Robotics__Vision_and_Control_Fundamental_Algorithms_in_MATLAB英文版05-机器人导航

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Chapter

Robot navigation is the problem of guiding a robot towards a goal.

The human approach to navigation is to make maps and erect sign-

posts, and at first glance it seems obvious that robots should oper-

ate the same way. However many robotic tasks can be achieved with-

out any map at all, using an approach referred to as reactive naviga-

tion. For example heading towards a light, following a white line on

the ground, moving through a maze by following a wall, or vacu-

uming a room by following a random path. The robot is reacting

directly to its environment: the intensity of the light, the relative

position of the white line or contact with a wall. Grey Walter’s tor-

toise Elsie from page 61 demonstrated “life-like” behaviours – she

reacted to her environment and could seek out a light source. Today

more than 5 million Roomba vacuum cleaners are cleaning floors

without using any map of the rooms they work in. The robots work

by making random moves and sensing only that they have made

contact with an obstacle.

The more familiar human-style map-based navigation is used

by more sophisticated robots. This approach supports more com-

plex tasks but is itself more complex. It imposes a number of re-

quirements, not the least of which is a map of the environment. It

also requires that the robot’s position is always known. In the next

chapter we will discuss how robots can determine their position

and create maps. The remainder of this chapter discusses the reac-

tive and map-based approaches to robot navigation with a focus on

wheeled robots operating in a planar environment.

Navigation

the process of directing a vehicle so as to reach the intended destination

IEEE Standard 172-1983

Fig. 5.1.

Time lapse photograph of a

Roomba robot cleaning a room

(photo by Chris Bartlett)

5.1

Reactive Navigation

Surprisingly complex tasks can be performed by a robot even if it has no map and no

real idea about where it is. As already mentioned robotic vacuum cleaners use only

random motion and information from contact sensors to perform a complex task as

shown in Fig. 5.1. Insects such as ants and bees gather food and return it to the nest

based on input from their senses, they have far too few neurons to create any kind of

mental map of the world and plan paths through it. Even single-celled organisms such

as flagellate protozoa exhibited goal seeking behaviours. In this case we need to revise

our earlier definition of a robot to

a goal oriented machine that can sense, p

lan and act.

The manifestation of complex behaviours by simple organisms was of interest to

early researchers in cybernetics. Grey Walter’s robotic tortoise demonstrated that it

could moves toward a light source, a behaviour known as phototaxis.



This was an

important result in the then emerging scientific field of cybernetics.

5.1.1

Braitenberg Vehicles

A very simple class of goal achieving robots are known as Braitenberg vehicles and are

characterised by direct connection between sensors and motors. They have no explicit

internal representation of the environment in which they operates and nor do they

make explicit plans.



Consider the problem of a robot moving in two dimensions that is seeking the

maxima of a scalar field – the field could be light intensity or the concentration of

some chemical.



The Simulink® model

>> sl_braitenberg

shown in Fig. 5.2 achieves this using a steering signal derived directly from the sensors.



More generally a

taxis

is the response

of an organism to a stimulus gradient.

Valentino Braitenberg (1926–) is an Italian-Austrian neuro-scientist and cyberneticist, and former

director at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany. His 1986

book “Vehicles: Experiments in Synthetic Psychology” (image on right is of the cover this book,

published by The MIT Press, ©MIT 1984) describes reactive goal-achieving vehicles, and such

systems are now commonly known as Braitenberg Vehicles.

A Braitenberg vehicle is an automaton or robot which combines sensors, actuators and their

direct interconnection to produce goal-oriented behaviors. Grey Walter’s tortoise predates the

use of this term but is nevertheless an example of such a vehicle.

These vehicles are described as conceptually as analog circuits, but more recently small robots

based on a digital realization of the same principles have been developed.

William Grey Walter (1910–1977) was a neurophysiologist and pioneering cyberneticist born in

Kansas City, Missouri and studied at King’s College, Cambridge. Unable to obtain a research fellow-

ship at Cambridge he worked on neurophysiological research in hospitals in London and from 1939

at the Burden Neurological Institute in Bristol. He developed electroencephalographic brain topog-

raphy which used multiple electrodes on the scalp and a triangulation algorithm to determine the

amplitude and location of brain activity. (Image: courtesy of the Reuben Hoggett Collection)

Walter was influential in the then new field of cybernetics. He built robots to study how complex

reflex behavior could arise from neural interconnections. His tortoise Elsie (of the species Machina

Speculatrix) is shown, without its shell, on page 61. Built in 1948 Elsie was a three-wheeled robot ca-

pable of phototaxis that could also find its way to a recharging station. A second generation tortoise

(from 1951) is in the collection of the Smithsonian Institution. He published popular articles in “Scien-

tific American” (1950 and 1951) and a book “The Living Brain” (1953). He was badly injured in a car

accident in 1970 from which he never fully recovered. (Image courtesy Reuben Hoggett collection)

This is a fine philosophical point, the plan

could be considered to be implicit in the

details of the connections between the

motors and sensors.

This is similar to the problem of mov-

ing to a point discussed in Sect. 4.2.1.

This is similar to Braitenberg’s Vehicle 4a.

Chapter 5

Navigation

To ascend the gradient we need to estimate the gradient direction at the current

location and this requires at least two measurements of the field.



In this example we

use two sensors, bilateral sensing, with one on each side of the robot’s body. The sen-

sors are modelled by the green sensor blocks shown in Fig. 5.2 and are parameterized

by the position of the sensor with respect to the robot’s body, and the sensing function.

In this example the sensors are at ±2 units in the vehicle’s lateral or y-direction.

The field to be sensed is a simple inverse square field defined by

1 function sensor = sensorfield(x, y)

2 xc = 60; yc = 90;

3 sensor = 200./((x-xc).^2 + (y-yc).^2 + 200);

which returns the sensor value s(x, y) ∈ [0, 1] which is a function of the sensor’s posi-

tion in the plane. This particular function has a peak value at the point (60, 90).

The vehicle speed is

where s

and s

are the right and left sensor readings respectively. At the goal, where

= s

= 1 the velocity becomes zero.

Steering angle is based on the difference between the sensor readings

so when the field is equal in the left- and right-hand sensors the robot moves straight ahead.



We start the simulation from the Simulink® menu or the command line

>> sim('sl_braitenberg');

and the path of the robot is shown in Fig. 5.3. The starting pose can be changed through

the parameters of the Bicycle block. We see that the robot turns toward the goal and

slows down as it approaches, asymptotically achieving the goal position.

This particular sensor-action control law results in a specific robotic behaviour. We

could add additional logic to the robot to detect that it had arrived near the goal and

then switch to a stopping behaviour. An obstacle would block this robot since its only

behaviour is to steer toward the goal, but an additional behaviour could be added to

handle this case and drive around an obstacle. We could add another behaviour to

search randomly for the source if none was visible. Grey Walter’s tortoise had four

behaviours and switching was based on light level and a touch sensor.

Fig. 5.2. The Simulink® model

sl_braitenberg drives the

vehicle toward the maxima of a

provided scalar function. The ve-

hicle plus controller is an example

of a Braitenberg vehicle

We can make the measurements simul-

taneously using two spatially separated

sensors or from one sensor over time as

the robot moves.

Similar strategies are used by moths

whose two antennae are exquisitely

sensitive odor detectors that are used

to steer a male moth toward a phero-

mone emitting female.

5.1 · Reactive Navigation

Multiple behaviours and the ability to switch between them leads to an approach

known as behaviour-based robotics. The subsumption architecture was proposed as a

means to formalize the interaction between different behaviours. Complex, some might

say intelligent looking, behaviours can be manifested by such systems. However as more

behaviours are added the complexity of the system grows rapidly and interactions

between behaviours become more complex to express and debug. Ultimately the pen-

alty of not using a map becomes too great.

5.1.2

Simple Automata

Another class of reactive robots are known as bugs – simple automata that perform

goal seeking in the presence of non-driveable areas or obstacles. There are a large

number of bug algorithms and they share the ability to sense when they are in proxim-

ity to an obstacle. In this respect they are similar to the Braitenberg class vehicle, but

the bug includes a state machine and other logic in between the sensor and the motors.

The automata have memory which our earlier Braitenberg vehicle lacked.



In this

section we will investigate a specific bug algorithm known as bug2.

We start by loading an obstacle field to challenge the robot

>> load map1

which defines a 100 × 100 matrix variable map in the workspace. The elements are

zero or one representing free space or obstacle respectively and this is shown in Fig. 5.4.

Tools to generate such maps are discussed on page 92. This matrix is an example of an

occupancy grid which will be discussed further in the next section.

At this point we state some assumptions. Firstly, the robot operates in a grid world

and occupies one grid cell. Secondly, the robot does not have any non-holonomic con-

straints and can move to any neighbouring grid cell. Thirdly, it is able to determine its

position on the plane which is a non-trivial problem that will be discussed in detail in

Chap. 6. Finally, the robot can only sense its immediate locale and the goal. The robot

does not use the map – the map is used by the simulator to provide sensory inputs to

the robot.

We create an instance of the bug2 class

>> bug = Bug2(map);

and the goal is

>> bug.goal = [50; 35];

Fig. 5.3.

Path of the Braitenberg vehicle

moving toward (and past) the

maximum of a 2D scalar field

whose magnitude is shown

color coded

Braitenberg’s book describes a series of

increasingly complex vehicles, some of

which incorporate memory. However

the term

Braitenberg vehicle

has be-

come associated with the simplest ve-

hicles he described.

Chapter 5

Navigation

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