This paper considers the problem of fast and safe autonomous navigation in partially known environments. Our main contribution is a control policy design based on ellipsoidal trajectory bounds obtained from a quadratic state-dependent distance metric. The ellipsoidal bounds are used to embed directional preference in the control design, leading to system behavior that is adapted to the local environment geometry, carefully considering medial obstacles while paying less attention to lateral ones. We use a virtual reference governor system to adaptively follow a desired navigation path, slowing down when system safety may be violated and speeding up otherwise. The resulting controller is able to navigate complex environments faster than common Euclidean-norm and Lyapunov-function-based designs, while retaining stability and collision avoidance guarantees.
This paper considers the problem of safe autonomous navigation in unknown environments, relying on local obstacle sensing. We consider a control-affine nonlinear robot system subject to bounded input noise and rely on feedback linearization to determine ellipsoid output bounds on the closed-loop robot trajectory under stabilizing control. A virtual governor system is developed to adaptively track a desired navigation path, while relying on the robot trajectory bounds to slow down if safety is endangered and speed up otherwise. The main contribution is the derivation of theoretical guarantees f…
QuasiNav: Asymmetric Cost-Aware Navigation Planning with Constrained Quasimetric Reinforcement Learning
University of Maryland, Baltimore County（马里兰大学巴尔的摩分校）；Amazon Inc.（亚马逊公司）；DEVCOM Army Research Lab（DEVCOM陆军研究实验室）；Stormfish Scientific Corporation（斯托姆菲什科学公司）
Safe MPC Alignment with Human Directional Feedback
Intelligent Robotics and Interactive Systems (IRIS) Lab in the School for Engineering of Matter, Transport and Energy（智能机器人与交互系统实验室（IRIS））；Polytechnic School（理工学院）；School for Engineering of Matter, Transport and Energy（工程物质、运输与能量学院）；Departments of Industrial Engineering & Management Sciences（工业工程与管理科学系）；Department of Electrical and Systems Engineering（电气与系统工程系）
Hong Kong University of Science and Technology（香港科技大学）；Carnegie Mellon University（卡内基梅隆大学）
In densely populated environments, socially compliant navigation is critical for autonomous robots as driving close to people is unavoidable. This manner of social navigation is challenging given the constraints of human comfort and social rules. Traditional methods based on hand-craft cost functions to achieve this task have difficulties to operate in the complex real world. Other learning-based approaches fail to address the naturalness aspect from the perspective of collective formation behaviors. We present an autonomous navigation system capable of operating in dense crowds and utilizing …
Directional Constraints for Efficient Exploration in Safe Reinforcement Learning
Dipartimento di Automatica e Informatica, Politecnico di Torino（自动控制与信息工程系，都灵理工大学）；Tongji University, Shanghai Research Institute for Intelligent Autonomous Systems（同济大学，上海智能自主系统研究院）；German Research Center for AI (DFKI)（德国人工智能研究中心（DFKI））；Intelligent Autonomous Systems Group, TU Darmstadt（智能自主系统组，达姆施塔特工业大学）；Lund University（隆德大学）；Istituto Italiano di Tecnologia（意大利技术研究院）
Eindhoven University of Technology（埃因霍温理工大学）；Eindhoven AI Systems Institute（埃因霍温人工智能系统研究所）
Safe and smooth robot motion around obstacles is an essential skill for autonomous robots, especially when operating around people and other robots. Conventionally, due to real-time operation requirements and onboard computation limitations, many robot motion planning and control methods follow a two-step approach: first construct a (e.g., piecewise linear) collision-free reference path for a simplified robot model, and then execute the reference plan via path-following control for a more accurate and complex robot model. A challenge of such a decoupled robot motion planning and control method…
Multi-directional Safe Rectangle Corridor-Based MPC for Nonholonomic Robots Navigation in Cluttered Environment
The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）
Provably safe and human-like car-following behaviors: Part 1. Analysis of phases and dynamics in standard models
Department of Civil and Environmental Engineering, California Institute for Telecommunications and Information Technology, Institute of Transportation Studies（土木与环境工程系、电信与信息科技研究院、交通研究学院）
Provably safe and human-like car-following behaviors: Part 2. A parsimonious multi-phase model with projected braking
Department of Civil and Environmental Engineering, California Institute for Telecommunications and Information Technology, Institute of Transportation Studies（土木与环境工程系、电信与信息科技研究院、交通研究学院）
Robots often have to deal with the challenges of operating in dynamic and sometimes unpredictable environments. Although an occupancy map of the environment is sufficient for navigation of a mobile robot or manipulation tasks with a robotic arm in static environments, robots operating in dynamic environments demand richer information to improve robustness, efficiency, and safety. For instance, in path planning, it is important to know the direction of motion of dynamic objects at various locations of the environment for safer navigation or human-robot interaction. In this paper, we introduce d…
Decision-making under uncertainty is a crucial ability for autonomous systems. In its most general form, this problem can be formulated as a Partially Observable Markov Decision Process (POMDP). The solution policy of a POMDP can be implicitly encoded as a value function. In partially observable settings, the value function is typically learned via forward simulation of the system evolution. Focusing on accurate and long-range risk assessment, we propose a novel method, where the value function is learned in different phases via a bi-directional search in belief space. A backward value learnin…
Future-Aware Flow Planning for Safe UAV Target Following
Stockholm University（斯德哥尔摩大学）；Peking University（北京大学）；Beijing Normal-Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）；Guangdong Provincial/Zhuhai Key Laboratory of IRADS（广东省/珠海市智能机器人先进感知与决策系统重点实验室）
Capability-Aware Heterogeneous Control Barrier Functions for Decentralized Multi-Robot Safe Navigation
Department of Computer Science and Engineering, Texas A&M University（德克萨斯A&M大学计算机科学与工程系）；Department of Computer Science, University of Illinois Chicago（伊利诺伊大学芝加哥分校计算机科学系）
National Research University Higher School of Economics（国家研究型高等经济大学）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Peoples’ Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Bauman Moscow State Technical University（鲍曼莫斯科国立技术大学）
We study the navigation problem for a robot moving amidst static and dynamic obstacles and rely on a hierarchical approach to solve it. First, the reference trajectory is planned by the safe interval path planning algorithm that is capable of handling any-angle translations and rotations. Second, the path following problem is treated as the constrained control problem and the original flatness-based approach is proposed to generate control. We suggest a few enhancements for the path planning algorithm aimed at finding trajectories that are more likely to be followed by a robot without collisio…
School of Transportation and Logistics, Southwest Jiaotong University（西南交通大学交通运输与物流学院）；Amazon Toronto（亚马逊多伦多分部）
When driving,it is vital to maintain the right following distance between the vehicles to avoid rear-end collisions. The minimum safe distance depends on many factors, however, in this study the safe distance between the human-driven vehicles and a fully autonomous vehicle at a sudden stop by an automatic emergency brake was studied based on the human driver ability to react in an accident, the vehicles' braking system performance, and the speed of vehicles. For this approach, a safe distance car-following model was proposed to describe the safe distance between vehicles on a single lane dry r…
This paper characterizes safe following distances for on-road driving when vehicles can avoid collisions by either braking or by swerving into an adjacent lane. In particular, we focus on safety as defined in the Responsibility-Sensitive Safety (RSS) framework. We extend RSS by introducing swerve manoeuvres as a valid response in addition to the already present brake manoeuvre. These swerve manoeuvres use the more realistic kinematic bicycle model rather than the double integrator model of RSS. When vehicles are able to swerve and brake, it is shown that their required safe following distance …
Balancing path following accuracy and error convergence with graceful motion in steering control is challenging due to the competing nature of these requirements, especially across a range of operating speeds and conditions. This paper demonstrates that an integrated multi-tiered steering controller considering the impact of slip on kinematic control, dynamic control, and steering actuator rate commands achieves accurate and graceful path following. This work is founded on multi-tiered sideslip and yaw-based models, which allow derivation of controllers considering error due to sideslip and th…
This article formulates a generic representation of a path-following controller operating under contained motion, which was developed in the context of surgical robotics. It reports two types of constrained motion: i) Bilateral Constrained Motion, also called Remote Center Motion (RCM), and ii) Unilaterally Constrained Motion (UCM). In the first case, the incision hole has almost the same diameter as the robotic tool. In contrast, in the second state, the diameter of the incision orifice is larger than the tool diameter. The second case offers more space where the surgical instrument moves fre…
Tongji University（同济大学）；University of Washington（华盛顿大学）
A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). To fulfil the multi-objectives of car following, a reward function reflecting driving safety, efficiency, and comfort was constructed. With the reward function, the RL agent learns to control vehicle speed in a fashion that maximizes cumulative rewards, through trials and errors in the simulation environment. A total of 1,341 car-following events extracted from the Next Generation Simulation (NGSIM) dataset were used to train the model. Car-following behavior produced by the model wer…
Assistance robots have gained widespread attention in various industries such as logistics and human assistance. The tasks of guiding or following a human in a crowded environment such as airports or train stations to carry weight or goods is still an open problem. In these use cases, the robot is not only required to intelligently interact with humans, but also to navigate safely among crowds. Thus, especially highly dynamic environments pose a grand challenge due to the volatile behavior patterns and unpredictable movements of humans. In this paper, we propose a Deep-Reinforcement-Learning-b…
University of Toronto（多伦多大学）；Arab Academy for Science, Technology, and Maritime Transport（阿拉伯科学、技术与海运学院）
In the coming years and decades, autonomous vehicles (AVs) will become increasingly prevalent, offering new opportunities for safer and more convenient travel and potentially smarter traffic control methods exploiting automation and connectivity. Car following is a prime function in autonomous driving. Car following based on reinforcement learning has received attention in recent years with the goal of learning and achieving performance levels comparable to humans. However, most existing RL methods model car following as a unilateral problem, sensing only the vehicle ahead. Recent literature, …
The Ohio State University（俄亥俄州立大学）；Ford Motor Company（福特汽车公司）
An online evolving framework is proposed to support modeling the safe Automated Vehicle (AV) control system by making the controller able to recognize unexpected situations and react appropriately by choosing a better action. Within the framework, the evolving Finite State Machine (e-FSM), which is an online model able to (1) determine states uniquely as needed, (2) recognize states, and (3) identify state-transitions, is introduced. In this study, the e-FSM's capabilities are explained and illustrated by simulating a simple car-following scenario. As a vehicle controller, the Intelligent Driv…
