Chapters 12 and 13 showed the results of exploring by wall-following. How, if at all, can that performance be bettered? This chapter begins by describing, in Section 14.1, some circumstances in which wall-following appears to be inefficient and then, in Section 14.2, proposes a new strategy, Supervised Wall-Following, to eliminate these inefficiencies.

Section 14.3 presents the results of experimental tests of Supervised Wall-Following. Its value is shown to be higher in more complex environments.

Section 14.4 summarises the results and suggests some directions in which the algorithm could be developed.

14.1 Shortcomings of Wall-Following

In the wall-following experiments described in the previous two chapters, ARNE was specifically denied access to the map while making navigational decisions. A consequence of this was that a human observer watching the exploration (and looking at the map) would become frustrated by ARNE's inflexibility. In certain circumstances ARNE would make movements which, to the human observer, would simply appear to be ‘stupid’. Three of the most obvious circumstances are: falling into traps, re-examining known objects, and repeating fruitless examinations. The remainder of this section considers each of these problems in turn, illustrating with examples.

Figure 14.1 shows a simple example of a wall-following trap. ARNE began at position 6 in room ‘Columns’, close to one of the free-standing cylinders. It began to circulate around the cylinder and continued to do so for the entire exploration period. This obviously restricted its view of the environment and limited the quality of the map.