Robotic Route Planning for Tight Turns and Narrow Passageways
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Solution Overview
Problem
Robotic systems face challenges in navigating tight corners and narrow passageways due to unoptimized routes learned by operators, which can result in collisions or getting stuck, as these routes do not consider the robot's physical dimensions and current state, such as turn radius, wheel state, and sensor data.
Innovation Solution
The method involves generating and optimizing routes by using virtual robots to predict future positions and collisions, applying elastic banding to segments of the route to ensure safe navigation through tight corners and narrow passageways, and modifying state points along the route to account for changes, allowing the robot to avoid collisions and optimize its path in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a route is provided to a robot without considering its physical dimensions and current state, then the route can be simple and easy to implement, but the robot may collide with objects or get stuck while traveling the route
Solution Approach 1:
The system performs preliminary optimization of the route before the robot executes it. The optimization process considers the robot's physical dimensions, turn radius, and current state to pre-calculate safe navigation paths, avoiding collisions and stuck situations before they occur during actual travel
Solution Approach 2:
The route optimization is dynamic and adapts to the robot's current state. As the robot travels along the route, the system continuously adjusts the path based on real-time sensor data, wheel state, and environmental changes, making the route flexible rather than static
2Productivity
If a robot learns a route by being moved manually, then the route can be quickly obtained, but some movements may not be repeatable during autonomous operation due to safety concerns
Solution Approach 1:
The system creates a virtual model (copy) of the robot and uses it to simulate and test route segments. By visualizing future states and positions of virtual robots, the system can evaluate whether manually learned movements are safe and repeatable for autonomous operation without physically testing them on the actual robot
Solution Approach 2:
The system performs preliminary safety validation by simulating the route with virtual robots before the actual robot executes the movements. This preliminary anti-action prevents unsafe movements from being executed, blocking potential collisions or unsafe maneuvers before they happen
3Ease of operation
If the robot navigates tight corners and narrow passageways without route optimization, then the navigation process is simple, but the robot's physical dimensions inhibit its ability to navigate around corners and narrow passageways without collisions
Solution Approach 1:
The system applies local optimization to specific segments of the route that contain tight corners or narrow passageways. Instead of optimizing the entire route uniformly, it identifies problematic areas and applies specialized optimization techniques only where needed, considering the robot's physical dimensions and turn radius in those local regions
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.