The document details various aspects of autonomous robotic systems, focusing on locomotion, kinematics, perception, localization, planning, and navigation. It covers types of mobile robots, including wheeled, legged, and aerial robots, discussing advantages and challenges for each type. Additionally, the importance of sensor integration, localization techniques like SLAM, and path planning algorithms like A* are emphasized for enhancing robotic navigation capabilities.

1. FUNDAMENTAL
TO
AUTONOMOUS
SYSTEM
Razali
Tomari

2. Topic Highlight
•Session 1: Robotic Locomotion (ability to move)
 Introduction to Autonomous Navigation and
Locomotion
 Wheel Mobile Robots
 Legged Mobile Robots
 Aerial Mobile Robots
•Session 2: Mobile Robot Kinematics & Perception (motion and how
to sense)
 Introduction
 Mobile Robot Manoeuvrability & Workspace
 Sensors for Mobile Robot
 Fundamental of Computer Vision
•Session 3: Mobile Robot Localization, Planning and Navigation (how and where to move)
 Robot localization
 Map representation
 Probabilistic Map-Based Localization
 Path Planning
 Obstacle Avoidance

3. Topic Highlight
•Session 1: Robotic Locomotion (ability to move)
 Introduction
 Wheel Mobile Robots
 Legged Mobile Robots
 Aerial Mobile Robots
•Session 2: Mobile Robot Kinematics & Perception (motion and how
to sense)
 Introduction
 Mobile Robot Manoeuvrability & Workspace
 Sensors for Mobile Robot
 Fundamental of Computer Vision
•Session 3: Mobile Robot Localization, Planning and Navigation (how and where to move)
 Robot localization
 Map representation
 Probabilistic Map-Based Localization
 Path Planning
 Obstacle Avoidance

4. Topic Highlight
•Session 1: Robotic Locomotion (ability to move)
 Introduction
 Wheel Mobile Robots
 Legged Mobile Robots
 Aerial Mobile Robots
•Session 2: Mobile Robot Kinematics & Perception (motion and how
to sense)
 Introduction
 Mobile Robot Manoeuvrability & Workspace
 Sensors for Mobile Robot
 Fundamental of Computer Vision
•Session 3: Mobile Robot Localization, Planning and Navigation (how and where to move)
 Robot localization
 Map representation
 Probabilistic Map-Based Localization
 Path Planning
 Obstacle Avoidance

5. Autonomous navigation system
Navigation is the ability to determine your location
within an environment and to be able to figure out
a path that will take you from your current location
to some goal.
Autonomous, it's how we get a vehicle to determine
its location using a set of sensors and then to move
on its own through an environment
to reach a desired goal.

6. Requirement for Autonomous navigation system
MAP SENSORS & ACTUATOR
PATH PLANNING & NAVIGATION ALGORITHM

7. Introductio
n to
Robotic
Locomotion
• Locomotion: Robots need to move in
diverse environments—factories,
homes, exploration sites—depending
on their applications
• Importance: Autonomous robots
require efficient locomotion methods
to perform their tasks, whether it's
navigating rough terrain or flying in
the air
• Classification:
• Wheeled robots
• Legged robot
• Aerial robot

8. Wheel Mobile
Robots – Overview
• Wheel Configurations
• Differential Drive: Commonly used in robots like
Roomba
• Omni-Wheels: Allows motion in any direction,
essential in environments needing high mobility
• Mecanum Wheels: Similar to omni-wheels but
arranged diagonally with rollers, offering excellent
maneuverability, especially in industrial robotics

9. Advantage
s
• Stability: Wheeled robots are
inherently stable compared to
legged robots which require
balance mechanisms
• Energy Efficiency: On flat
surfaces, wheeled robots
consume less energy, making
them a better choice for long-
range travel
• Simple Control: Compared to
legged or aerial robots, wheeled
robots are easier to control since
their motion is mostly linear
Challenges
• Terrain Limitations: Wheeled
robots struggle on rough or
uneven terrain and cannot
climb stairs
• Skidding/Slipping: On certain
surfaces , wheels lose traction,
making control less precise
• Maneuverability: Non-
holonomic wheeled robots
can't move sideways, limiting
their ability to maneuver in
constrained spaces
Wheel Mobile Robots

10. Legged Mobile
Robots – Overview
• Legged Systems
• Bipedal (2 legs): Humanoid robots like
Boston Dynamics’ Atlas and ASIMO.
• Quadrupedal (4 legs): Robots like
Spot offer a balance between stability
and mobility, making them excellent
for rough, uneven terrains
• Hexapods/Octopods: More legs
increase stability, with applications in
rough terrain exploration

11. • Terrain Adaptability: Unlike wheeled
robots, legged robots excel on rough
and uneven surfaces, making them ideal
for search and rescue operations or
Mars exploration
• Obstacle Navigation: Legs allow robots
to step over obstacles, a capability not
possible with wheels
• Improved Maneuverability: Legged
robots can rotate in place and move in
complex ways, making them more
versatile in confined environments
• Control Complexity: Balancing a bipedal
robot, especially on rough terrain, is
computationally intensive and requires
advanced control algorithms
• Energy Consumption: Legged
locomotion requires significantly more
energy compared to wheels
• Stability Issues: Slipping or losing
balance is more common in legged
robots, especially on inclines or slippery
surfaces
Legged Mobile
Robots
Advantage
s
Challenges

12. Aerial Mobile Robots – Overview
• Types
• Rotary-Wing: Common quadcopters like DJI drones
• Fixed-Wing: More like airplanes; they require a
runway or catapult to launch
• Hybrid Systems: Combines aspects of rotary and
fixed-wing designs, offering VTOL capabilities,
useful for high-endurance tasks

13. • Coverage of Large Areas: Aerial
robots can traverse large areas
quickly, making them ideal for
surveillance, mapping, or delivery
• No Terrain Restrictions: Unlike
ground robots, aerial systems can
navigate over challenging landscapes
such as mountains, lakes, and dense
forests
• Speed: Aerial robots, especially fixed-
wing drones, can move faster than
ground robots, useful in time-sensitive
tasks like emergency response
• Battery Life: Most consumer drones have
limited flight times
• Payload Restrictions: Aerial robots have
limited capacity to carry heavy payloads,
which constrains their use in applications
like heavy cargo transport
• Safety & Regulations: Navigating airspace,
avoiding collisions, and abiding by
government regulations is challenging,
especially in urban environments
Aerial Mobile Robots
Advantage
s
Challenges

14. Comparison of Locomotion Modes
•A comparison chart of the key metrics
•Efficiency: Wheeled > Aerial > Legged
•Terrain Adaptability: Legged > Aerial >
Wheeled
•Complexity: Legged > Aerial > Wheeled

15. Future Trends in Robotic
Locomotion
• Soft Robotics: Soft robots with flexible
limbs and surfaces adapt better to
unpredictable environments, such as those
found in medical surgery or underwater
exploration
• Hybrid Locomotion: Systems combining
wheels, legs, and even flight capabilities
for all-terrain versatility
• Autonomy & AI: Robots are increasingly
using AI to make decisions about which
mode of locomotion to use in specific
environments

16. Introduction to Mobile Robot
Kinematics
• Kinematics: Deals with motion without considering
forces, which is critical for understanding how a robot’s
joints, wheels, or actuators result in motion
• Robot Motion: The ability to predict how a robot will
move given input commands is the foundation of path
planning and control
• DoF: Degrees of Freedom represent the robot
capability to move in certain directions (Mobile robot,
3DOF (x,y,θ)
• Holonomic Systems: Can achieve any movement
instantaneously in all directions (omni wheel)
• Non-Holonomic Systems: Subject to motion
constraints (differential drive)

17. Kinematic
Models for
Wheeled Robots
• Differential Drive: The simplest
wheeled robot model where each
wheel operates independently
• Car-like: Simplified model for
cars and motorcycles

18. Sensors for Mobile Robots
• Sensors: Critical for perception, allowing robots to understand
their surroundings and make decisions based on that data
• Main Categories
• Internal Sensors: Measure internal robot states like wheel
speed, battery levels
• External Sensors: Measure the external environment, such as
proximity to obstacles or temperature
2D LIDAR 3D LIDAR
RADAR 3D CAMERA
ENCODER
IMU

19. Internal Sensors
• Examples
• Wheel Encoders: Measure
the rotation of wheels, used
for odometry
• Inertial Measurement Unit
: Tracks acceleration and
orientation, essential for
stabilizing aerial robots and
aiding localization in all
mobile robots

20. External Sensors – Vision Sensors
• Cameras: Allow robots to perceive the
environment visually, enabling object
recognition and tracking
• Challenges: Cameras are subject to issues
like poor lighting or occlusions, but they
are versatile and rich in information
• Applications: Used in object detection ,
SLAM , and navigation

21. External Sensors – Range Sensors
• LiDAR: A laser-based sensor used to measure
distances to objects with high accuracy,
generating 3D point clouds of the environment
• Ultrasonic Sensors: Emit sound waves and
measure the time it takes for the echoes to
return
• Infrared Sensors: Emit infrared light to detect
obstacles; typically used for short-range
distance measurement

22. External Sensors – Contact Sensors
• Touch Sensors: Register contact
with objects, useful for close-
proximity tasks or when precision is
needed
• Bump Sensors: Detect when a robot
physically bumps into an object;
commonly used in early-stage
robots for basic collision detection

23. Sensor Fusion and
Challenge
• Definition: Combines data from multiple sensors to improve
accuracy and robustness
• Applications: Used in self-driving cars, where a combination of
LiDAR, cameras, and radar ensures the vehicle can navigate
safely in complex environments
• Sensor Noise: All sensors are subject to noise—random
inaccuracies that affect the sensor data
• Environmental Factors: Issues like low lighting, rain, snow, or
fog can severely impact the performance of vision-based
sensors

24. Case Study: Autonomous Wheelchair
System • Sensors Used
• LiDAR: For precise, real-
time mapping and obstacle
detection
• 3D Cameras: For human
face estimation and
obstacle detection
• IMU: determine wheelchair
orientation
• Camera : User intent
prediction
( Threat and Human detection )
( Orientation refinement)
( Obstacle Detection)
( User Intent Prediction)
Laser FOV
Kinect FOV

25. Method Pipeline
Kinect
Laser
Pose Tracking
Blob
Mapping
Head
Detection
Region
Validation
Blob
Analysis
Personal
Space
User
αu
-ve
Positive & Overhanging Obstacles Negative Obstacles
Motor Command
Human detection & pose tracking
Navigation Map
User Input
25
Socially
Acceptabl
e
Safety
HCI
Interface

26. 26
• Obstacles Definition (Murarka A, 2010):
Ground plane
Overhanging Obstacle
Positive Obstacle
Negative Obstacle

27. 27
αgX + βgY + γgZ +d =0
• Initial step for detecting the obstacle
P1
P0
αgX + βgY + γgZ +d =0
Pc
Ground plane Ground plane equation :
Point Cloud, e.g. Pc, location w.r.t
ground plane:
2
2
2
g
g
g
c
g
c
g
c
g d
Z
Y
X
s












Obstacle
Negative
Obstacle
Positive
ve
ve
s






s

28. 28
• Negative Obstacle Detection using the S sign
Problem :N
ot
Correctly
Detected
P1
P0
Pc0
Virtual
ground
plane
Pc
Ground
plane
c
c
g
c
g
c
g
co P
Z
Y
X
d
P















• Solution :

29. 29
• Negative Obstacle Detection using the S sign
Problem :N
ot
Correctly
Detected
P1
P0
Pc0
Virtual
ground
plane
Pc
Ground
plane
c
c
g
c
g
c
g
co P
Z
Y
X
d
P















• Solution :

30. 30
EZ = 120 ˚
Non-EZ = 240˚
4
1 2
3 5
• Dynamically changing the PS shape w.r.t. the awareness level
• Example of Result
Obstacle Detection and Buffer Space
Assignment

31. 31
• System evaluation in real scenario

32. 32
• Door Passing and Hallway Navigation

33. Future Trends
in Mobile Robot
Perception
• AI and Machine Learning:
As perception systems
improve, machine learning
will play a key role in real-
time object recognition and
classification, improving the
autonomy of mobile robots
• Edge Computing: Processing
sensor data directly on the
robot speeds up decision-
making, allowing for quicker
responses in critical situations

34. Introduction to Localization,
Planning, and Navigation
Definition: Localization is
determining a robot’s
position within an
environment;
planning is devising a path
from the start to the goal,
and
navigation is the process of
executing that plan while
avoiding obstacles
Importance: Accurate
localization is critical for
successful navigation and
planning

35. What is Robot Localization?
Definition: Localization is the
process by which a robot
determines its position relative
to a known map or in an
unknown environment
Key Problem: Without precise
localization, a robot cannot
execute its tasks reliably

36. Types of
Localization
Problems
• Position Tracking: The robot has
a known starting position and
tracks its position as it moves
• Global Localization: The robot
must determine its position from
scratch in a known environment
• Kidnapped Robot Problem: The
robot is randomly displaced and
must re-localize itself

37. Odometry in
Localization
• Odometry is the use of internal
sensors to estimate the robot’s
position based on its movement
• Advantages: Useful for short-term
localization and when external data
is unavailable
• Challenges: Accumulates errors
over time due to wheel slippage or
sensor drift, leading to inaccurate
position estimates

38. Probabilistic
Localization
• Because sensor data and motion
models are inherently uncertain,
probabilistic methods are used to
represent the robot’s belief about
its position
• Well known method : Kalman Filter
& Particle Filter
• Applications: Common in SLAM and
autonomous navigation systems,
such as those used in autonomous
cars and mobile service robots

39. Map
Representation
in Localization
• Types of Maps
• Feature-Based Maps: Store specific, recognizable
features
• Occupancy map : Divide the environment into a
grid, with each cell representing free or occupied
space

40. Particle
Filter for
Localization
• Definition: A particle filter
uses a set of particles to
represent the possible
locations of the robot
• Advantages: Suitable for
non-linear, non-Gaussian
problems, like global
localization
• Example: A cleaning robot
using a particle filter to
determine its position in an
unfamiliar home

41. Problem statement : Where is the location of the
Robot in the map?
Particle Filter Example

42. Problem statement : Where is the location of the
Robot in the map?

43. Problem statement : Where is the location of the
Robot in the map?

44. Problem statement : Where is the location of the
Robot in the map?

45. Problem statement : Where is the location of the
Robot in the map?
Relative measurement

46. Robot with laser reading

47. Based on laser reading, where shall the
robot located in map

48. Bunch of possibility in each wall

49. All possible location of robot

50. Possible location with uncertainty

51. Generate probability map

52. How particle filter can be use

53. From the map, generate possible random
particle

54. Example 50 random particle that cover the
map

55. Based on laser reading, which particle that
represent the robot true pose

56. Compare laser reading with each particle

57. Particle with high possibility will have bigger weight
value. Then resample new particle in high probability
location

58. The new particle distribution after first cycle
sampling

59. When robot move, particle will also move and then
based on sensor reading, the particle will be
evaluated again

60. The process repeated until the particle
concentrated into high possibility location

61. Which will indicate the robot localization in
the map

64. Simultaneous
Localization
and Mapping
• SLAM is the process where a
robot simultaneously
constructs a map of an
unknown environment and
localizes itself within it
• Importance: SLAM is crucial
for robots operating in
unstructured or unknown
environments, such as
autonomous vacuum
cleaners or drones in rescue
operations

65. SLAM
Techniques
• Types of SLAM
• Graph-Based SLAM:
Represents the environment
as a graph, where nodes are
robot poses, and edges are
constraints derived from
sensor data
• Extended Kalman Filter
SLAM: An extension of the
Kalman filter, used for
smaller environments
• FastSLAM: A particle filter-
based approach that
handles large, complex
environments

66. Path Planning
– Introduction
• Definition: Path planning is the
process of determining a feasible
and safe path from the robot’s
current position to the target
position
• Key Objective: Avoid obstacles
while minimizing travel time or
distance

67. Types of Path
Planning
Global Planning: The
robot has full
knowledge of the
environment
Local Planning: The
robot dynamically plans
a path based on real-
time sensor data
without a complete
map. Obstacle
Avoidance
Hybrid Approaches:
Combine both global
and local planning for
more robust and
adaptable navigation

68. Path Planning

69. A* Algorithm for Path Planning

70. A* Algorithm for Path Planning
• A* is a popular search algorithm
that optimizes Dijkstra’s
algorithm by adding heuristics to
estimate the cost from the current
node to the goal
• Finding shortest path from start to
goal
• Advantages: Efficient and optimal
in finding the shortest path while
considering obstacles
• Applications: Widely used in
autonomous vehicles, video
games , and robotic navigation in
structured environments

71. Local Planning : What is Robot Navigation?
Definition: Navigation is
the execution of a path
while considering dynamic
factors like moving
obstacles, sensor noise,
and changes in the
environment
Challenges: Handling
dynamic environments,
managing localization
errors in real time, and
ensuring the robot
reaches its destination
efficiently and safely

72. Local Path Planner : Obstacle Avoidance
Techniques
• Potential Field Method:
The robot treats obstacles
as repelling forces and
the goal as an attracting
force
• Vector Field Histogram :
Builds a histogram of
obstacle-free regions
around the robot and
selects the most suitable
direction for motion

73. Example of Navigation in Confine Space
Autonomously