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

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News

 
 
 
 
 

🔥 News

Jul 2023 – Present
  • 2025.02: 🎉 I have started as a visiting scholar with Prof. Changliu Liu at the Robotics Institute, Carnegie Mellon University.
  • 2025.02: 🎉 1 paper is submitted to IROS 2025.
  • 2025.02: 🎉🎉 1 paper is accepted by IEEE Transactions on Vehicular Technology. Congrats to Zengqi!
  • 2025.02: 🎉 1 paper is submitted to IEEE Transactions on Cybernetics.
  • 2024.01: 🎉 1 paper is submitted to IEEE CDC 2025.
  • 2024.01: 🎉 1 paper is submitted to IEEE Transactions on Intelligent Transportation Systems.
  • 2024.11: 🎉🎉 1 paper is accepted by IEEE Transactions on Vehicular Technology. Congrats to Wenru!
  • 2024.11: 🎉 🎉 My paper “Barrier-Enhanced Parallel Homotopic Trajectory Optimization for Safety-Critical Autonomous Driving” has been accepted by IEEE Transactions on Intelligent Transportation Systems.
  • 2024.11: 🎉 1 invention patent is issued.
  • 2024.09: 🎉 1 paper is submitted to IEEE Transactions on Intelligent Transportation Systems.
  • 2024.06: 🎉🎉 1 paper is accepted by IROS 2024. Congrats to Zengqi!
  • 2024.04: 🎉🎉 My paper “Spatiotemporal Receding Horizon Control with Proactive Interaction Towards Autonomous Driving in Dense Traffic” has been accepted by IEEE Transactions on Intelligent Vehicles.
  • 2024.04: 🎉🎉 I have passed my PhD qualifying exam.
  • 2024.03: 🎉 2 papers are Submitted to IEEE Transactions on Vehicular Technology.
  • 2023.02: 🎉🎉 1 paper is accepted by ECC 2024.
  • 2023.07: 🎉🎉 2 papers are accepted by ITSC 2024.

Experience

Working 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

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.

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

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

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

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

1 min read
Time reallocation for trajectory replanning.
See all posts

Projects

ALL THINGS ARE DIFFICULT BEFORE THEY ARE EASY

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Occlusion-Aware Contingency Safety-Critical Planning for Autonomous Vehicles

This research introduces a occlusion-aware contingency safety-critical planning approach for safety-critical autonomous vehicles. Leveraging reachability analysis for risk assessment, forward reachable sets of occluded phantom obstacles are computed. The occlusion-aware contingency planner then constructs multiple locally optimal trajectory branches (each tailored to different risk scenarios), and a shared consensus trunk is generated to ensure smooth transitions and motion consistency.

Safe and Real-Time Consistent Planning for Autonomous Vehicles in Partially Observed Environments via Parallel Consensus Optimization

This research introduces a consistent parallel trajectory optimization (CPTO) approach for real-time, consistent, and safe trajectory planning for autonomous driving in partially observed environments. The CPTO framework introduces a consensus safety barrier module, ensuring that each generated trajectory maintains a consistent and safe segment, even when faced with varying levels of obstacle detection accuracy. We validate our CPTO framework through extensive comparisons with state-of-the-art baselines across multiple driving tasks in partially observable environments. Our results demonstrate improved safety and consistency using both synthetic and real-world traffic datasets.

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. Through a series of experiments, the proposed development demonstrates improved task accuracy, stability, and consistency in various traffic scenarios using synthetic and real-world traffic datasets.

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.

Moments

Featured Publications

This research seamlessly integrates discrete decision-making maneuvers with continuous trajectory variables for safety-critical …
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This research proposes a computationally-efficient spatiotemporal receding horizon control (ST-RHC) scheme to generate a safe, …
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Present an integrated decision and trajectory planning approach for the autonomous vehicles to achieve high travel efficiency in …
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We propose a learning-based tracking control scheme based on a feedback linearization controller in which uncertain disturbances are …
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A safe and learning-based control framework for model predictive control is proposed to optimize nonlinear systems with a …
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Enforcing safety on precise trajectory tracking is critical for aerial robotics subject to wind disturbances. In this paper, we present …
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A learning-based MPFC control paradigm for nonlinear systems under uncertain disturbances, coupling a high-level model predictive path …
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A learning-based safety-stability-driven control algorithm is presented to guarantee the safety and tracking stability for nonlinear …
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Recent Publications

Some publications i have recently published

(2024). Barrier-Enhanced Parallel Homotopic Trajectory Optimization for Safety-Critical Autonomous Driving. In IEEE TITS.

PDF Project Video DOI

(2024). Spatiotemporal Receding Horizon Control with Proactive Interaction Towards Autonomous Driving in Dense Traffic. In IEEE TIV.

PDF Video DOI

(2023). Real-Time Parallel Trajectory Optimization with Spatiotemporal Safety Constraints for Autonomous Driving in Congested Traffic. In ITSC.

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(2022). Safe Learning-Based Feedback Linearization Tracking Control for Nonlinear System with Event-Triggered Model Update. In IEEE RAL (Presentation at IEEE ICRA 2022).

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(2022). Safe learning-based gradient-free model predictive control based on cross-entropy method. In EAAI.

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Popular Topics

academia autonomous driving learning robotics

Contact

Connect with me

  • Newell Simon Hall, 5000 Forbes Ave, Pittsburgh, PA 15213, United States
  • Workday 9:00 to 19:00
  • Book an appointment