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Animated human agents with motion planning capability for 3D-space postural goals

In this paper, we present a rule-based heuristic method of motion planning for an animated human agent with massively redundant degrees of freedom. It constructs motion plans to achieve 3D-space goals of control points on the body, e.g. a hand, while avoiding collisions. Like the artificial potential field approach, the method performs motion decisions in 3D world space rather than in joint space. To handle the massively redundant degrees of freedom, we use a qualitative kinematic model, which specifies motions of body parts and dependencies among them, without specifying the exact distance parameters. This model helps the body select appropriate primitive motions for given goals of control points more globally than does the gradient vector of an artificial potential field of the body. The method simulates (in imagination) the suggested plan to find whether some body parts hit objects, and how much they penetrate the objects. Based on this simulated collision information, the method suggests intermediate goals of the collision body parts. A subplan to achieve these intermediate goals is again postulated by using the qualitative kinematic model. This explicit reasoning helps alleviate the basic cause of local minima in the potential field approach, namely, conflicts between attractive potential fields due to goal positions of control points and repulsive potential fields due to obstacles.