Nonlinear Robot Trajectory Planning Under Joint and Collision Constraints
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Solution Overview
Problem
Current robotic systems in warehouse and logistics operations are inefficient and inflexible due to loosely integrated mobile manipulator designs, which limit their ability to perform complex tasks and require multiple specialized robots or human intervention, leading to suboptimal speed and accuracy.
Innovation Solution
A highly integrated mobile manipulator robot with system-level mechanical design and holistic control strategies between the manipulator and mobile base, utilizing nonlinear optimization to determine feasible trajectories that account for joint limits, collision avoidance, and environmental constraints, enabling efficient and dynamic motion planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If loosely integrated mobile manipulator designs are used, then device complexity is reduced and ease of manufacture is improved, but productivity and task performance efficiency deteriorate
Solution Approach 1:
The patent merges the mobile base and manipulator into a highly integrated mobile manipulator system with unified control architecture. The nonlinear optimization framework jointly plans trajectories for both the mobile base and manipulator, enabling coordinated motion that improves task execution efficiency and productivity while maintaining manufacturability through modular component design.
Solution Approach 2:
The system implements dynamic trajectory generation using nonlinear optimization that adapts to real-time constraints and task requirements. The control strategy dynamically adjusts motion parameters, collision avoidance boundaries, and joint limits during operation, enabling flexible and efficient task performance without sacrificing ease of manufacture.
2Adaptability or versatility
If multiple specialized robots are deployed, then task coverage and adaptability are improved, but device complexity and system integration difficulty increase
Solution Approach 1:
The highly integrated mobile manipulator is designed as a universal platform capable of performing multiple tasks through coordinated motion of its mobile base and manipulator. The nonlinear optimization framework enables the system to adapt to various task requirements by adjusting trajectory parameters and constraints, providing versatility without requiring multiple specialized robots.
Solution Approach 2:
The system segments the control framework into modular components: trajectory generation module, collision avoidance module, and execution module. This segmentation allows the complex system to be managed through independent, interchangeable modules that can be configured for different tasks, reducing overall system complexity while maintaining adaptability.
3Reliability
If human intervention is required, then task accuracy and reliability are improved, but productivity and operational speed deteriorate
Solution Approach 1:
The mobile manipulator system performs self-verification of trajectory feasibility through integrated collision avoidance algorithms and constraint checking. The nonlinear optimization framework automatically adjusts trajectories to satisfy joint limits, avoid collisions, and meet task requirements without human intervention, enabling autonomous operation that maintains reliability while improving productivity.
Solution Approach 2:
The system implements feedback mechanisms where trajectory execution results are continuously monitored and fed back to the optimization framework. This feedback loop enables automatic correction of trajectory deviations and real-time adaptation to unexpected conditions, ensuring task reliability while maintaining high operational speed through autonomous decision-making.
4Device complexity
If conventional motion planning methods are used, then computational simplicity is maintained, but trajectory accuracy and constraint satisfaction deteriorate
Solution Approach 1:
The system employs nonlinear optimization to dynamically adjust trajectory parameters including position, velocity, acceleration, and timing to satisfy complex constraints such as joint limits, collision avoidance boundaries, and task-specific requirements. This parameter optimization approach achieves high trajectory accuracy while managing computational complexity through efficient numerical solvers.
Solution Approach 2:
The trajectory generation module performs preliminary feasibility analysis and constraint checking before trajectory execution. By pre-computing collision avoidance boundaries and validating joint limit compliance, the system ensures trajectory accuracy and constraint satisfaction are met before motion begins, reducing the need for complex real-time corrections during execution.
Data Source
AI summary
Systems and methods for determining movement of a robot are provided. A computing system of the robot receives information including an initial state of the robot and a goal state of the robot. The computing system determines, using nonlinear optimization, a candidate trajectory for the robot to move from the initial state to the goal state. The computing system determines whether the candidate trajectory is feasible. If the candidate trajectory is feasible, the computing system provides the candidate trajectory to a motion control module of the robot. If the candidate trajectory is not feasible, the computing system determines, using nonlinear optimization, a different candidate trajectory for the robot to move from the initial state to the goal state, the nonlinear optimization using one or more changed parameters.