Robot Trajectory Optimization Under Feasibility and Constraint Limits
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Solution Overview
Problem
Existing robotic systems face challenges in efficiently determining feasible trajectories for robotic devices in complex environments, particularly in warehouse and logistics operations, due to limitations in handling multiple system constraints and environmental factors.
Innovation Solution
The system employs nonlinear optimization to determine candidate trajectories for robotic devices, using computing modules like a trajectory generator and a trajectory checker to ensure feasibility by considering constraints such as joint limits, collision avoidance, and smoothness requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If nonlinear optimization is used to determine trajectories, then trajectory accuracy and constraint satisfaction are improved, but computational complexity and solving time increase
Solution Approach 1:
The trajectory optimization problem is segmented into multiple discrete time steps, where the trajectory is represented as a sequence of polynomial splines evaluated at discrete points. This allows the continuous optimization problem to be broken into manageable discrete segments that can be solved iteratively using sequential quadratic programming.
Solution Approach 2:
The system dynamically adjusts optimization parameters during the solving process. The trajectory checker dynamically evaluates feasibility and provides feedback to the trajectory generator, which then adjusts the candidate trajectory parameters iteratively until a feasible solution is found or maximum attempts are exhausted.
2Reliability
If multiple constraints are enforced during trajectory generation, then trajectory feasibility is improved, but computation time and processing load increase
Solution Approach 1:
The system performs preliminary feasibility checking by evaluating all constraints (joint limits, collision avoidance, smoothness requirements) before finalizing the trajectory. The trajectory checker预先 assesses whether a candidate trajectory satisfies all constraints, allowing early rejection of infeasible trajectories and avoiding wasted computation on invalid solutions.
Solution Approach 2:
A feedback loop is established between the trajectory checker and trajectory generator. The trajectory checker evaluates the candidate trajectory against all constraints and provides feedback on feasibility. Based on this feedback, the trajectory generator adjusts parameters and generates improved candidate trajectories, creating an iterative refinement process that converges toward feasible solutions.
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.