Adaptive animât navigation based on a flexibility model for the environment

In this paper we propose a uniied framework for local animat navigation and position local-ization. This framework is based on the use of exibility maps of the environment. Flexibility maps contain both the current knowledge of the animat about the positions of the objects in its environment as well as the information required to calculate its future path. The major beneet of using exibility maps is that several navigation tasks can be incorporated elegantly into a single framework. In addition, we found that the simple control laws derived from this approach generate paths that look very natural in realistic circumstances, adding to the biological plausibil-ity of the approach. We will show that a exibil-ity model increases the adaptivity of an animat to changes in its environment by allowing the use of families of paths instead of single paths, and by allowing the derivation of entirely new paths from the animat's memory of previously learned paths.