Figures B.I to B.8 show the test environments for the exploration experiments. Two figures are given for each environment. The first diagram shows the walls and objects in the environment. It also shows the positions and orientations from which exploration experiments were started. The second diagram for shows the ‘ideal’ free-space map which would result from complete knowledge of the objects in the environment.

16.1 Motivation

Experience with human control of the exploration process suggested that map quality could be increased rapidly in the early stages of exploration by heading into open regions of space instead of staying close to one of the walls (Section 15.4). The ‘Longest Lines’ strategy described in this chapter was motivated by this observation. The essential idea is to perform a full sensor scan and head in the direction of the longest reading. As many steps as possible are then taken in that direction until an obstacle is encountered. The algorithm then continues by heading in the direction of the longest reading from this new position.

This strategy shares with wall-following the fact that it is totally reactive. Navigational decisions are made solely on the basis of the latest sensor readings.

Section 16.2 gives the details of the implementation and Section 16.3 compares the results to those of Wall-Following and Supervised Wall-Following. Section 16.4 summarises the experimental results and considers the strengths and weaknesses of the strategy.

16.2 Implementation

The strategy, as described in the previous section, is straightforward. The only slight complication is the problem of multiple reflections. Wall-following used the shortest range readings from each viewpoint; multiple reflections were not a problem because they typically cause long range readings. On the other hand, the ‘Longest Lines’ strategy is particularly interested in the long readings. It is therefore necessary to acknowledge the likelihood of multiple reflections and to compensate for them.

Section 4.1 described the attraction of wall-following as an exploration strategy and gave examples of its use in a number of research projects. It was argued in Section 4.3 that wall-following should be the first strategy to be implemented and tested because it will give an indication of what can be achieved when ARNE acts only on the basis of immediatelyavailable information and does not use the map to guide its exploration. This chapter describes the way in which wall-following was implemented on ARNE and presents the results of some explorations using this strategy.

12.1 Implementation

Wall-following has been implemented in two stages. First, ARNE approaches the nearest object that it can detect and positions itself ready for wall-following proper to start. The bulk of the exploration is then a repetitive process of ‘scan,turn,move’ actions in which ARNE moves so as to maintain an ideal distance from the nearest detected object. The remainder of this section describes the implementation of these two stages.

The first stage is quite simple. ARNE performs a complete sensor scan and groups the raw returns into readings, as described in Section 6.3. ARNE then selects the smallest range reading and moves so as to be at a standard distance, IDEAL-WALL-CLEARANCE, from the object. If the minimum range is greater than IDEAL-WALL-CLEARANCE, this means turning in the direction of the minimum reading. Otherwise ARNE turns directly away from the shortest reading.

This thesis examines the process by which an autonomous mobile robot constructs a map of its operating environment. This process can be considered as two distinct topics. First, the robot has to interpret the findings of its sensors so as to make accurate deductions about the state of its environment. This is the problem of ‘map-building’ Second, it has to select its viewpoints so that the sensory measurements contain new and useful information. This is the problem of ‘exploration’. This thesis describes a practical and experimental investigation into both of these issues.

This document is structured as a large number of short chapters. This reflects the wide range of subjects which had to be examined in order to build an effective working robot for map-building and exploration experiments. For ease of reading, the chapters are grouped into three parts; Part I (Chapters 2 to 4) examines the principal areas of previous research upon which this thesis is built; Part II (Chapters 5 to 10) describes the components of the map-building system; and finally Part III (Chapters 11 to 20) reports on experiments to evaluate the effectiveness of a range of exploration strategies. The closing chapters of Part III summarise the results and conclusions and suggest directions for further research.

The remaining sections of this introductory chapter serve as an overview of the thesis and put the later chapters into context.

The table on page 218 lists the input commands to ARNE's control software. The responses to each command are also listed.

ARNE's key physical component is a 300 mm diameter disc which supports the control electronics and the rotating sonar sensor. Below the disc is a chassis which holds the motors and shaft encoders to control the two drive wheels.

5.1 Hardware

ARNE has a drive wheel on each side of the chassis and a low-friction castor at the back. It moves holonomically, turning the wheels in the same direction to move forward or in opposite directions to rotate on the spot. Shaft encoders with a precision of 1024 steps per revolution determine the distance travelled by each wheel to a precision of 0.2 mm.

At the lowest level, the wheel movements are controlled by two dedicated HCTL-1100 motion control chips (Hewlett-Packard 1992, pages 1–77 to 1–115) which generate and execute trapezoidal velocity profiles. The length, acceleration and peak velocity of these movements are specified by the on-board CPU, a 68000-compatible ‘Mini-Module’ micro controller from PSI Systems Limited (PSI 1991).

ARNE's only range sensor is a single rotating Polaroid ultrasonic rangeflnder (Polaroid 1991) which can be seen in Figure 5.1 on top of the box which houses the CPU and other control electronics. The transducer is rotated by a stepper motor with a minimum step size of 1.8°. A full 360° scan is performed in twenty 18° steps.

Section 1.3 explained the decision to connect ARNE to a stationary workstation. A 9600–baud connection to the Mini Module's RS485 serial port was used for this purpose.

This chapter presents the sonar sensor model that was developed in this research. Figure 6.1 shows that the model is used to interpret the raw sonar returns from ARNE before the information is passed on to the the other modules on the workstation.

Section 6.1 outlines the operation of the Polaroid ultrasonic rangefinder used by ARNE. Section 6.2 then describes initial experiments to measure the range to a smooth wall in the test environment. The experiments highlight two key features of the sonar sensor: its wide beam and its uneven signal strength. Section 6.3 proposes a sonar model to mitigate the effect of these features by grouping neighbouring range readings. Section 6.4 then describes experiments to verify that the model will be applicable when measuring the range to the variety of objects that ARNE will encounter in the test environment. Section 6.5 then summarises the model.

6.1 The Polaroid Ultrasonic Sensor

Time-of-flight sonar is used in this thesis; distance information is derived from the time taken for a pulse of sound to travel to an object and be reflected back to the sensor.

Figure 6.2 is a simplified diagram of the rangefinder. Voltage pulses are sent to the transducer, which emits 16 cycles of square wave sound at about 50 kHz. As the sound begins, a timer is started. For a short period after transmission, the transducer is disabled (to give enough time for the vibration to die away) and it is then used to listen for an echo.

15.1 Motivation

This chapter describes a brief digression from autonomous exploration into human-guided exploration. The results in Chapter 14 showed Supervised Wall-Following to be an effective exploration strategy in environments with occlusion and traps. It was not, however, significantly better than simple wall-following in the ‘Empty’ environment. This raised the question:

Is it possible to improve the exploration performance in the ‘Empty’ environment or is Supervised Wall Following generating the best possible results, given the physical robot and its sensors?

To answer this question, experiments were performed to see whether a human operator, guided only by the developing map, could direct ARNE's movements so as to produce better results than Supervised Wall-Following. Similar experiments were performed in the more complicated ‘Walls’ environment.

15.2 Procedure

The exploration software includes an X-Windows interface which enables an operator to send commands (‘move forward’, ‘turn left’, ‘turn right’, and ‘scan’) directly to ARNE. This interface was used in the experiments described in this section. The interface also has the facility for the user to indicate, using the mouse, a position on the map to which ARNE should move. The system then plans and executes such a path. This facility was used for the longer movements between regions of interest.

Consideration was given to the choice of operator for these experiments. It was felt that a volunteer would have no experience of the way in which ARNE senses the world and builds the map and would therefore be unable to explore efficiently.

This Appendix gives the details of the calculations by which a line is fitted to a set of sonar observations.

It is initially necessary to distinguish two cases; the creation of a new confirmed line and the updating of an existing confirmed line. In both cases the confirmed line is fitted to a number of contact points, one for each sonar reading which corresponds to the line. The only difference is in the way the contact points are determined.

A confirmed line is created by ‘upgrading’ a cluster of elementary line segments. As explained in Section 7.1, each line segment has two contact points. Since segments are added to the cluster if they share a sonar reading with a segment already in the cluster, it is common for a single sonar reading to correspond to more than one segment. To avoid giving unnecessary weight to these ‘multiple’ readings, the confirmed line is fitted to a single contact point for each sonar reading. It is therefore necessary to calculate an ‘average contact point’ if the sonar reading corresponds to multiple segments.

If, on the other hand, the confirmed line is to be updated then the line already has a number of contact points and a new one is to be added. As explained in Section 7.3, a contact point is obtained by taking a point at the measured distance from the robot in a direction normal to the line.

This research places great emphasis on practical experimentation and quantitative evaluation of the results. To do this it is essential to have a precise measure of map quality. It is then possible to tune the map-building algorithms or to evaluate an exploration strategy by monitoring the quality of the map as exploration progresses. This chapter examines the issue of measuring the quality of a robot's map.

As an introduction, Section 10.1 examines some of the properties which one would expect to find in a useful quality metric, illustrating with examples of quality measures used by other researchers. The properties are that:

1. metric must be clearly defined.

2. metric must be multi-valued.

3. metric must reflect the purpose of the map.

4. metric must balance coverage and detail.

5. metric must be applicable during the construction of the map.

Section 10.2 discusses the need for an ‘omniscient’ observer. Is it possible for the robot to determine the quality of its map independently or can quality only be judged by comparison with a perfect map held by an external observer? It concludes that some quality measures can indeed by created by the robot independently but that measures derived from an ideal map are the most useful for the current purpose.

Section 10.3 surveys previous research in map-building, documenting the types of metrics which have been used. No ready-made metric was found which could be used in the current research.