Virtual Effector Trajectory Planning With Relaxed Contact Constraints
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Solution Overview
Problem
Current multi-contact motion planning methods for virtual characters are inefficient due to the separation of contact sequence and pose estimation steps, leading to suboptimal trajectory optimization and potential physical limitations such as joint overload or instability in robots.
Innovation Solution
A method that acquires a sequence of postures with contact points and kinematic poses, modifies constraint topologies, performs constraint relaxation, and applies a trajectory generation algorithm to generate an effector trajectory, allowing for inverse kinematics and real-time control signal output for improved motion planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If multi-contact motion planning is divided into separate subtasks (contact sequence search, pose estimation, trajectory optimization), then computational complexity of each individual step is reduced, but interdependencies between steps are lost leading to suboptimal solutions and increased overall computation time
Solution Approach 1:
The patent merges the previously separate subtasks (contact sequence search, pose estimation, and trajectory optimization) into a unified multi-contact motion planning framework. This integration allows the system to consider interdependencies between all steps simultaneously, optimizing the entire motion planning process rather than treating each step independently, thereby reducing overall computation time and improving solution quality.
2Ease of operation
If contact candidates are determined first and then used for pose estimation, then the search space for contacts is managed efficiently, but the contact candidates cannot be adapted during subsequent pose estimation and trajectory optimization steps
Solution Approach 1:
The patent implements a dynamic approach where contact candidates are not fixed after the initial search but are continuously adapted and refined during the pose estimation and trajectory optimization steps. This dynamic adaptation allows the system to adjust contact candidates based on emerging information from subsequent optimization steps, improving the overall quality of the motion plan while still maintaining computational efficiency through the initial structured search.
Data Source
AI summary
A method and simulation system for controlling at least one effector trajectory for solving a predefined task by at least one virtual effector. The method includes acquiring a sequence of postures to modify at least one of a contact constraint topology and an object constraint topology, generating a set of constraint equations based on at least one of the modified contact constraint topology and the modified object constraint topology, performing constraint relaxation on the generated set of constraint equations to generate a task description including the relaxed set of constraint equations, generating the effector trajectory by applying a trajectory generation algorithm on the generated task description, performing an inverse kinematics algorithm on the generated effector trajectory for generating a control signal, outputting the control signal to an output device, and generating, by the output device, image information displaying the effector trajectory of the virtual effector based on the control signal.