Tasks in outdoor open world environments are now ripe for automation with mobile manipulators. The dynamic, unstructured and unknown environments associated with such tasks -- a prime example would be collecting roadside trash -- makes them particularly challenging. In this paper we present an approach to solving the problem of picking up, transporting, and dropping off novel objects outdoors. Our solution integrates a navigation system, a grasp detection and planning system, and a custom task planner. We perform experiments that demonstrate that the system can be used to transport a wide class of novel objects (trash bags, general garbage, gardening tools and fruits) in unstructured settings outdoors with a relatively high end-to-end success rate of 85%. See it at work at: https://youtu.be/93nWXhaGEWA
Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots
Southern University of Science and Technology（南方科技大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）；The Chinese University of Hong Kong（香港中文大学）；University of Alberta（阿尔伯塔大学）；National University of Singapore（新加坡国立大学）
This paper proposes the use of an on-demand, ride hailed and ride-Shared Autonomous Vehicle (SAV) service as a feasible solution to serve the mobility needs of a small city where fixed route, circulator type public transportation may be too expensive to operate. The presented work builds upon our earlier work that modeled the city of Marysville, Ohio as an example of such a city, with realistic traffic behavior, and trip requests. A simple SAV dispatcher is implemented to model the behavior of the proposed on-demand mobility service. The goal of the service is to optimally distribute SAVs alon…
Scenarios Engineering driven Autonomous Transportation in Open-Pit Mines
Hong Kong Baptist University（香港浸会大学）；IRADS, BNU-HKBU UIC（北京师范大学-香港浸会大学联合国际学院IRADS）；Peng Cheng Laboratory（鹏城实验室）；Indiana University-Purdue University Indianapolis（印第安纳大学与普渡大学印第安纳波利斯联合分校）；University of Chinese Academy of Sciences（中国科学院大学）；Waytous Ltd.（万道科技有限公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Existing intelligent driving technology often has a problem in balancing smooth driving and fast obstacle avoidance, especially when the vehicle is in a non-structural environment, and is prone to instability in emergency situations. Therefore, this study proposed an autonomous obstacle avoidance control strategy that can effectively guarantee vehicle stability based on Attention-long short-term memory (Attention-LSTM) deep learning model with the idea of humanoid driving. First, we designed the autonomous obstacle avoidance control rules to guarantee the safety of unmanned vehicles. Second, w…
PerceptIn（感知创新公司）；South China University of Technology（华南理工大学）；Northwestern University（西北大学）
The infrastructure-vehicle cooperative autonomous driving approach depends on the cooperation between intelligent roads and intelligent vehicles. This approach is not only safer but also more economical compared to the traditional on-vehicle-only autonomous driving approach. In this paper, we introduce our real-world deployment experiences of cooperative autonomous driving, and delve into the details of new challenges and opportunities. Specifically, based on our progress towards commercial deployment, we follow a three-stage development roadmap of the cooperative autonomous driving approach:i…
Scenario Engineering for Autonomous Transportation: A New Stage in Open-Pit Mines
Hong Kong Baptist University（香港浸会大学）；BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）；Peng Cheng Laboratory（鹏城实验室）；Indiana University-Purdue University Indianapolis (IUPUI)（印第安纳大学-普渡大学印第安纳波利斯联合分校）；Guangdong Provincial Key Laboratory IRADS（广东省IRADS重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Since early 2020, the coronavirus disease 2019 (COVID-19) has spread rapidly across the world. As at the date of writing this article, the disease has been globally reported in 223 countries and regions, infected over 108 million people and caused over 2.4 million deaths (https://covid19.who.int/, accessed on Feb. 17, 2021). Avoiding person-to-person transmission is an effective approach to control and prevent the pandemic. However, many daily activities, such as transporting goods in our daily life, inevitably involve person-to-person contact. Using an autonomous logistic vehicle to achieve c…
QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems
Indian Institute of Science Education and Research（印度科学教育与研究学院）；Prince Mohammad Bin Fahd University（穆罕默德·本·法赫德王子大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Hurghada University（古尔代盖大学）
Dual-Quadruped Collaborative Transportation in Narrow Environments via Safe Reinforcement Learning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Advanced Remanufacturing & Technology Centre (ARTC)（先进再制造与技术中心）；Agency for Science, Technology and Research (A*STAR)（科技研究局）；Cheng Kar-Shun Robotics Institute, The Hong Kong University of Science and Technology（陈克舜机器人研究所，香港科技大学）
Technical University of Munich (TUM)（慕尼黑工业大学）；University of Sarajevo（萨拉热窝大学）
This paper presents a novel hierarchical motion planning approach based on Rapidly-Exploring Random Trees (RRT) for global planning and Model Predictive Control (MPC) for local planning. The approach targets a three-wheeled cycle rickshaw (trishaw) used for autonomous urban transportation in shared spaces. Due to the nature of the vehicle, the algorithms had to be adapted in order to adhere to non-holonomic kinematic constraints using the Kinematic Single-Track Model. The vehicle is designed to offer transportation for people and goods in shared environments such as roads, sidewalks, bicycle l…
Towards Multi-Object Nonprehensile Transportation via Shared Teleoperation: A Framework Based on Virtual Object Model Predictive Control
State Key Laboratory of Robotics and System, Harbin Institute of Technology（哈尔滨工业大学机器人技术与系统国家重点实验室）；Advanced Manufacturing Laboratory, Huawei Technologies（华为技术有限公司先进制造实验室）
This paper serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering for autonomous vehicle (AV) control in the era of mixed autonomy. We will discuss state-of-the-art applications of AI-guided methods, identify opportunities and obstacles, raise open questions, and help suggest the building blocks and areas where AI could play a role in mixed autonomy. We divide the stage of autonomous vehicle (AV) deployment into four phases: the pure HVs, the HV-dominated, the AVdominated, and the …
Czech Technical University in Prague（布拉格捷克理工大学）；New York University（纽约大学）；University of Pennsylvania（宾夕法尼亚大学）
Mobile manipulators have the potential to revolutionize modern agriculture, logistics and manufacturing. In this work, we present the design of a ground-based mobile manipulator for automated structure assembly. The proposed system is capable of autonomous localization, grasping, transportation and deployment of construction material in a semi-structured environment. Special effort was put into making the system invariant to lighting changes, and not reliant on external positioning systems. Therefore, the presented system is self-contained and capable of operating in outdoor and indoor conditi…
This paper is concerned with learning transferable contact models for aerial manipulation tasks. We investigate a contact-based approach for enabling unmanned aerial vehicles with cable-suspended passive grippers to compute the attach points on novel payloads for aerial transportation. This is the first time that the problem of autonomously generating contact points for such tasks has been investigated. Our approach builds on the underpinning idea that we can learn a probability density of contacts over objects' surfaces from a single demonstration. We enhance this formulation for encoding aer…
How autonomous vehicles and human drivers share public transportation systems is an important problem, as fully automatic transportation environments are still a long way off. Understanding human drivers' behavior can be beneficial for autonomous vehicle decision making and planning, especially when the autonomous vehicle is surrounded by human drivers who have various driving behaviors and patterns of interaction with other vehicles. In this paper, we propose an LSTM-based trajectory prediction method for human drivers which can help the autonomous vehicle make better decisions, especially in…
Sky-Drive: A Distributed Multi-Agent Simulation Platform for Human-AI Collaborative and Socially-Aware Future Transportation
Department of Civil and Environmental Engineering, University of Wisconsin-Madison（土木与环境工程系，威斯康星大学麦迪逊分校）；Lyles School of Civil and Construction Engineering, Purdue University（莱尔斯土木与建设工程学院，普渡大学）；Elmore Family School of Electrical and Computer Engineering, Purdue University（埃尔摩家族电气与计算机工程学院，普渡大学）；Department of Computer Science and Engineering, The University of Texas at Arlington（计算机科学与工程系，德克萨斯大学阿灵顿分校）；Google（谷歌）
Technical University of Denmark（丹麦技术大学）；Stanford University（斯坦福大学）
Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic, self-driving vehicles. Given a graph representation of the transportation network - one where, for example, nodes represent areas of the city, and edges the connectivity between them - we argue that the AMoD control problem is naturally cast as a node-wise decision-making problem. In this paper, we propose a deep reinforcement learning framework to control the rebalancing of AMoD systems through graph neural networ…
USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways
School of Information Engineering, Yancheng Institute Technology（盐城理工学院信息工程学院）；Thrust of Artificial Intelligence and Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能与智能交通研究部）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学先进科技学院）；School of Navigation, Wuhan University of Technology（武汉理工大学航海学院）
