A grasp‐based motion planning algorithm for character animation

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-togoal 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.

Planning biped locomotion using motion capture data and probabilistic roadmaps

“…There have been two major streams of randomized techniques for path planning: randomized path planning with potential fields [Barraquand and Latombe 1991;Kalisiak and van de Panne 2000;Koga et al 1994] and probabilistic path planning with roadmaps [Barraquand et al 1997;Kavraki and Latombe 1994;Kavraki et al 1995;Overmars and Svestka 1994]. A randomized path planning scheme employs a potential field to guide the search for a path to the goal while avoiding the obstacles.…”

“…To coordinate multiple robots, Svestka and Overmars [1998] combined roadmaps for multiple robots into a roadmap for the single composite robot. Kalisiak and van de Panne [2000] presented a grasp-based motion planning algorithm in a constrained 2D environment with designated handholds and footholds. Kindel et al [2000] developed a path planner for a robot with dynamic constraints and verified its effectiveness both in real and simulated environments.…”

Path Planning for a Statically Stable Biped Robot Using PRM and Reinforcement Learning

“…Recently online techniques have been developed for generating trajectories for biped walk, based on footstep placement [14]. Some work in virtual reality addressed the problem of foot placement of virtual characters, and developed motion planners [10]. Research dealing with path generation and obstacle avoidance for statically stable biped robots is not available in the robotics literature.…”

A Grasp-based Motion Planning Algorithm for Character Animation

“…Table 1 describes which heuristics are used for which modes of locomotion. The details of these heuristics can be found in [12].…”

“…Even though these transitions return to the same locomotion mode, they provide the necessary regrasping operations which allow the character to keep advancing using that particular mode. The full details of the locomotion FSM are available in [12].…”