Navigation for digital actors based on synthetic vision, memory, and learning

This paper describes an animation approach where synthetic vision is used for navigation by a digital actor. The vision is the only channel of information between the actor and its environment and offers a universal approach to pass the necessary information from the environment to an actor in the problems of path searching, obstacle avoidance, and internal knowledge representation with learning and forgetting characteristics. For the general navigation problem, we propose a local and a global approach. In the global approach, a dynamic octree serves as global 3D visual memory and allows an actor to memorize their environment that he sees and to adapt it to a changing and dynamic environment. His reasoning process allows him to find 3D paths based on his visual memory by avoiding impasses and circuits. In the local approach, low level vision based navigation reflexes, normally performed by intelligent actors, are simulated. The local navigation model uses the direct input information from his visual environment to reach goals or subgoals and to avoid unexpected obstacles.