My name is Lei Zheng and I’m a Phd Candidate specializing in Robotics

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Skills

I AM REALLY GOOD AT THE FOLLOWING TECHNICAL SKILLS

Analysis

90%

Leadership

80%

Photography

70%

Research

95%

Experience

PREVIOUS ASSOCIATIONS THAT HELPED TO GATHER EXPERIENCE

 
 
 
 
 

Senior Robotics Engineer

XAG

Jul 2021 – Jul 2022 GuangZhou

Develop algorithms to bring drones, robots, autopilot, artificial intelligence, and Internet-of-things into the world of agricultural production.

Create a smart agriculture ecosystem that leads us into the era of Agriculture 4.0 characterized by automation, precision, and efficiency to provide the world with sufficient, diversified, and safe food.

Responsibilities include:

  • Flight data analysis
  • Developing visualization environment for mapping and planning algorithms
  • Developing state-of-the-art motion control and trajectory optimization software and integrating software into test vehicles
  • Creating highly reliable, maintainable, and testable code (modern C++) by applying best-practice software engineering methods, including code reviews, design guidelines, refactoring, unit and regression testing
  • Contributing to agricultural production projects, including requirement engineering, validation, and verification
  • writing patents and engaging with the scientific community

Accomplish­ments

PREVIOUS ASSOCIATIONS THAT HELPED TO GATHER EXPERIENCE

Risk Aware and Robust Nonlinear Planning

Jul 2022
Advanced Probabilistic and Robust Optimization-Based Algorithms for Control and Safety Verification of Nonlinear Uncertain Autonomous Systems

Pattern Recognition and Machine Learning

Feb 2019 – Jul 2019
Fundamental concepts, theories, and algorithms for pattern recognition and machine learning,which are used in computer vision, speech recognition, data mining, statistics, information retrieval, and bioinformatics.Topics include: Bayesian decision theory, parametric and non-parametric learning, data clustering, component analysis,boosting techniques, support vector machine, and deep learning with neural networks.

Recent Posts

MY FANS DON’T FEEL LIKE I HOLD ANYTHING BACK FROM THEM

1 min read
Present a real-time fail-operational controller for autonomous driving in the presence of time-varying environmental disturbances. This controller is designed to guide autonomous vehicles back to a predefined safe state asymptotically, while upholding task efficiency.

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Present a real-time parallel trajectory optimization method for the autonomous vehicles to achieve high travel efficiency in dynamic and congested environments.

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We propose a learning-based tracking control scheme based on a feedback linearization controller and Gaussian Processes.

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Propose a new learning-based framework based on incremental bayesian learning and control theory.

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A learning-based safety-preserving cascaded quadratic programming control policy for safe trajectory tracking under wind disturbances.

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Time reallocation for trajectory replanning.
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Projects

ALL THINGS ARE DIFFICULT BEFORE THEY ARE EASY

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Barrier-Enhanced Homotopic Parallel Trajectory Optimization for Safety-Critical Autonomous Driving

Enforcing safety while preventing overly conservative behaviors is essential for autonomous vehicles to achieve high task performance. In this project, we propose a barrier-enhanced homotopic parallel trajectory optimization approach with over-relaxed alternating direction method of multipliers for real-time integrated decision-making and planning in cluttered driving environments.

Multi-Modal Spatiotemporal Receding Horizon Planning for Autonomous Driving

This project presents a cutting-edge approach for safe and efficient autonomous driving in dense traffic scenarios. Our proposed Spatiotemporal Receding Horizon Control (ST-RHC) scheme generates dynamically feasible and energy-efficient trajectories in real-time, enabling vehicles to accurately perform complex driving tasks. The algorithm employs receding horizon optimization and iterative parallel methods to design a trajectory tree that optimizes planning and ensures proactive interaction to avoid accidents. We have implemented our algorithms on an autonomous car, successfully achieving vehicle following, lane changing, overtaking, and cruise driving in dense traffic flow simulations based on ROS2.

High-speed flight

Once received return control signal, the drone must generate smooth trajectories in real-time to avoid collision and be close to the reference spraying path to realize high-performance precision spraying in precision farming.

multi-agent

Multi-agent in precision farming.

Safe Learning

Designed efficient incremental Gaussian Processes accounting for airflow uncertainties. The wind disturbance caused by the external environment is estimated to improve flight safety and control stability in cluttered environments. Following that, the estimated wind disturbance is used to compensate for the associated control error.

Connected Cruise Control in Mixed Traffic Flow

For safety-critical vehicles in mixed traffic flow where most vehicles are human-driven, each autonomous vehicle keeps tracking its front vehicle at a desired constant speed while maintaining a safe following distance with it in normal situations. However, when a vehicle decelerates urgently in unexpected situations, the vehicle behind has to reduce its speed to avoid collision with the front vehicle. In these cases, there exists a conflict between safety and stable high-performance tracking. For safety-critical autonomous vehicles, safety must not be violated and the tracking errors should be kept as small as possible.

Safety-critical Agricultural Drone

To achieve high-speed autonomous flight of aerial vehicles and realize high-performance precision spraying in precision farming. Trajectories must be generated in real-time to avoid collision and be close to the reference spraying path. Because of the high navigation speed, short sensing range, and unknown environments, response time is extremely limited, making generating high-quality trajectories a significant challenge.

Gallery

I have done many projects, here is some featured image

Featured Publications

In IEEE RAL (Presentation at IEEE ICRA 2022)
We propose a learning-based tracking control scheme based on a feedback linearization controller in which uncertain disturbances are approximated online using Gaussian Processes. Using the predicted distribution of disturbances given by GPs, a Control Lyapunov Function and Control Barrier Function based Quadratic Program is applied, with which probabilistic stability and safety are guaranteed.
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In EAAI
A safe and learning-based control framework for model predictive control is proposed to optimize nonlinear systems with a non-differentiable objective function under uncertain environmental disturbances. The control framework integrates a learning-based MPC with an auxiliary controller in a way of minimal intervention. The proposed approach provides a promising solution to the propagation of stochastic state distributions in MPC, allowing for improved task performance while ensuring high levels of safety.
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In ACC
Enforcing safety on precise trajectory tracking is critical for aerial robotics subject to wind disturbances. In this paper, we present a learning-based safety-preserving cascaded quadratic programming control for safe trajectory tracking under wind disturbances.
PDF Video DOI

In IEEE RAL (Presentation at IEEE ICRA 2021)
A learning-based MPFC control paradigm for nonlinear systems under uncertain disturbances, coupling a high-level model predictive path following controller for proactivity with a low-level learning-based feedback linearization controller for adaptivity. Following that, nonlinear systems can rapidly rejoin their reference trajectory after sudden wind disturbances with stability guarantees.
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In WCSP
A learning-based safety-stability-driven control algorithm is presented to guarantee the safety and tracking stability for nonlinear safety-critical systems subject to control input constraints under model uncertainties.
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Recent Publications

Some publications i have recently published

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Popular Topics

academia autonomous driving learning robotics

Contact

Connect with me

  • The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
  • Monday 10:00 to 13:00 Wednesday 09:00 to 10:00
  • Book an appointment