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 xX 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 cant. 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: Force=Small (x) ={0-5N}, Force=Medium (x) ={4-9N}
Force=Large (x) ={8-12N}. Fuzzy Set2-The fuzzy value range for
linguistic variable DOM: DoM=Left (x), DoM=Right (x), DoM=Forward
(x), DoM=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}.
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.