Aircraft Route Rejoin Steering Under Kinodynamic Constraints
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Solution Overview
Problem
Aerial vehicles face navigational challenges when deviations occur due to obstacles or manual flight control deviations from predefined routes, requiring effective methods to determine and navigate a return path while adhering to kinodynamic constraints.
Innovation Solution
The system generates a vector field around the aerial vehicle's environment, using techniques like closed-loop rapidly-exploring random trees (CL-RRT) to create return paths that merge with the original route, while considering kinodynamic constraints such as turn radius, climb rate, and thrust limitations.
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 adapt when obstacles or manual deviations occur
Solution Approach 1:
The navigation system dynamically transitions from static predefined routes to dynamic vector field guidance when deviations are detected. The system maintains the efficiency of predefined routes during normal operation but automatically adapts to new conditions by generating real-time return paths based on current vehicle state and kinodynamic constraints.
Solution Approach 2:
The system continuously monitors vehicle position and route adherence, detecting deviations through feedback mechanisms. When deviations are detected, the system triggers recalculation of return paths using CL-RRT algorithms, creating a closed-loop control system that adapts navigation based on actual performance and environmental conditions.
2Reliability
If the system generates return paths using CL-RRT algorithm, then the ability to handle kinodynamic constraints is improved, but computational complexity increases
Solution Approach 1:
The navigation problem is segmented into distinct phases: predefined route following, deviation detection, and return path calculation. The CL-RRT algorithm is only activated when needed for return path generation, rather than continuously, reducing overall computational burden while maintaining reliability when constraints must be satisfied.
Solution Approach 2:
The system changes computational parameters dynamically - using simple route following parameters during normal operation and switching to complex CL-RRT parameters only when deviations occur. This parameter switching allows the system to maintain high reliability for constraint satisfaction while minimizing average computational complexity.
3Object-affected harmful factors
If the aerial vehicle deviates from the predefined route, then obstacle avoidance is achieved, but navigation precision deteriorates
Solution Approach 1:
Instead of allowing random deviations and then correcting, the system inverts the approach by generating structured return paths that deliberately guide the vehicle back to the predefined route. The vector field is constructed to point toward route recovery, ensuring that even when deviations occur for obstacle avoidance, the return journey maintains high navigation precision.
Solution Approach 2:
The vector field acts as an intermediary between the deviation state and the predefined route. Rather than directly commanding the vehicle to return, the system generates a field of directional vectors that guide the vehicle back along optimal paths that respect kinodynamic constraints, preserving navigation precision during the recovery process.
Data Source
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.