Autonomous Vehicle Spin Control for Sharp Turn Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating sharp turns without operator intervention, as existing systems rely on pre-mapped annotations and lack the ability to dynamically avoid obstacles by spinning in place.
Innovation Solution
A system and method that includes a spin controller, triggered by a model predictive controller, which enables an autonomous vehicle to spin in place by determining the necessary axis of rotation and path to a destination, avoiding obstacles by calculating angles and paths using occupancy grid data and adjusting rotation direction as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles rely on pre-mapped annotations for sharp turns, then the navigation system can handle known sharp turns, but the system cannot dynamically respond to obstacles or unmarked turns
Solution Approach 1:
The navigation system transitions from static pre-mapped annotations to dynamic real-time path planning. The model predictive controller continuously calculates optimal paths based on current occupancy grid data, enabling the vehicle to adapt to dynamic obstacles and unmarked turns without requiring complex manual reprogramming.
Solution Approach 2:
The system performs self-navigation through sharp turns and obstacle avoidance without operator intervention. The spin controller and model predictive controller work together to autonomously determine rotation directions, calculate paths, and execute maneuvers, making the system self-sufficient for handling complex navigation scenarios.
2Ease of operation
If the autonomous vehicle spins in place to navigate sharp turns, then the vehicle can reach destinations requiring sharp turns, but the control system complexity increases
Solution Approach 1:
The control system is divided into distinct functional modules: the model predictive controller for path planning, the spin controller for execution, and the occupancy grid processor for obstacle detection. This segmentation allows each component to handle specific tasks, simplifying the overall control architecture while enabling complex spin maneuvers.
Solution Approach 2:
The spin controller acts as an intermediary between the model predictive controller's path planning and the vehicle's physical execution. It translates high-level navigation commands into specific wheel control signals, managing the complexity of spin maneuvers through a dedicated control layer.
Data Source
AI summary
Spin controller that can enable an autonomous vehicle (AV) to spin in place. A model predictive controller (MPC) can trigger the spin controller any time the MPC determines that a spin in place is required. In some configurations, the spin controller can move the AV to within 5° of the destination point. Spin controller can determine spin method based on the configuration of the AV, calculate an optimum turning path based at least on device mode and obstacles, and can enable the AV to spin in place.