Aerial Vehicle Trajectory Planning for Fast 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 that calculates path trajectory segments using sensor data, identifies seed waypoints, and employs a random searching algorithm, such as the rapidly exploring random tree algorithm, to generate obstacle-avoiding trajectories, while iteratively adjusting for changing velocities and kinematic limits.
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 required
Solution Approach 1:
The trajectory is divided into multiple short segments between waypoints. Each segment is independently planned using a simplified model, and the complete trajectory is assembled by linking these segments together. This segmentation allows for faster computation of individual segments while maintaining overall trajectory accuracy.
Solution Approach 2:
The system pre-calculates and stores trajectory segments between waypoints before actual navigation. When the aerial vehicle needs to navigate between two points, the pre-computed segments are retrieved and linked together, avoiding the need to perform complex real-time calculations during actual operation.
2Reliability
If current trajectory planning methods are used, then a trajectory can be generated, but reliability issues with obstacle avoidance occur
Solution Approach 1:
The obstacle avoidance mechanism operates in an additional dimension by introducing a vertical offset to the trajectory segments. When an obstacle is detected in the horizontal plane, the system generates alternative segments that route around the obstacle by utilizing the vertical dimension (z-coordinate), thereby achieving reliable obstacle avoidance without increasing horizontal path complexity.
Solution Approach 2:
The system introduces intermediate waypoints as mediators between the start and end points when obstacles are present. These intermediate points serve as stepping stones that allow the trajectory to indirectly navigate around obstacles, breaking down the complex obstacle avoidance problem into simpler sub-segments.
3Speed
If frequent updates are performed in current trajectory planning methods, then the trajectory can be tracked, but the computation time increases
Solution Approach 1:
Trajectory segments are pre-computed and stored in memory before the aerial vehicle begins navigation. When updates are required during flight, the system simply retrieves pre-calculated segments and adjusts parameters, rather than performing full trajectory recalculations, thereby enabling frequent updates with minimal computation time.
Solution Approach 2:
The trajectory is segmented into independent sections that can be updated individually. When an update is required, only the affected segments near the current position need to be recalculated or adjusted, while the rest of the pre-computed segments remain valid, reducing the overall computation burden for frequent updates.
Data Source
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.