ccxu

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ZMART made a record in the IARC history

2018年8月27日,经国际空中机器人大赛(IARC)委员会评定,浙江大学代表队ZMART以综合评分第一、比赛成绩第一,获得IARC第七代任务世界冠军,赢得2万美元比赛奖金。浙江大学成为继斯坦福大学(1995),卡耐基梅隆大学(1997),柏林工大(2000),佐治亚理工(2008),麻省理工(2009),清华大学(2013)之后IARC第七个世界冠军得主。自此第七代任务结束,IARC比赛进入第八代任务。 News featured in the media include: 电视频道报道:国际空中机器人(IARC)大赛及青少年附加赛在中国教育电视台-1 频道播出,Youku链接 College of Control Science & Engineering, Zhejiang University, “历时5年,浙大队ZMART勇夺国际空中机器人大赛世界冠军,终结第七代任务“ China Automation Association (中国自动化学会), “【2018 IARC】国际空中机器人大赛将于8月26日在北京航空航天大学体育馆开幕!大赛背景介绍与参赛队伍简介一睹为快!“ 国际空中机器人微信大赛公众号/搜狐网,“2018年国际空中机器人大赛在北京成功举办” INTERNATIONAL AERIAL ROBOTICS COMPETITION Mission 7 (2014-2018), http://www.aerialroboticscompetition.org/mission7.php THE INTERNATIONAL AERIAL ROBOTICS COMPETITION’S 27 Year History, http://www.aerialroboticscompetition.org/pastmissions.php “Mission 7 took a monumental leap by requiring autonomous aerial robots to interact with and control autonomous ground robots. Teams were tasked with developing systems to herd ground robots out one end of an arena in the absence of 3D cues such as walls. The ground robots could only be interacted with by touch. A top touch would command a 45° clockwise turn and a blocking action would result in a 180° turn. To complicate matters, the ground robots do a 180° turn every 20 seconds and add up to 15 degrees of trajectory noise every 5 seconds. The ground robots also impact one another and quickly devolve into non-deterministic travel. In the midst of the arena were four obstacle robots to complicate navigation and obstacle avoidance. The aerial robots had to dynamically determine a best course of action to keep the ground robots from exiting on three of four sides of the arena. In the top performances, which were replicated multiple times, the Zhejiang University team showed that its autonomous aerial robot could track individual ground robots, redirect them in either 45° or 180° increments while at the same time staying within the arena boundaries and avoiding the mobile obstacles circulating within the arena.”

WRSC

Introduction The World Robotics Sailing Championship (WRSC) and The International Robotic Sailing Conference (IRSC) is the world largest fully autonomous sailing robot event which is a spinoff competition from the Microtransat challenge, a trans Atlantic race for autonomous sailing robots. The competition is open to wind-powered unmanned surface vehicles up to 4 metres long. The conference provides researchers with the opportunity to present and exchange ideas on their work. The WRSC is intended to promote the development of autonomous wind propelled sailing robots, through a series of short distance races, navigation and autonomy challenges. Many teams who take part in the Microtransat (or who plan to) also attend the WRSC. The accompanying IRSC (International Robotic Sailing Conference) provides researchers working on problems related to autonomous sailing the chance to exchange ideas during a scientific conference. So far the WRSC has been through 12 years. WRSC will be organized in 4 challenges: fleet race, station keeping, area scanning and obstacle avoidance. You need to master robotic navigation, positioning, trajectory planning and other technologies to challenge them. Results • The 11th WRSC and IRSC was held in Sounthampton, UK. As first time participants, our team ZMART won the 3rd place of WRSC 2018 Micro-sailiboat class. • The 12th WRSC and IRSC was held in Ningbo, China. Our team ZMART won the 1st place of WRSC 2019.

2019 Robomaster AI Challenge

IntroductionICRA’s RoboMaster challenge asks teams to use the same hardware to build one or a pair of these rovers. Instead of human pilots, these rovers must be fully autonomous. The AI challenge asks teams to build rovers that can sense the environment around themselves, navigate an arena, and engage in combat with the opposing team. Rovers will need to move around the battlefield, a five by eight meter space, launching projectiles and trying to avoid incoming hits. At the end of the match, the team that has scored the most hits on their opponents will be declared the winner. The grand prize winner will receive a $20,000 prize.Teams purchase hardware from RoboMaster and code their own autonomous systems. After signing up, teams must submit photos, videos, and documentation that track their progress. Teams that demonstrate their ability to design fully functioning and autonomous rover units will qualify for the competition.Results• Our team, ZMART won the 2nd place of DJI RoboMaster AI Challenge, with $15,000 prize. 

IARC Mission 7

Introduction International Aerial Robot Competition (IARC) was founded in 1991, promoted by Professor Robert from Georgia Institute of Technology, funded by the Association for Unmanned Vehicle Systems International (AUVSI), and held once a year. The tournament so far has been through 27 years, with seven generations of tasks completed, which are all highly intelligent technology competition tasks from automatic to autonomous control. IARC aims to advance UAV technology by setting challenging, practical and meaningful competition tasks. These tasks are almost impossible to accomplish when they are presented, yet the world will benefit when they are ultimately accomplished by aerial robots. Mission 7, “Air Shepherd Dog Action”, requires aerial robots rely entirely on their own indoor navigation and control technology to block and control 10 ground moving objects to designated areas of the competition venue, and to avoid collisions of four moving obstacles of different heights. Results • Our team, ZMART, representing Zhejiang University, won the champion of mission 7 of IARC, and become the seventh world champion after Stanford University, Carnegie Mellon University, Technische Universität Berlin, Georgia Institute of Technology, Massachusetts Institute of Technology and Tsinghua University. ZMART Performance video • ZMART 2017 Trailer – We need you • 2016 IARC Official Results • 2016 IARC Performance Collection – ZMART For more information, please refer to this link.  

Fast Motion Planning

Introduction High-speed autonomous navigation with micro aerial vehicles (MAVs) operating in unknown dynamic environments requires the vehicles to be capable of quick replans to avoid potential obstacles. The high operating speed, the short sensing range and the unknown dynamic environments all leave strictly limited reaction time to make motion plans of high qualities. Developing reasonable navigation frameworks and fast motion planning algorithms is an essential part of it, especially for those lightweight multi-rotors with limited onboard sensing and computation capabilities. The projects under this topic focus on developing effective yet computation-friendly motion planning methods to enable safe and high-speed navigation of MAVs, considering constraints and sensing and control uncertainties. Related Researches: TGK-Planner: An Efficient Topology Guided Kinodynamic Planner for Autonomous Quadrotors, Hongkai Ye, Xin Zhou, Chao Xu, Jian Chu, Fei Gao, IEEE Robotics and Automation Letter (RA-L). [paper] [code] EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors, Xin Zhou, Zhepei Wang, Chao Xu, Fei Gao, IEEE Robotics and Automation Letter (RA-L with ICRA 2021 option). [paper] [code] Mapless-Planner: A Robust and Fast Planning Framework for Aggressive Autonomous Flight without Map Fusion, Jialin Ji, Zhepei Wang, Yingjian Wang, Chao Xu, Fei Gao, the International Conference on Robotics and Automation (ICRA 2021). [paper] EVA-Planner: Environmental Adaptive Quadrotor Planning, Lun Quan, Zhiwei Zhang, Chao Xu, Fei Gao, the International Conference on Robotics and Automation (ICRA 2021). [paper] [code] Video Links: TGK-Planner: https://www.bilibili.com/video/BV1gA411e7DH EGO-Planner: https://www.bilibili.com/video/BV1VC4y1t7F4 Mapless-Planner: https://www.bilibili.com/video/BV1mt4y1e7TS EVA-Planner: https://www.bilibili.com/video/BV1Zz4y1C7rt

Autonomous Aerial Swarms

Introduction The agility of quadrotors enables this machine to perform single-agent autonomous navigation in unknown environments and multi-agent precise formation control in open or known fields. However, few works combine both of them to present any real-world system which is capable of navigating quadrotor swarms sharing the same unknown space, especially with only onboard processing. This project is a systematic solution that enables high-performance traveling in cluttered environments for quadrotor swarms in field environments. In addition, it requires no external localization and computation or a pre-built map. Related Research: EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments, Xin Zhou, Jiangchao Zhu, Hongyu Zhou, Chao Xu, Fei Gao, the International Conference on Robotics and Automation (ICRA 2021). [paper] [code]

A paper is accpeted by IEEE RA-L

December 16th, 2020 Hongkai Ye, Xin Zhou, Chao Xu, Jian Chu, Fei Gao’s work on “TGK-Planner: An Efficient Topology Guided Kinodynamic Planner for Autonomous Quadrotors” is accepted by IEEE Robotics and Automation Letters (RA-L) for publication.

Trajectory Generation

Introduction AM-Traj: A waypoint-based trajectory generator. This project proposes a framework for large-scale waypoint-based trajectory generation, with highlights on its superior computational efficiency and simultaneous spatial-temporal optimality. Exploiting the implicitly decoupled structure of the problem, we conduct alternating minimization between boundary conditions and time durations of trajectory pieces. Algebraic convenience of both sub-problems is leveraged to escape poor local minima and achieve the lowest time consumption. Moreover, based on polynomial theory, an extremely fast feasibility checker is designed for various kinds of constraints. By incorporating it into our alternating structure, a constrained minimization algorithm is constructed to optimize trajectories on the premise of feasibility. Benchmark evaluation shows that our algorithm outperforms state-of-the-art waypoint-based methods regarding efficiency, optimality, and scalability. The algorithm can be incorporated in a high-level waypoint planner, which can rapidly search over a three-dimensional space for aggressive autonomous flights. The capability of our algorithm is experimentally demonstrated by quadrotor fast flights in a limited space with dense obstacles. Large-Scale Trajectory Generation in Flight Corridors: For quadrotor trajectory planning, describing a polynomial trajectory through coefficients and end-derivatives both enjoy their own convenience in energy minimization. We name them double descriptions of polynomial trajectories. The transformation between them, causing most of the inefficiency and instability, is formally analyzed in this paper. Leveraging its analytic structure, we design a linear-complexity scheme for both jerk/snap minimization and parameter gradient evaluation, which possesses efficiency, stability, flexibility, and scalability. With the help of our scheme, generating an energy optimal (minimum snap) trajectory only costs […]

A paper is accpeted by IEEE RA-L

October 21st, 2020 Xin Zhou, Zhepei Wang, Chao Xu and Fei Gao’s work on “EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors” is accepted by IEEE Robotics and Automation Letters (RA-L) for publication.

A paper is accpeted by ISER

October 7th, 2020 Jialin Ji, Xin Zhou, Chao Xu, Fei Gao’s work on “CMPCC: Corridor-based Model Predictive Contouring Control for Aggressive Drone Flight” is accepted by International Symposium on Experimental Robotics (ISER) for publication. ISER 2020 will be held in La Valletta, Malta from March 22nd to 25th in 2021 due to travel restrictions after the CoVid-19 pandemic.