Virtual Robot Route Planning for Tight Turns and Narrow Passageways
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Solution Overview
Problem
Existing robotic systems struggle to navigate tight corners and narrow passageways efficiently, often resulting in collisions or getting stuck due to unoptimized routes learned by operators.
Innovation Solution
The system generates and optimizes robotic route planning by using virtual robots to predict future positions and collisions, and applies methods like elastic banding to widen turns and avoid obstacles, allowing for safe navigation through tight spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a robot follows a pre-programmed route taught by an operator, then the robot can navigate the route autonomously, but the robot may collide with objects or get stuck because the route is not optimized for the robot's physical dimensions and properties
Solution Approach 1:
The system performs preliminary simulation of the robot's movement along the route before actual execution. Virtual robots are created to test the route in advance, identifying potential collision points and problematic segments. This allows the system to optimize the route beforehand, ensuring safety and reliability before the real robot navigates autonomously.
Solution Approach 2:
The system creates virtual copies (virtual robots) of the physical robot to simulate and test route navigation. These virtual robots replicate the physical robot's dimensions and properties, allowing safe testing and optimization of routes without risk to the actual robot or environment. The virtual simulation results are then applied to guide the real robot's autonomous navigation.
2Adaptability or versatility
If a robot learns a route by being moved manually, then the robot can acquire navigation paths, but the learned movements may not be repeatable during autonomous operation due to safety concerns
Solution Approach 1:
The system creates virtual replicas of the manually learned route and tests them through virtual robot simulations. This allows the system to verify which manually learned movements can be safely repeated autonomously and which need modification. The virtual testing ensures that only safe, repeatable movements are transferred to autonomous operation.
Solution Approach 2:
The simulation system provides feedback on the repeatability and safety of manually learned movements. By analyzing the virtual robot's performance on the learned route, the system identifies which movements can be reliably repeated and which require adjustment, enabling safe transfer of learned behaviors to autonomous operation.
3Productivity
If a robot navigates tight corners and narrow passageways using unoptimized routes, then the robot can attempt to reach destinations, but the robot's physical dimensions inhibit its ability to navigate without collisions
Solution Approach 1:
The system pre-simulates the robot's passage through tight corners and narrow passageways to identify navigation difficulties before actual traversal. By analyzing the virtual robot's performance in advance, the system can optimize turn radii, speeds, and path segments to ensure smooth navigation through constrained spaces.
Solution Approach 2:
The system modifies route parameters such as turn radius, speed, and positioning to accommodate the robot's physical dimensions. By adjusting these parameters based on virtual simulation results, the robot can navigate tight corners and narrow passageways more efficiently and safely.
Data Source
AI summary
Systems and methods for optimizing robotic route planning are disclosed in relation to autonomous navigation of sharp turns, narrow passageways, and/or a sharp turn into a narrow passageway. Robots navigating a route comprising any of the above run the risk of colliding with environment obstacles when executing these maneuvers. Accordingly, systems and methods for improving robotic route planning are necessary within the art and are disclosed herein.