It is essential in many applications to impose a scalable coordinated motion control on a large group of mobile robots, which is efficient in tasks requiring repetitive execution, such as environmental monitoring. In this paper, we design a guiding vector field to guide multiple robots to follow possibly different desired paths while coordinating their motions. The vector field uses a path parameter as a virtual coordinate that is communicated among neighboring robots. Then, the virtual coordinate is utilized to control the relative parametric displacement between robots along the paths. This enables us to design a saturated control algorithm for a Dubins-car-like model. The algorithm is distributed, scalable, and applicable for any smooth paths in an $n$-dimensional configuration space, and global convergence is guaranteed. Simulations with up to fifty robots and outdoor experiments with fixed-wing aircraft validate the theoretical results.
Coordinated Autonomous Drones for Human-Centered Fire Evacuation in Partially Observable Urban Environments
Mechanical Engineering University of California, Berkeley（机械工程 加州大学伯克利分校）；Computer Sciences University of California, Berkeley（计算机科学 加州大学伯克利分校）；Computer Engineering Johns Hopkins University（计算机工程 约翰霍普金斯大学）
Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation
Huazhong University of Science and Technology（华中科技大学）；Hunan University（湖南大学）；University of Groningen（格罗宁根大学）；Eindhoven University of Technology（埃因霍温理工大学）
Singapore Centre for 3D Printing（新加坡3D打印中心）；Nanyang Technological University（南洋理工大学）
This paper considers the motion planning problem for multiple tethered planar mobile robots. Each robot is attached to a fixed base by a flexible cable. Since the robots share a common workspace, the interactions amongst the robots, cables, and obstacles pose significant difficulties for planning. Previous works have studied the problem of detecting whether a target cable configuration is intersecting (or entangled). Here, we are interested in the motion planning problem: how to plan and coordinate the robot motions to realize a given non-intersecting target cable configuration. We identify fo…
Decoupling Physical Speed from Path Parameterization in Singularity-Free Guiding Vector Fields
Hunan University（湖南大学）；Hunan Normal University（湖南师范大学）；National University of Defense Technology（国防科技大学）；Information Support Force Engineering University（信息支援部队工程大学）
Chalmers University of Technology（查尔姆斯理工大学）；Lund University（隆德大学）
This paper proposes a motion control scheme for robots operating in a dynamic environment with concave obstacles. A Model Predictive Controller (MPC) is constructed to drive the robot towards a goal position while ensuring collision avoidance without direct use of obstacle information in the optimization problem. This is achieved by guaranteeing tracking performance of an appropriately designed receding horizon path. The path is computed using a guiding vector field defined in a subspace of the free workspace where each point in the subspace satisfies a criteria for minimum distance to all obs…
University of Groningen（格罗宁根大学）；Universidad Complutense de Madrid（马德里康普顿斯大学）
Most of the existing path-following navigation algorithms cannot guarantee global convergence to desired paths or enable following self-intersected desired paths due to the existence of singular points where navigation algorithms return unreliable or even no solutions. One typical example arises in vector-field guided path-following (VF-PF) navigation algorithms. These algorithms are based on a vector field, and the singular points are exactly where the vector field diminishes. In this paper, we show that it is mathematically impossible for conventional VF-PF algorithms to achieve global conve…
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…
Guiding Vector Field Generation via Score-based Diffusion Model
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；WINDY Lab, Department of Artificial Intelligence, Westlake University（西湖大学人工智能系WINDY实验室）
Bielefeld University（比勒费尔德大学）；Queensland University of Technology（昆士兰科技大学）
We investigate a reinforcement approach for distributed sensing based on the latent space derived from multi-modal deep generative models. Our contribution provides insights to the following benefits: Detections can be exchanged effectively between robots equipped with uni-modal sensors due to a shared latent representation of information that is trained by a Variational Auto Encoder (VAE). Sensor-fusion can be applied asynchronously due to the generative feature of the VAE. Deep Q-Networks (DQNs) are trained to minimize uncertainty in latent space by coordinating robots to a Point-of-Interest…
DFKI GmbH（德国人工智能研究中心）；Scuola Superiore Sant’Anna（圣安娜高等研究院）；University of Bremen（不来梅大学）
Manipulating objects with robotic hands is a complicated task. Not only the fingers of the hand, but also the pose of the robot's end effector need to be coordinated. Using human demonstrations of movements is an intuitive and data-efficient way of guiding the robot's behavior. We propose a modular framework with an automatic embodiment mapping to transfer recorded human hand motions to robotic systems. In this work, we use motion capture to record human motion. We evaluate our approach on eight challenging tasks, in which a robotic hand needs to grasp and manipulate either deformable or small…
ETH Zurich（苏黎世联邦理工学院）；LAAS-CNRS（法国国家科学研究中心分析与系统架构实验室）；Université de Toulouse（图卢兹大学）；CNRS（法国国家科学研究中心）；University of Naples Federico II（那不勒斯费德里科二世大学）
Today, physical Human-Robot Interaction (pHRI) is a very popular topic in the field of ground manipulation. At the same time, Aerial Physical Interaction (APhI) is also developing very fast. Nevertheless, pHRI with aerial vehicles has not been addressed so far. In this work, we present the study of one of the first systems in which a human is physically connected to an aerial vehicle by a cable. We want the robot to be able to pull the human toward a desired position (or along a path) only using forces as an indirect communication-channel. We propose an admittance-based approach that makes pHR…
Microsoft Research（微软研究院）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）
Language-guided robots must be able to both ask humans questions and understand answers. Much existing work focuses only on the latter. In this paper, we go beyond instruction following and introduce a two-agent task where one agent navigates and asks questions that a second, guiding agent answers. Inspired by theory of mind, we propose the Recursive Mental Model (RMM). The navigating agent models the guiding agent to simulate answers given candidate generated questions. The guiding agent in turn models the navigating agent to simulate navigation steps it would take to generate answers. We use…
