UAV-Guided Robot Motion Planning for Adaptive Workcells
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Solution Overview
Problem
Manual programming of robotic movements is tedious, time-consuming, and error-prone, and plans generated for one workcell are often incompatible with other environments, leading to inefficiencies and increased downtime as the number of workcells grows.
Innovation Solution
A system that uses on-board sensors of unmanned aerial vehicles (UAVs) to generate and adapt motion plans for robots in various workcells, automatically accounting for changes and constraints, thereby reducing manual measurement time and costs, and enabling real-time robot motion planning and predictive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual programming is used to dictate robotic movements, then the robot can perform tasks with precise control, but the process becomes tedious, time-consuming, and error-prone
Solution Approach 1:
The system enables robots to automatically adapt their motion plans by using sensor data from the workcell environment. The robot performs self-calibration and self-adjustment by comparing sensor measurements with its existing motion plan, eliminating the need for manual reprogramming when environmental changes occur.
Solution Approach 2:
The system continuously monitors the workcell environment using sensors and feeds this information back to the motion planning system. This feedback loop allows the robot to detect changes in the workcell and automatically adjust its motion plan without human intervention, reducing programming time while maintaining precision.
2Productivity
If a motion plan is manually generated for one workcell, then the robot can operate efficiently in that specific environment, but the plan cannot be used for other workcells with different physical properties
Solution Approach 1:
The system creates a universal motion planning framework that can adapt to multiple different workcell configurations. By using sensor-based environmental modeling and automatic adaptation algorithms, a single motion plan can be adjusted to work across various workcells with different physical dimensions, robots, tools, or layouts, eliminating the need for separate manual programming for each environment.
Solution Approach 2:
The motion plan is made dynamic rather than static. The system continuously updates the motion plan based on real-time sensor data from the workcell, allowing the plan to adapt to changing environmental conditions, different robot configurations, or modified workcell layouts automatically during operation.
3Productivity
If the number of workcells increases, then the system can handle more tasks, but the time and resources required for manual measurement and plan generation increase dramatically
Solution Approach 1:
The system replaces manual mechanical measurement processes with automated sensor-based measurement. UAVs equipped with sensors automatically capture workcell geometry and environmental data, eliminating the need for manual measurement tools and human operators. This substitution dramatically reduces the time and resources required for measuring and planning across multiple workcells.
Solution Approach 2:
The system creates digital copies or models of the workcell environment using sensor data from UAVs. These digital twins can be stored and reused across multiple workcells, allowing the system to rapidly generate motion plans for new workcells by referencing existing models rather than performing complete manual measurements each time.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for robot motion planning using unmanned aerial vehicles (UAVs). One of the methods includes determining that a current plan for performing a particular task with a robot requires modification; in response, generating one or more flight plans for an unmanned aerial vehicle (UAV) based on a robotic operating environment comprising the robot; obtaining, using the UAV in accordance with the one or more flight plans, a new measurement of the robotic operating environment comprising the robot; and generating, based at least on a difference between the new measurement of the robotic operating environment and a previous measurement of the robotic operating environment, a modified plan for performing the particular task with the robot.