Adaptive Obstacle Avoidance Trajectory Control for Delivery Drones using Low-Cost Nonlinear Variable Gain PID Controller

Core Concepts

This paper proposes a new nonlinear adaptive learning controller, NLVG-PID, that is low-cost and portable to different quadcopter platforms to enable effective obstacle avoidance during delivery drone missions.

Abstract

The paper presents a new nonlinear adaptive learning controller, NLVG-PID, for obstacle avoidance trajectory control (OATC) of quadcopter delivery drones. The key highlights are: The NLVG-PID controller is designed to adapt to large-angle maneuvers and load changes in UAV delivery missions. It consists of a nonlinear variable gain (NLVG) function and an extreme value search (ES) algorithm to reduce overshoot and settling time. The NLVG-PID framework is developed, where the nonlinear gain terms are used instead of fixed PID gains to improve the controller's fast control capability for errors of different sizes. An ES method is introduced to learn the optimal nonlinear PID parameters under boundary conditions, making the controller low-cost and portable to different quadcopter platforms. Simulations are conducted on a quadcopter to verify the effectiveness of the proposed NLVG-PID control scheme under two typical collision-free trajectories - a storm-type path and a Lissajous curve. The results show the NLVG-PID controller can reduce response overshoot and settling time compared to a fixed-gain PID controller.

Stats

The paper provides the following key metrics: IAE (Integrated Absolute Error), ITAE (Integrated Time and Absolute Error), and ITSE (Integrated Time-Weighted Squared Error) for the attitude and position control performance of the PID and NLVG-PID controllers. Attitude angle errors and position errors for the storm-type and Lissajous curve trajectories under PID and NLVG-PID control.

Quotes

"The NLVG-PID controller of a delivery quadcopter is designed, and the extremum seeking is introduced to learn the optimal nonlinear PID parameter under boundary conditions." "To our knowledge, this is the first NLVG-PID obstacle avoidance controller to be used in delivery drones."

Key Insights Distilled From

Low-cost adaptive obstacle avoidance trajectory control for express delivery drone

by Yanhui Zhang... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19956.pdf

Deeper Inquiries

How can the proposed NLVG-PID controller be extended to handle more complex obstacle environments, such as dynamic obstacles or obstacles with uncertain locations

The proposed NLVG-PID controller can be extended to handle more complex obstacle environments by incorporating advanced sensing and perception technologies. For dynamic obstacles, the controller can integrate real-time data from sensors like LiDAR, radar, or computer vision systems to detect and track moving obstacles. By continuously updating the obstacle information, the controller can adjust the trajectory in response to dynamic changes in the environment. Additionally, the controller can utilize predictive algorithms to anticipate the future positions of dynamic obstacles and plan trajectories accordingly. To address obstacles with uncertain locations, the NLVG-PID controller can implement probabilistic modeling techniques to estimate the potential locations of obstacles based on sensor data uncertainty. By incorporating probabilistic obstacle mapping and trajectory planning algorithms, the controller can navigate through environments with uncertain obstacles while maintaining safety and efficiency.

What are the potential challenges in implementing the NLVG-PID controller on a real-world delivery drone platform, and how can they be addressed

Implementing the NLVG-PID controller on a real-world delivery drone platform may face several challenges. One challenge is the computational complexity of the controller, especially when dealing with large-scale environments or high-speed maneuvers. This can lead to increased processing time and potential delays in decision-making. To address this, hardware optimization and parallel processing techniques can be employed to enhance the controller's efficiency. Another challenge is the integration of the controller with existing drone systems and software. Compatibility issues, communication delays, and system integration complexities may arise during the implementation process. To overcome these challenges, thorough testing, simulation, and validation procedures should be conducted to ensure seamless integration with the drone platform. Additionally, close collaboration between control engineers and drone developers is essential to address any compatibility issues effectively. Ensuring the reliability and robustness of the NLVG-PID controller in real-world scenarios is crucial. Factors such as sensor noise, environmental disturbances, and communication latency can impact the controller's performance. Implementing fault detection and isolation algorithms, redundancy mechanisms, and robust control strategies can enhance the controller's resilience to uncertainties and disturbances, thereby improving the overall safety and performance of the delivery drone platform.

Given the focus on delivery drones, how could the insights from this work be applied to improve the safety and efficiency of other autonomous aerial vehicle applications, such as search and rescue or infrastructure inspection

The insights from this work on adaptive obstacle avoidance trajectory control for delivery drones can be applied to enhance the safety and efficiency of other autonomous aerial vehicle applications, such as search and rescue or infrastructure inspection. By incorporating adaptive learning control techniques like NLVG-PID, these applications can improve their ability to navigate complex environments, avoid obstacles, and optimize trajectory planning in real-time. For search and rescue operations, autonomous aerial vehicles equipped with NLVG-PID controllers can efficiently navigate through cluttered and dynamic environments to locate and assist individuals in distress. The adaptive nature of the controller allows the drones to adapt to changing conditions and obstacles, increasing the chances of successful rescue missions. In infrastructure inspection tasks, drones can use NLVG-PID controllers to autonomously navigate around structures, inspect critical components, and detect anomalies. By optimizing trajectory planning and obstacle avoidance, these drones can enhance the efficiency and accuracy of inspection processes, reducing the need for manual intervention and improving overall inspection outcomes.