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/Territory | United States |
City | Portland, OR |
Period | 6/8/05 → 6/10/05 |
ASJC Scopus subject areas
- Control and Systems Engineering