Embedded Motion Control 2018 Group 6

Group members

Name:	Report name:	Student id:
Thomas Bosman	T.O.S.J. Bosman	1280554
Raaf Bartelds	R. Bartelds	add number
Bas Scheepens	S.J.M.C. Scheepens	0778266
Josja Geijsberts	J. Geijsberts	0896965
Rokesh Gajapathy	R. Gajapathy	1036818
Tim Albu	T. Albu	19992109
Marzieh Farahani	Marzieh Farahani	Tutor

Initial Design

Link to Initial design report

The report for the initial design can be found here Media:First-report-embedded.pdf.

Requirements and Specifications

Use cases for Escape Room

1. Wall and Door Detection

2. Move with a certain profile

3. Navigate

Use cases for Hospital Room

(unfinished)

1. Mapping

2. Move with a certain profile

3. Orient itself

4. Navigate

Requirements and specification list

In the table below the requirements for the system and their specification as well as a validation are enumarated.

Functions, Components and Interfaces

The software that will be deployed on PICO can be categorized in four different components: perception, monitoring, plan and control. They exchange information through the world model, which stores all the data. The software will have just one thread and will give control in turns to each component, in a loop: first perception, then monitoring, plan, and control. Adding multitasking in order to improve performance might be applied in a later stage of the project. Below, the functions of the four components are described. What these components will do is described for both the Escape Room Challenge (ERC) and the Hospital Challenge (HC).

In the given PICO robot there are two sensors: a laser range finder (LRF) and an odometer. The function of the LRF is to provide the detailed information of the environment through the beam of laser. The LRF specifications are shown in the table bellow,

Specification	Values	Units
Detectable distance	0.01 to 10	meters [m]
Scanning angle	-2 to 2	radians [rad]
Angular resolution	0.004004	radians [rad]
Scanning time	33	milliseconds [ms]

At each scanning angle point a distance is measured with reference from the PICO. Hence an array of distances for an array of scanning angle points is obtained at each time instance with respect to the PICO.

The three encoders provides the odometry data (i.e) position of the PICO in x, y and &theta directions at each time instance. The LRF and Odometry observers' data plays a crucial role in mapping the environment. The mapped environment is preprocessed by two major blocks Perception and Monitoring and given to the World Model. The control approach to achieve the challenge is through Feedforward, since the observers provide the necessary information about the environment so that the PICO can react accordingly.

Perception:

Perception obtains sensor data in continuous time (i.e) the sampling rate of the LRF is maximum. Depending upon the state of the environment, the region of interest of LRF for observation is determined. The minimum distance calculated from this region is returned to the world model. This distance is accessed by other functional blocks for further processing.

ERC:

Input: LRF-data

Use:

• Process (filter) the laser-readings

Interface to world model, perception will store:

• Distances to all near obstacles

HC:

Input: LRF-data, odometry

Use:

• Process both sensors with gmapping
• Process (filter) the laser-readings

Interface to world model, perception will store:

• processed data from the laser sensor, translated to a map (walls, rooms and exits)
• odometry data: encoders, control effort

Monitoring:

Monitoring is a class entity that infers the state of the world model based on the current and previously stored LRF and Odometry data. Monitoring observes the environment at discrete time intervals (i.e) the sampling rate of the observers is less. The possible states from monitoring to the world model are as follows:

1.Start (Initialize and Verify the working of the observers)

2.Wall on Right

3.Wall on Left

4.Wall in front

5.Door on Right

6.Door on Left

7.Wall hit

8.Stop

The above mentioned states of the environment is conditionally assigned to the world model based on the observers' data.

ERC:

Input: distances to objects

Use:

• Process distances to determine situation: following wall, (near-)collision, entering corridor, escaped room

Interface to world model, monitoring will provide to the world model:

• What situation is occuring

HC:

Input: map, distances to objects

Use:

• Process distances and map to determine situation: following path, (near-)collision, found object

Interface to world model, monitoring will provide to the world model:

• What situation is occuring

Plan:

ERC:

Input: state of situation

Use:

• Determine next step: prevent collision, continue following wall, stop because finished

Interface to world model, plan will share with the world model:

• The transition in the statemachine, meaning what should be done next

HC:

Input: state of situation

Use:

• Determine next step: go to new waypoint, continue following path, prevent collision, go back to start, stop because finished
• Determine path to follow

Interface to world model, plan will share with the world model:

• The transition in the statemachine, meaning what should be done next

Control:

ERC:

Input: distances+angles to objects, state of next step

Use:

• Determine setpoint angle and velocity of robot

Interface to world model, what control shares with the world model:

• Setpoint angle and velocity of robot

HC:

Input: state of next step, path to follow

Use:

• Determine setpoint angle and velocity of robot

Interface to world model, what control shares with the world model:

• Setpoint angle and velocity of robot

Overview of the interface of the software structure:

The diagram below provides a graphical overview of what the statemachine will look like. Not shown in the diagram is the case when the events Wall was hit and Stop occur. The occurence of these events will be checked in each state, and in the case they happened, the state machine will navigate to the state STOP. The state machine is likely to be more complex, since some states will comprise a sub-statemachine.