Robot Motion Trajectory Planning for Complex Phase-Based Movements
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Solution Overview
Problem
Current robot technologies struggle to accurately and efficiently perform complex and time-consuming movements, such as somersaults, due to limitations in joint output force, speed, and balance control, which restrict their ability to replicate human-like movements with high quality.
Innovation Solution
A motion control method that generates a trajectory for robots by dividing the motion into phases, determining desired poses at each node, and inputting these poses into a cost function model to optimize control parameters, allowing the robot to move accurately along the desired trajectory, incorporating techniques like model predictive control and nonlinear optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robot uses conventional motion control methods, then joint output force and speed are limited, but movement complexity and quality deteriorate
Solution Approach 1:
The motion process is divided into multiple motion phases based on contact state changes, with each phase having specific desired poses at key nodes. This segmentation allows complex movements to be broken down into manageable phases, each optimized independently while maintaining overall movement quality and reducing power requirements.
Solution Approach 2:
The method pre-calculates desired poses at key nodes for each motion phase before execution. By determining these target poses in advance and using them as reference values for cost function optimization, the system can plan optimal trajectories that minimize power consumption while achieving complex movement goals.
2Adaptability or versatility
If robot performs complex movements like somersaults, then movement quality improves, but control accuracy and balance stability deteriorate
Solution Approach 1:
The system uses cost function optimization with desired poses as reference values to continuously adjust control parameters. This feedback mechanism ensures that even during complex movements like somersaults, the robot maintains control accuracy by comparing actual states with optimized trajectories and making real-time corrections.
Solution Approach 2:
The method optimizes control parameters including joint torques and movement trajectories through cost function minimization. By dynamically adjusting these parameters based on phase-specific constraints and desired poses, the system maintains balance stability while performing high-quality complex movements.
3Adaptability or versatility
If robot executes complex trajectories, then movement richness improves, but computation time and control complexity worsen
Solution Approach 1:
By dividing the motion into phases based on contact state changes and identifying key nodes within each phase, the system reduces computation time. Each phase can be optimized independently with pre-determined desired poses, avoiding the need to compute entire complex trajectories in one step, thus reducing overall computation time while maintaining movement richness.
Solution Approach 2:
The method focuses optimization on key nodes within each motion phase rather than every point in the trajectory. By determining desired poses at critical nodes and using these as reference values for cost function optimization, the system achieves efficient computation while still generating rich, complex movements.
Data Source
AI summary
The present disclosure provides a motion control method and apparatus, and a method and apparatus for generating a trajectory of a motion. The motion control method is applied to a robot, and includes: generating, according to a type of a desired motion, at least one motion phase of a motion process and a time for each motion phase; determining, according to the at least one motion phase and the time of each motion phase, a desired pose of the robot at least one node during the motion process; inputting the desired pose as a reference value into a cost function model to obtain a trajectory of the desired motion, where the trajectory includes a pose and a control parameter of the robot at each sampling point during the motion process; and controlling the robot to move according to the trajectory of the desired motion.