This document describes research on a hexapod robot called COMET-IV that uses a laser range finder (LRF) to assist with force-based walking. It discusses the robot's configuration including its dimensions, sensors, and control system. An algorithm is proposed that uses the LRF to build a 3D grid map of obstacles in the environment and dynamically adjusts the robot's stable walking range based on force feedback. Experiments show the vision-assisted approach provides more stable walking behavior compared to no vision input. Future work is planned to further integrate sensor data and control leg movements for varied terrain.

1. Laser Range Finder (LRF) Assisted Force-based
Walking for Hexapod Robot COMET-IV
by
Mohd Razali Daud1
, Kenzo Nonami2
1,2
Department of Mechanical Engineering, Division of Artificial System Science,
Graduate School of Engineering, Chiba University
1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
1
E-mail: mrazali@graduate.chiba-u.jp
2
E-mail: nonami@faculty.chiba-u.jp

2. 1. Introduction (1)
Surgery robot [1] Walking Forest Machine [2]
Efficient assists and convenience for human life

3. 1. Introduction (2)
Efficient assists and convenience for human life
Cleaning robot, iRobot [3]
Service robot, PatrolBot [4]

4. 1. Introduction (3)
Ground robots
more statically stableLegged robot
Wheeled type robot [5]
Crawler type robot [5]
Legged type robot [4]

5. 1. Introduction (5)
If no vision sensor!!!

6. 1. Introduction (6)
With vision sensor

7. 2. COMET-IV Configuration (1)
Omni-directional
camera
Stereo-vision
camera

8. 2. COMET-IV Configuration (2)
Item Value
Overall Height [m] 2.80
Body Height [m] 0.8
Width [m] 3.50
Length [m]
(omni directional gait)
3. 0
Max. Walking Speed [m/h] 1000
Weight [kg] 2120
Gasoline Engine [cc] 750 (2 units)

9. 2. COMET-IV Configuration (3)
Autonomous Navigation System
Control System
Leg Controller
Navigation Computer
Target Computer
Proportional
Solenoid Valve
: Pressure Sensor
: Potentiometer
Hydraulic
Motor
Shoulder
Thigh
Shank
Foot
×6
A/D
Board
Attitude Sensor (On the body)
D/A
Board
Valve
Controller
Host Computer
(Program Transmit)
Laser Range Finder
Stereo-vision Camera
Omni-directional Camera
Gait Planner
Path Planner

10. MAPPING & MOTION PLAN
StartGoal
2. COMET-IV Configuration (4) – Why 3D
3-D LRF

11. Reflected
laser beam
spots (Wall)
LRF with normal
mount style (2D)
Moving robot
Wall
2. COMET-IV Configuration (5) – Why 3D

12. 3. Walking Trajectory and Patterns
0.5n oc ct cθ θ θ= −
cmθ
smθ
1. Movement based on center of body
and shoulder rotational angle.
2. Using tripod gait pattern.
( )ocθ
: center of body (CoB) angle
: initial of SCS based angle of each leg
( )ct
θ
{ }2 5
,cm c cθ θ θ=
{ }1 3 4 6
, , ,sm c c c cθ θ θ θ θ=
: SCS conrol input trajectory for each leg( )ncθ

13. 4. Force threshold-based Trajectory
0 1,3( )
n
z foot footF F z I F= =

14. 5. 3D Position of Obstacle Based on LRF
c cos siniX R ϕ θ=
c cos cosiY R θ ϕ=
c siniZ H R ϕ= +

15. 6. Grid Occupancy Registration (1)
( ) ( ) ( )
1 1
GD CD
GC C GC C GC C GC
P P
R R R Tψ θ ϕ
   
=   
   

16. 6. Grid Occupancy Registration (2)
Obstacles with less
than 0.75 m height
Obstacles with more
than 0.75 m height

17. 7. Obstacle’s Geometric Requirement (1)

18. 7. Obstacle’s Geometric Requirement (2)
Edges of
obstacles

19. 8. Dynamic Stable Range for Force Control (1)
min
max
( );
( ) ;
e b
b
e x b
L t S
S
L t S

= 
+ l
,
,
( ) wb wb
wa wa
k x y
e sk x y
L s G l
=
=
= ∑
Without
vision
With
vision

20. 8. Dynamic Stable Range for Force Control (2)

21. 8. Dynamic Stable Range for Force Control (3)

22. 9. Experiments and Results (1)

23. 9. Experiments and Results (2)
Matched with
vision input

24. 9. Experiments and Results (3)
Z-axis position of Leg 1

25. 9. Experiments and Results (4)
Vision-based PPF gives more
stable result

26. 9. Experiments and Results (5)
Vision-based PPF gives more
stable result

27. 9. Experiments and Results (6)
Vision-based PPF gives
better result

28. 9. Conclusion and future works
1. The proposed GWALR with customized Edge Detection method has
successfully determined the geometric of the obstacles that exist in
the environment, and they are correctly represented by the grid cell
map.
2. The control algorithm has effectively and dynamically switches the
stable range in accordance to the performance of Pull-back action,
and has successfully maintained the body of the robot in a horizontal
position at all times even when walking on uneven ground. However,
though the leg opening size is fixed at 0.8 m per half cycle, when the
robot is operated to walk long distances, at times the opening
changed in a random manner, and may cause the leg to step at a
different place instead of as instructed by the controller unit.
3. In future research, the use of the vision output to control the leg
opening size should be considered while enabling the robot to
always keep its body in a horizontal position.
4. To ensure the robot can be operated in all surface conditions,
integration of the LRF with the other sensors need to be done.