Robot Motion Planning With Workspace Footprints for Collision Avoidance
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Solution Overview
Problem
Current robotics planning methods require extensive manual programming, are time-consuming, and prone to errors, and fail to account for real-time physical constraints of workcells and sensor-based interactions, leading to inefficiencies and potential collisions.
Innovation Solution
A system that generates and modifies motion plans in real-time using sensor data to optimize robot movements within a predefined workspace footprint, avoiding collisions and enabling human-robot collaboration and multi-robot coordination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual programming is used to dictate robotic movements, then the robot can perform skills with precise control, but the programming process becomes tedious, time-consuming, and error-prone
Solution Approach 1:
The system enables robots to autonomously generate and modify their own motion plans using sensor data and planning algorithms, eliminating the need for extensive manual programming. The robot performs self-service by automatically adapting its movement schedules based on real-time workcell conditions while maintaining precise control
Solution Approach 2:
The motion planning system dynamically adjusts robot schedules and trajectories in real-time based on sensor feedback and changing workcell conditions. This dynamic approach replaces static manual programming with adaptive automated planning that maintains precision while reducing programming time
2Manufacturing precision
If manual programming is used for one workcell, then the robot can perform skills accurately, but the schedule cannot be used for other workcells with different physical properties
Solution Approach 1:
The motion planning system is designed to be universal across different workcells by automatically adapting to various physical properties, robot configurations, and workcell dimensions. The system generates workcell-specific schedules using standardized algorithms that can handle diverse scenarios, making the planning system versatile while maintaining accurate skill execution
Solution Approach 2:
The system adapts to different workcells by dynamically adjusting planning parameters such as workspace footprints, collision constraints, and motion boundaries based on the specific physical properties of each workcell. This parameter adaptation enables the same planning system to accurately control robots across multiple different workcell configurations
3Adaptability or versatility
If sensor-based skills are planned in real-time, then the robot can adapt to changing conditions, but the planning system must account for physical constraints and avoid collisions
Solution Approach 1:
The planning system is segmented into modular components including local trajectory generation, global path planning, collision detection, and constraint satisfaction modules. This segmentation allows real-time adaptation through sensor feedback while managing complexity through organized, reusable planning primitives and hierarchical decision-making
Solution Approach 2:
The system uses continuous sensor feedback to update motion plans in real-time, creating a closed-loop planning system that adapts to changing conditions. The feedback mechanism incorporates sensor data about workcell state, robot position, and potential collisions into the planning process, enabling real-time adaptation without overwhelming system complexity
4Productivity
If the robot is allowed to move freely within the workcell, then the robot can perform skills efficiently, but it may interfere with other objects or robots
Solution Approach 1:
The system defines local workspace footprints that specify safe movement boundaries for each robot based on its position, task requirements, and surrounding obstacles. These localized constraints allow robots to move efficiently within their designated zones while automatically preventing interference with other robots and objects through spatial separation
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling robotic movements. One of the methods includes receiving, for a robot, an initial plan specifying a path and a local trajectory; receiving an updated observation of an environment of the robot; generating an initial modified local trajectory for the robot based on the updated observation in the environment of the robot; repeatedly following the initial modified local trajectory for the robot while generating a modified global path for the robot, comprising: obtaining data representing a workspace footprint for the robot, the workspace footprint defining a volume for a workspace of the robot, and generating the modified global path to avoid causing the robot to cross a boundary of the volume defined by the workspace footprint; and causing the robot to follow the modified global path for the robot.