### Abstract

In this paper, we propose a novel path-planning and obstacle avoidance algorithm GODZILA for navigation in unknown environments. No prior knowledge of the environment is required. The path-planning algorithm follows a purely local approach using only the current range sensor measurements at each sampling instant and requiring only a small number of stored variables in memory. No map of the environment is built during navigation. This minimizes the memory and computational requirements for implementation of the algorithm, a feature that is especially attractive for small autonomous vehicles. The algorithm utilizes three components: an optimization algorithm, a local straight-line path planner to visible targets, and random navigation. It is proved, for navigation in any finite-dimensional space, that the path-planning algorithm converges in finite time with probability 1. The performance of the algorithm is demonstrated through simulations for path-planning in two-dimensional and three-dimensional spaces. It is seen that a relatively small number of range sensor measurements is sufficient even in complex unknown environments.

Original language | English (US) |
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Title of host publication | Proceedings of the American Control Conference |

Pages | 110-115 |

Number of pages | 6 |

Volume | 1 |

State | Published - 2005 |

Event | 2005 American Control Conference, ACC - Portland, OR, United States Duration: Jun 8 2005 → Jun 10 2005 |

### Other

Other | 2005 American Control Conference, ACC |
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Country | United States |

City | Portland, OR |

Period | 6/8/05 → 6/10/05 |

### Fingerprint

### ASJC Scopus subject areas

- Control and Systems Engineering

### Cite this

*Proceedings of the American Control Conference*(Vol. 1, pp. 110-115). [WeA04.2]

**GODZILA : A low-resource algorithm for path planning in unknown environments.** / Krishnamurthy, P.; Khorrami, Farshad.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the American Control Conference.*vol. 1, WeA04.2, pp. 110-115, 2005 American Control Conference, ACC, Portland, OR, United States, 6/8/05.

}

TY - GEN

T1 - GODZILA

T2 - A low-resource algorithm for path planning in unknown environments

AU - Krishnamurthy, P.

AU - Khorrami, Farshad

PY - 2005

Y1 - 2005

N2 - In this paper, we propose a novel path-planning and obstacle avoidance algorithm GODZILA for navigation in unknown environments. No prior knowledge of the environment is required. The path-planning algorithm follows a purely local approach using only the current range sensor measurements at each sampling instant and requiring only a small number of stored variables in memory. No map of the environment is built during navigation. This minimizes the memory and computational requirements for implementation of the algorithm, a feature that is especially attractive for small autonomous vehicles. The algorithm utilizes three components: an optimization algorithm, a local straight-line path planner to visible targets, and random navigation. It is proved, for navigation in any finite-dimensional space, that the path-planning algorithm converges in finite time with probability 1. The performance of the algorithm is demonstrated through simulations for path-planning in two-dimensional and three-dimensional spaces. It is seen that a relatively small number of range sensor measurements is sufficient even in complex unknown environments.

AB - In this paper, we propose a novel path-planning and obstacle avoidance algorithm GODZILA for navigation in unknown environments. No prior knowledge of the environment is required. The path-planning algorithm follows a purely local approach using only the current range sensor measurements at each sampling instant and requiring only a small number of stored variables in memory. No map of the environment is built during navigation. This minimizes the memory and computational requirements for implementation of the algorithm, a feature that is especially attractive for small autonomous vehicles. The algorithm utilizes three components: an optimization algorithm, a local straight-line path planner to visible targets, and random navigation. It is proved, for navigation in any finite-dimensional space, that the path-planning algorithm converges in finite time with probability 1. The performance of the algorithm is demonstrated through simulations for path-planning in two-dimensional and three-dimensional spaces. It is seen that a relatively small number of range sensor measurements is sufficient even in complex unknown environments.

UR - http://www.scopus.com/inward/record.url?scp=23944500849&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=23944500849&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:23944500849

VL - 1

SP - 110

EP - 115

BT - Proceedings of the American Control Conference

ER -