Current motion planners, in general, can neither "see" the world around them, nor learn from experience. That is, their reliance on collision tests as the only means of sensing the environment yields a tactile, myopic perception of the world. Such short-sightedness greatly limits any potential for detection, learning, or reasoning about frequently encountered situations. As a result, it is common for current planners to solve and re-solve the same general scenarios over and over, each time none the wiser. We thus propose a general approach for motion planning, as well as a specific illustrative algorithm, in which local sensory information, in conjunction with prior accumulated experience, are exploited to improve planner performance. Our approach relies on learning viability models for the agent's "perceptual space", and the use thereof to direct planning effort. Experiments with three test agents show significant speedups and skill-transfer between environments.

This paper proposes a new variation of the RRT planner which demonstrates good performance on both loosely-constrained and highly-constrained environments. The key to the planner is an implicit flood-fill-like mechanism, a technique that is well suited to escaping local minima in highly constrained problems. We show sample results for a variety of problems and environments, and discuss future improvements

A numerical method is proposed for the constraint of the state of a dynamical system such that it cannot enter a predefined failure region. The proposed approach to this viability problem involves an explicit numerical approximation of a viability envelope, coupled with a practical strategy for enforcing containment that is based upon a predictive look-ahead strategy. The approach can be applied to achieve automated "intervention when necessary" to enforce system safety at interactive rates. Applications are shown to several low-dimensional systems, including steering control of a vehicle constrained to a given environment geometry.

Abstract The design of autonomous characters capable of planning their own motions continues to be a challenge for computer animation. We present a novel kinematic motion‐planning algorithm for character animation which addresses some of the outstanding problems. The problem domain for our algorithm is as follows: given a constrained environment with designated handholds and footholds, plan a motion through this space towards some desired goal. Our algorithm is based on a stochastic search procedure which is guided by a combination of geometric constraints, posture heuristics, and distance‐to‐goal metrics. The method provides a single framework for the use of multiple modes of locomotion in planning motions through these constrained, unstructured environments. We illustrate our results with demonstrations of a human character using walking, swinging, climbing, and crawling in order to navigate through various obstacle courses. Copyright © 2001 John Wiley & Sons, Ltd.