This document discusses various topics in robotics including robot configurations, localization and mapping, motion planning, and motor control. It describes how a robot's configuration can be specified by six numbers corresponding to its six degrees of freedom. Localization and mapping are framed as probabilistic inference problems that require accurate sensor and motion models. Motion planning involves finding trajectories in the robot's configuration space and various techniques like cell decomposition and probabilistic roadmaps can be used to discretize this space. Motor control balances deterministic and stochastic optimal control approaches.

1. Robotics
Chapter 25
Chapter 25 1

2. Outline
Robots, Effectors, and Sensors
Localization and Mapping
Motion Planning
Motor Control
Chapter 25 2

3. Mobile Robots
Chapter 25 3

4. Manipulators
R
RR
P
R R
Configuration of robot specified by 6 numbers
⇒ 6 degrees of freedom (DOF)
6 is the minimum number required to position end-effector arbitrarily.
For dynamical systems, add velocity for each DOF.
Chapter 25 4

5. Non-holonomic robots
θ
(x, y)
A car has more DOF (3) than controls (2), so is non-holonomic;
cannot generally transition between two infinitesimally close configurations
Chapter 25 5

6. Sensors
Range finders: sonar (land, underwater), laser range finder, radar (aircraft),
tactile sensors, GPS
Imaging sensors: cameras (visual, infrared)
Proprioceptive sensors: shaft decoders (joints, wheels), inertial sensors,
force sensors, torque sensors
Chapter 25 6

7. Localization—Where Am I?
Compute current location and orientation (pose) given observations:
Xt+1Xt
At−2 At−1 At
Zt−1
Xt−1
Zt Zt+1
Chapter 25 7

8. Localization contd.
xi, yi
vt ∆t
t ∆t
t+1
xt+1
h(xt)
xt
θt
θ
ω
Z1 Z2 Z3 Z4
Assume Gaussian noise in motion prediction, sensor range measurements
Chapter 25 8

9. Localization contd.
Can use particle filtering to produce approximate position estimate
Robot position
Robot position
Robot position
Chapter 25 9

10. Localization contd.
Can also use extended Kalman filter for simple cases:
robot
landmark
Assumes that landmarks are identifiable—otherwise, posterior is multimodal
Chapter 25 10

11. Mapping
Localization: given map and observed landmarks, update pose distribution
Mapping: given pose and observed landmarks, update map distribution
SLAM: given observed landmarks, update pose and map distribution
Probabilistic formulation of SLAM:
add landmark locations L1, . . . , Lk to the state vector,
proceed as for localization
Chapter 25 11

12. Mapping contd.
Chapter 25 12

13. 3D Mapping example
Chapter 25 13

14. Motion Planning
Idea: plan in configuration space defined by the robot’s DOFs
conf-3
conf-1
conf-2
conf-3
conf-2
conf-1
␸
␸
elb
shou
Solution is a point trajectory in free C-space
Chapter 25 14

15. Configuration space planning
Basic problem: ∞d
states! Convert to finite state space.
Cell decomposition:
divide up space into simple cells,
each of which can be traversed “easily” (e.g., convex)
Skeletonization:
identify finite number of easily connected points/lines
that form a graph such that any two points are connected
by a path on the graph
Chapter 25 15

16. Cell decomposition example
start
goal
start
goal
Problem: may be no path in pure freespace cells
Solution: recursive decomposition of mixed (free+obstacle) cells
Chapter 25 16

17. Skeletonization: Voronoi diagram
Voronoi diagram: locus of points equidistant from obstacles
Problem: doesn’t scale well to higher dimensions
Chapter 25 17

18. Skeletonization: Probabilistic Roadmap
A probabilistic roadmap is generated by generating random points in C-space
and keeping those in freespace; create graph by joining pairs by straight lines
Problem: need to generate enough points to ensure that every start/goal
pair is connected through the graph
Chapter 25 18

19. Motor control
Can view the motor control problem as a search problem
in the dynamic rather than kinematic state space:
– state space defined by x1, x2, . . . , ˙x1, ˙x2, . . .
– continuous, high-dimensional (Sarcos humanoid: 162 dimensions)
Deterministic control: many problems are exactly solvable
esp. if linear, low-dimensional, exactly known, observable
Simple regulatory control laws are effective for specified motions
Stochastic optimal control: very few problems exactly solvable
⇒ approximate/adaptive methods
Chapter 25 19

20. Biological motor control
Motor control systems are characterized by massive redundancy
Infinitely many trajectories achieve any given task
E.g., 3-link arm moving in plane throwing at a target
simple 12-parameter controller, one degree of freedom at target
11-dimensional continuous space of optimal controllers
Idea: if the arm is noisy, only “one” optimal policy minimizes error at target
I.e., noise-tolerance might explain actual motor behaviour
Harris & Wolpert (Nature, 1998): signal-dependent noise
explains eye saccade velocity profile perfectly
Chapter 25 20

21. Setup
Suppose a controller has “intended” control parameters θ0
which are corrupted by noise, giving θ drawn from Pθ0
Output (e.g., distance from target) y = F(θ);
y
Chapter 25 21

22. Simple learning algorithm: Stochastic gradient
Minimize Eθ[y2
] by gradient descent:
θ0Eθ[y2
] = θ0 Pθ0(θ)F(θ)2
dθ
=
θ0Pθ0(θ)
Pθ0(θ)
F(θ)2
Pθ0(θ)dθ
= Eθ[
θ0Pθ0(θ)
Pθ0(θ)
y2
]
Given samples (θj, yj), j = 1, . . . , N, we have
ˆθ0Eθ[y2] =
1
N
N
j=1
θ0Pθ0(θj)
Pθ0(θj)
y2
j
For Gaussian noise with covariance Σ, i.e., Pθ0(θ) = N(θ0, Σ), we obtain
ˆθ0Eθ[y2] =
1
N
N
j=1
Σ−1
(θj − θ0)y2
j
Chapter 25 22

23. What the algorithm is doing
x
x
x
x
x
x
x
x x
x
x
x
x
x x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Chapter 25 23

24. Results for 2–D controller
4
5
6
7
8
9
10
0.2 0.4 0.6 0.8 1 1.2
Velocityv
Angle phi
Chapter 25 24

25. Results for 2–D controller
4.51
4.52
4.53
4.54
4.55
4.56
4.57
4.58
4.59
4.6
4.61
0.6 0.61 0.62 0.63 0.64 0.65
Velocityv
Angle phi
Chapter 25 25

26. Results for 2–D controller
0.0055
0.006
0.0065
0.007
0.0075
0.008
0.0085
0.009
0.0095
0 2000 4000 6000 8000 10000
E(y^2)
Step
Chapter 25 26

27. Summary
The rubber hits the road
Mobile robots and manipulators
Degrees of freedom to define robot configuration
Localization and mapping as probabilistic inference problems
(require good sensor and motion models)
Motion planning in configuration space
requires some method for finitization
Chapter 25 27