Aerial Vehicle Trajectory Planning with Seeded RRT Obstacle Avoidance
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Solution Overview
Problem
Current trajectory planning methods for aerial vehicles are inefficient due to frequent updates, high computation time, and reliability issues with obstacle avoidance.
Innovation Solution
A system and method that uses a processor to calculate path trajectory segments, identify seed waypoints, and execute a random searching algorithm, such as the rapidly exploring random tree algorithm, to generate navigational commands for obstacle avoidance, incorporating sensor data from LIDAR, Radar, or echolocation systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If current trajectory planning methods link together approximations of short trajectories by geometric arcs and line segments, then the trajectory can be generated, but the computation time is too long and frequent updates are needed
Solution Approach 1:
The system pre-calculates and stores a database of optimal trajectory segments (Dubins paths) for various start and end conditions before actual navigation. When planning is needed, these pre-computed segments are retrieved and combined rather than calculating from scratch, significantly reducing real-time computation time and update frequency requirements
Solution Approach 2:
The complete trajectory is divided into multiple short segments, each connecting adjacent waypoints. Each segment is independently planned using the pre-computed Dubins path database, allowing parallel processing and reducing overall computation time compared to calculating the entire trajectory as one continuous path
2Reliability
If current trajectory planning methods use geometric arcs and line segments, then trajectories can be generated, but obstacle avoidance reliability is insufficient
Solution Approach 1:
The system dynamically adjusts trajectory generation by integrating real-time sensor data about obstacles with the pre-computed Dubins path database. When obstacles are detected, the system selectively modifies or replaces affected trajectory segments while maintaining continuity, achieving reliable obstacle avoidance through adaptive rather than static planning
Solution Approach 2:
A trajectory optimization module acts as an intermediary between the simple Dubins path generator and the final trajectory output. This module incorporates obstacle information and refines the basic geometric paths into reliable obstacle-avoiding trajectories, separating the complexity of obstacle handling from the fundamental path generation
3Measurement precision
If iterative calculation of trajectory segments is performed to account for changing velocities and kinematic limits, then navigation accuracy is improved, but computation time increases
Solution Approach 1:
The Dubins path database is pre-computed with various velocity and kinematic constraint scenarios during system initialization. This allows the real-time system to retrieve accurate trajectories matching current vehicle conditions without performing iterative calculations, achieving both high precision and fast computation
Solution Approach 2:
The system uses parameterized Dubins path formulations that directly incorporate velocity, acceleration, and turning rate constraints as input parameters. By changing these parameters based on current vehicle state rather than iteratively adjusting the path, the system achieves accurate trajectories that respect kinematic limits in constant time
Data Source
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AI summary
The present disclosure is directed to systems and methods for trajectory and route planning including obstacle detection and avoidance for an aerial vehicle. For example, an aerial vehicle's flight control system may include a trajectory planner that may use short segments calculated using an iterative Dubins path to find a first path between a start point and an end point that does not avoid obstacles. Then the trajectory planner may use a rapidly exploring random tree algorithm that uses points along the first path as seed points to find a trajectory or route between the start point and end point that avoids known or detected obstacles.