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Yuda Chen

6 accepted papers

2024

Asynchronous Spatial-Temporal Allocation for Trajectory Planning of Heterogeneous Multi-Agent Systems

IROS 2024poster

To plan the trajectories of a large-scale heterogeneous swarm, sequentially or synchronously distributed methods usually become intractable due to the lack of global clock synchronization. To this end, we provide a novel asynchronous spatial-temporal allocation method. Specifically, between a pair o…

Cited by 0SourcecodeScholar
2023

Multi-Robot Trajectory Planning With Feasibility Guarantee and Deadlock Resolution: An Obstacle-Dense Environment

RA-L 2023

This letter presents a multi-robot trajectory planning method which not only guarantees optimization feasibility and but also resolves deadlocks in obstacle-dense environments. The method is proposed via formulating a recursive optimization problem, where a novel safe corridor is generated online to

Cited by 24SourceScholar
2021

State Estimation for Hybrid Wheeled-Legged Robots Performing Mobile Manipulation Tasks

ICRA 2021poster

This paper introduces a general state estimation framework fusing multiple sensor information for hybrid wheeled-legged robots performing mobile manipulation tasks. At the core of the state estimator is a novel unified odometry for hybrid locomotion which can seamlessly maintain tracking and has no…

Cited by 12SourceScholar
2021

Supervised Autonomy for Remote Teleoperation of Hybrid Wheel-Legged Mobile Manipulator Robots

IROS 2021poster

This paper proposes an improved supervised autonomy framework for remote teleoperation of a quadrupedal bimanual mobile manipulator in an unknown environment, with the usage of advanced perception technology and allowing the operator to easily assist the robot with decision making for executing task…

Cited by 6SourceScholar
2020

A*3D Dataset: Towards Autonomous Driving in Challenging Environments

ICRA 2020poster

With the increasing global popularity of self-driving cars, there is an immediate need for challenging real-world datasets for benchmarking and training various computer vision tasks such as 3D object detection. Existing datasets either represent simple scenarios or provide only day-time data. In th…

Cited by 206SourcecodeScholar