Industrial Applications of Genetic Algorithms:Development of Mobile Robot Wall-following Algorithms Using Genetic Programming

Previous | Table of Contents | Next

In almost every run GP is able to generate algorithms that perform reasonably well. The individual shown in Figure 14.8 makes a couple of errors, but for the most part it is exhibiting the desired behavior.

Figure 14.8 Typical end result of a GP run.

The code for the individual displayed in Figure 14.8 is shown below. Analysis of this code (which is typical of the complexity of GP-generated algorithms) is left as an exercise for the reader.

0 | WhileTooFarFromWall
1 | WhileTooFarFromWall
2 | Do2
3 | Do2
4 | WhileTooCloseToWall
5 | WhileInCoridorRange
6 | TurnParallelToClosestWall
7 | Do2
8 | WhileInCoridorRange
9 | Do2
10 | WhileTooCloseToWall
11 | WhileTooFarFromWall
12 | MoveForward
13 | Do2
14 | Do2
15 | WhileTooCloseToWall
16 | TurnParallelToClosestWall
17 | Do2
18 | Do2
19 | WhileTooCloseToWall
20 | WhileInCoridorRange
21 | TurnParallelToClosestWall
22 | Do2
23 | WhileInCoridorRange
24 | Do2
25 | WhileTooCloseToWall
26 | WhileTooFarFromWall
27 | MoveForward
28 | Do2
29 | Do2
30 | WhileTooCloseToWall
31 | TurnParallelToClosestWall
32 | Do2
33 | MoveForward
34 | nTowardsClosestWall
35 | Do2
36 | MoveForward
37 | WhileInCoridorRange
38 | Do2
39 | TurnTowardsClosestWall
40 | Do2
41 | TurnParallelToClosestWall
42 | MoveForward
43 | MoveForward
44 | WhileInCoridorRange
45 | TurnAwayFromClosestWall
46 | Do2
47 | MoveForward
48 | WhileInCoridorRange
49 | Do2
50 | TurnTowardsClosestWall
51 | Do2
52 | TurnParallelToClosestWall
53 | MoveForward
54 | MoveForward
55 | WhileInCoridorRange
56 | TurnAwayFromClosestWall
END

CONCLUSION

Genetic programming has demonstrated the capability of generating wall-following navigation algorithms from the functions and terminals provided. More generally, the experiments conducted over the course of this project have shown the feasibility of using genetic programming to develop mobile robot navigation algorithms. This project lays the foundation for planned follow-on projects of maze traversal, map generation, and full-coverage area traversal. The results of this project show enough promise to warrant further research into these more complex tasks.

REFERENCES

  1 Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection. Cambridge: MIT Press.
  2 Reynolds, Craig W. (1994) Evolution of obstacle avoidance behavior: Using noise to promote robust solutions. Advances in Genetic Programming, pages 221-241. Cambridge: MIT Press.

Previous | Table of Contents | Next

Copyright © CRC Press LLC