Vector-Field Aircraft Route Rejoin Under Kinodynamic Constraints
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Solution Overview
Problem
Aerial vehicles face navigational challenges when deviating from a predefined route due to obstacles or manual control, requiring effective methods to return to the original route while adhering to kinodynamic constraints.
Innovation Solution
A data processing system generates a vector field with directional information to guide the aerial vehicle back to the route using techniques like closed-loop rapidly-exploring random trees (CL-RRT), considering constraints such as minimum turn radius and maximum rate of climb, and selects the lowest cost return path to merge with the original route.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the aerial vehicle uses a predefined route for autonomous navigation, then navigation efficiency is improved, but the system cannot handle obstacles or manual control deviations
Solution Approach 1:
The system dynamically transitions between autonomous and manual modes, and automatically generates new routes when deviations occur. The motion planner continuously adapts the flight path based on current position, obstacles, and mode of operation, making the system both efficient and adaptable.
Solution Approach 2:
The system incorporates feedback mechanisms where the current position and status are continuously monitored. When a deviation from the predefined route is detected (either manual or obstacle-induced), the system receives feedback and automatically generates a correction path to return to the original route or reach a new destination.
2Reliability
If the system generates return paths using CL-RRT technique, then the ability to find valid paths under kinodynamic constraints is improved, but computational complexity increases
Solution Approach 1:
The path planning problem is segmented into distinct phases: detecting deviation from predefined route, generating return path to route, and merging back with original route. The CL-RRT algorithm is applied specifically to the return path generation phase, while predefined routes handle the main navigation, dividing the computational burden.
Solution Approach 2:
The system uses CL-RRT to generate multiple candidate return paths and then prunes them based on kinodynamic constraints and cost functions. Rather than finding the absolute optimal path, the system finds a sufficiently good path that satisfies all constraints, reducing computational effort while maintaining reliability.
3Adaptability or versatility
If the aerial vehicle deviates from the predefined route, then obstacle avoidance or manual control is enabled, but navigation efficiency deteriorates
Solution Approach 1:
The system prepares return paths in advance when deviations are detected, rather than reacting in real-time during critical moments. By having pre-computed return paths ready, the system minimizes the time spent off-route and quickly returns to efficient predefined navigation, reducing the impact on overall navigation efficiency.
Solution Approach 2:
The system changes navigation parameters dynamically - switching from following predefined routes to executing return paths, and adjusting the cost function parameters based on the specific situation. This allows optimal navigation efficiency in normal conditions while maintaining adaptability when deviations occur.
Data Source
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AI summary
Motion steering under kino dynamic constraints is provided. A system can receive an indication to return to a route generated for autonomous navigation by the aircraft to a destination. The system can generate a vector field formed of a plurality of vectors that merge into the route. The vector field can be generated responsive to the indication to return to the route. The system can provide instructions to steer the aircraft toward the destination along the route via one or more vectors of the plurality of vectors in the vector field. The instructions can be based on a cost function applied to the vector field.