humanoid robots.
 The objective is to reduce the fuzzy rules and make the
fuzzy inference set less computationally intensive and
fast.
 Exploiting the advantages of easy trainability and high
generalizability
 introduced an intuitive fuzzy logic based learning

4. Why Is Fuzzy Logic?
 Fuzzy refers for uncertainties and imprecision.
 Fuzzy logic actually captures the fuzziness and
vagueness existing in the environment .
 Many value logic
 Fuzzy used to developed the more real and low
cost solution.
 The fuzzy inference system takes two crisp
values as inputs, fuzzified it, applied number of
rules ,and defuzzified the output to convert it into
a crisp value.

5. Fuzzy Inference system architecture
Fuzzifier
Rules
Defuzzifier
Inference
Crisp Input
x€X
Fuzzy Input Sets Fuzzy output Sets
Crisp Output

6. Push Recovery
Three strategy ( Ankle , Knee and Hip)
while F1, F2 and F3 are magnitudes of force
Push recovery [1] is the capability of any subject
to recover from applied external perturbation with
support of other limbs.

7. Why Bipedal?
 To enter in human like environment 4D (Dirty,
Dull, Difficult and Dangerous) .
 Work human similar environment without and
changes in structure i.e. unstructured terrain,
climbing of stair case, hazardous environment
and narrow terrain
 These type of system widely used in various real
time application like rescues operation, bomb
disposal, rehabilitation, mining, hospitality
industry etc .
 The human walk and push recovery is the
learning mechanism and it grows with age.

8. Motivation
 As on date no humanoid robots are commercially
available which can negotiate push in real time.
 However, if humanoid robots are to work in a
cluttered environment push is a very commonly
experienced phenomenon which we as human can
recover from where as humanoids can’t.
 In such cases, the robot could potentially damage
itself and its surroundings.
 Our motivation is a humanoid robot working in a
social environment should have some bounded push
recovery capability like us.
 It will make humanoids smart and robust since in real
life during working in a unstructured environment
some unexpected push may be experienced by the
robots.

9. Closed Loop controller

10. Fuzzy Logic based Closed Loop
controller

11. Proposed Hierarchical Fuzzy
Controller design for humanoid Push
Recovery
FIS2:
Fuzzy Set3:Reaction
Small {Roll, Pitch}
Average {Roll, Pitch}
Large {Roll, Pitch}
FIS1:
Fuzzy Set3:Reaction
Small {Roll, Pitch}
Average {Roll, Pitch}
Large {Roll, Pitch}
Fuzzy Set1-Force
{Small (0-5N), Average
(4-8N), Large (7-12N}
Fuzzy Set2-DoM
(Direction of Motion)
{Left, Right, Forward,
Backward}
Strategy Applied
{Ankle, Hip, Knee}
State (fall, non fall)

12. FIS1 and 2

13. Design
 The two inputs variables are Force and Direction of
Moment (DOM). The corresponding membership
function for above two set are following:
 Fuzzy Set1-The fuzzy value range for linguistic variable
Force:
 µForce=Small (x) ={0-5N}, µForce=Medium (x) ={4-9N}
µForce=Large (x) ={8-12N}.
 Fuzzy Set2-The fuzzy value range for linguistic variable
 DOM: µDoM=Left (x), µDoM=Right (x), µDoM=Forward
(x), µDoM=Backward (x)

14. Fuzzy Inference System 2(FIS2)
Design.
 The FIS 2 uses the output of FIS1 as input
linguistic variables.
 FIS2 has output is combination of force and
direction applied. Small {Roll, Pitch}, Average
{Roll, Pitch}, Large {Roll, Pitch}
 Fuzzy Set3: defines a linguistic variable Reaction
has values Small {Roll, Pitch} Average {Roll,
Pitch} Large {Roll, Pitch}.
 FIS2 have output value in term of whether the
robot will able to recover or not and which
strategy the robot will apply for recovery. The set
for FIS2 output Strategy Applied {Ankle, Hip,
Knee} And State {fall, non fall}.

15. Fuzzy Rule 1
 Rule 1: μaction=small roll
= max[µForce=Small(x),min[µDOM=Left(x),µDOM=Right(x)]]
 Rule 2: μaction=small pitch
= max[µForce=Small(x),min[µDOM=Forward(x),µDOM=Backwa
rd(x)]]
 Rule 3: μaction=large roll
= max[µForce=Large(x),min[µDOM=Left(x),µDOM=Right(x)]]
 Rule 4: μaction=large pitch = max[µForce= Large (x),
min[µDOM=Forward(x),µDOM=Backward(x)]]

16. Fuzzy Rule2
 Rule 5: μreaction=ankle strategy, not falling = max [µAction=small
roll(x),µAction=small pitch(x)]-(17)
 Rule 6:μreaction=knee strategy, not falling = max[min[µAction=average
roll(x),µAction=small pitch(x)],min[µAction=average roll(x),
µAction=average pitch (x)]]-(18)
 Rule 7: μreaction=hip strategy, not falling = max [min[µAction=small
roll(x),µAction=large pitch(x)],min[µAction=large roll(x), µAction=small pitch
(x)],min[µAction=average roll(x), µAction=average pitch (x)]]-(19)
 Rule 8: μreaction= falling F/B= max[µAction=large roll(x),
µAction=largesmall pitch (x)]-(20)
 Rule 9: μreaction= falling L/R= max[µAction=small roll(x), µAction=small
pitch (x)]- (21)

17. Experiment al Setup for Humanoid Push
recovery – Frontal /Backward Plane
Subject in indigenous developed HMCD
wearable suit

18. Scientific Investigation Of Humanoid Push
Recovery

19. Ideal gait curve of different Joints
0 20 40 60 80 100 120 140 160 180 200
-10
0
10
20
30
40
Gait Cycle
HIP(degree)
0 20 40 60 80 100 120 140 160 180 200 220
-60
-40
-20
0
Gait Cycle
Knee(Degree)
0 20 40 60 80 100 120 140 160 180 200 220
-15
-10
-5
0
5
10
15
Gait Cycle
Ankle(degree)
(a) hip, (b) knee (c) ankle

20. Results
Observed Leg Joint Curve for Right and Left Leg of Right
Hand Subject

21. Average Push Force

22. Large Push Force

23. Surface View FIS 1 and FIS2

24. DoM
Force
Left/Right Forward/Backward
Small
Small Roll Small Pitch
Average Average Roll Average Pitch
Large Large Roll Large Pitch
Pitch
Roll
Small Pitch Average Pitch Large Pitch
Small Roll Ankle Strategy Knee Strategy Hip Strategy
Average Roll Knee Strategy Hip Strategy Falls in frontal plane
Large Roll Hip Strategy Falls sideways Falls
Fuzzy rule set FIS- 1 and 2 for learning

25. Conclusion
 Introduces an intuitive fuzzy logic controller for bipedal
push recovery.
 The hierarchical fuzzy logic based controller has been
designed to reduce the computational cost incurred by
large number of variables.
 We have designed the hierarchical fuzzy logic controller.
 It has been tested on the actual data and generalized the
hierarchical fuzzy controller for easy trainability.
 It has been verified that the hierarchical fuzzy system can
simplify the complex behavior.
 Our developed fuzzy inference system is less
computationally intensive and able to recover the forces
from all the direction.
 The impact of different magnitude forces on the different

26. References
1. Semwal, Vijay Bhaskar; Bhushan, Aparajita; Nandi,
G.C., "Study of humanoid Push recovery based on
experiments," Control, Automation, Robotics and
Embedded Systems (CARE), 2013 International
Conference on , pp.1,6, 16-18 Dec. 2013.
2. Vijay Bhaskar Semwal, Pavan Chakraborty, G.C.
Nandi, Less computationally intensive fuzzy logic
(type-1) based controller for humanoid push
recovery, Robotics and Autonomous Systems,
Available online 16 September 2014.
3. Gordon, Sean W., and Napoleon H. Reyes. "A
Method for computing the Balancing Positions of a
Humanoid Robot." NZCSRSC 2008, April 2008.