Robot Motion Path Adjustment Using Vision Data and Payload Features
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Solution Overview
Problem
Existing robotic systems struggle to adapt to diverse tasks and components without significant pre-programming efforts, as they are typically designed for constrained actions and components, leading to inefficiencies and potential damage or instability when handling varied actions and components.
Innovation Solution
The implementation of computer-implemented methods that utilize vision data to adjust a robot's programmed trajectory in real-time, optimizing motion paths based on environmental data, payload properties, and end-effector information, allowing for dynamic changes in velocity, acceleration, and path curvature to enhance efficiency and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a robot is pre-programmed to repeatedly perform constrained actions on constrained components, then the robot can operate reliably in controlled environments, but the robot fails to adapt to diverse tasks and new components without significant engineering effort
Solution Approach 1:
The system dynamically adjusts the robot's motion profile between waypoints based on real-time vision data and environmental conditions. Instead of fixed pre-programmed trajectories, the system continuously modifies velocity, acceleration, and path parameters to adapt to diverse components and tasks while maintaining operational reliability through constrained adjustment ranges.
Solution Approach 2:
The system changes motion parameters (velocity, acceleration, trajectory points) based on vision data about the environment and object properties. This allows the robot to adapt to different tasks and components by modifying operational parameters rather than requiring complete re-programming, resolving the contradiction between reliability and adaptability.
2Productivity
If the robot moves at maximum velocity and acceleration to minimize cycle time, then productivity increases, but the robot may cause damage to objects or become unstable in certain situations
Solution Approach 1:
The system dynamically adjusts velocity and acceleration parameters between waypoints based on vision data about object properties and environmental conditions. This allows optimization of cycle time by increasing speed when safe, while maintaining stability by reducing speed when objects are vulnerable or conditions require caution.
Solution Approach 2:
The system uses vision data as feedback to continuously adjust motion parameters. By monitoring environmental conditions and object characteristics, the system can optimize productivity by moving faster when conditions permit while maintaining reliability by slowing down when vision data indicates potential risks.
3Reliability
If manual programming is used to generate motion profiles that minimize time while staying within safety constraints, then the robot operates safely, but significant engineering effort and computational resources are required
Solution Approach 1:
The system performs self-programming by automatically generating and adjusting motion profiles based on vision data about the environment and object properties. Instead of requiring manual engineering effort for each new task or component, the robot autonomously adapts its motion parameters, reducing programming complexity while maintaining safety through constrained adjustment ranges.
Solution Approach 2:
The system dynamically generates motion profiles on-the-fly rather than relying on static pre-programmed paths. This dynamic approach reduces the need for extensive manual programming by allowing the robot to adapt to new situations autonomously while maintaining safety through controlled parameter adjustments.
4Reliability
If the robot follows a fixed programmed trajectory, then the motion path is predictable and controllable, but the robot cannot optimize for diverse tasks and environmental conditions
Solution Approach 1:
The system uses dynamic trajectory adjustment between fixed waypoints rather than completely fixed paths. This allows optimization for diverse tasks and conditions by modifying velocity, acceleration, and intermediate points while maintaining overall motion control through the constrained waypoint framework.
Solution Approach 2:
The system changes motion parameters along the trajectory based on vision data, allowing optimization of operational efficiency while maintaining reliability through the structured waypoint approach. This resolves the contradiction by enabling parameter flexibility within a controlled framework.
Data Source
AI summary
An example computer-implemented method includes receiving, from one or more vision components in an environment, vision data that captures features of the environment, including object features of an object that is located in the environment, and prior to a robot manipulating the object: (i) determining based on the vision data, at least one first adjustment to a programmed trajectory of movement of the robot operating in the environment to perform a task of transporting the object, and (ii) determining based on the object features of the object, at least one second adjustment to the programmed trajectory of movement of the robot operating in the environment to perform the task, and causing the robot to perform the task, in accordance with the at least one first adjustment and the at least one second adjustment to the programmed trajectory of movement of the robot.