We present a hierarchical control approach for maneuvering an autonomous vehicle (AV) in tightly-constrained environments where other moving AVs and/or human driven vehicles are present. A two-level hierarchy is proposed: a high-level data-driven strategy predictor and a lower-level model-based feedback controller. The strategy predictor maps an encoding of a dynamic environment to a set of high-level strategies via a neural network. Depending on the selected strategy, a set of time-varying hyperplanes in the AV's position space is generated online and the corresponding halfspace constraints are included in a lower-level model-based receding horizon controller. These strategy-dependent constraints drive the vehicle towards areas where it is likely to remain feasible. Moreover, the predicted strategy also informs switching between a discrete set of policies, which allows for more conservative behavior when prediction confidence is low. We demonstrate the effectiveness of the proposed data-driven hierarchical control framework in a two-car collision avoidance scenario through simulations and experiments on a 1/10 scale autonomous car platform where the strategy-guided approach outperforms a model predictive control baseline in both cases.
Clutter Resilient Occlusion Avoidance for Tightly-Coupled Motion-Assisted Detection
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Macau（澳门大学）；Weizmann Institute of Science（魏茨曼科学研究所）；Southern University of Science Technology（南方科技大学）
Traffic collision avoidance systems (TCAS) are used in order to avoid incidences of mid-air collisions between aircraft. We present a game-theoretic approach of a TCAS designed for autonomous unmanned aerial vehicles (UAVs). A variant of the canonical example of game-theoretic learning, fictitious play, is used as a coordination mechanism between the UAVs, that should choose between the alternative altitudes to fly and avoid collision. We present the implementation results of the proposed coordination mechanism in two quad-copters flying in opposite directions.
Barriers on the EDGE: A scalable CBF architecture over EDGE for safe aerial-ground multi-agent coordination
Robotics and Artificial Intelligence Group of the Department of Computer Science, Electrical and Space Engineering at Luleå University of Technology（鲁内斯大学机器人与人工智能小组）
Carnegie Mellon University（卡内基梅隆大学）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
This paper introduces a novel social preference-aware decentralized safe control framework to address the responsibility allocation problem in multi-agent collision avoidance. Considering that agents do not necessarily cooperate in symmetric ways, this paper focuses on semi-cooperative behavior among heterogeneous agents with varying cooperation levels. Drawing upon the idea of Social Value Orientation (SVO) for quantifying the individual selfishness, we propose a novel concept of Responsibility-associated Social Value Orientation (R-SVO) to express the intended relative social implications be…
Time-To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley（加州大学伯克利分校电子工程与计算机科学系）；Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（麻省理工学院航空与航天系）；GRASP Laboratory, University of Pennsylvania（宾夕法尼亚大学GRASP实验室）；Department of Electrical and Computer Engineering, University of California, Los Angeles（加州大学洛杉矶分校电子与计算机工程系）
ARCAS: An Augmented Reality Collision Avoidance System with SLAM-Based Tracking for Enhancing VRU Safety
Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校土木、建筑与环境工程系）；School of Architecture, The University of Texas at Austin（德克萨斯大学奥斯汀分校建筑学院）
Cooperative autonomous approaches to avoiding collisions among small Unmanned Aerial Vehicles (UAVs) is central to safe integration of UAVs within the civilian airspace. One potential online cooperative approach is the concept of reciprocal actions, where both UAVs take pre-trained mutually coherent actions that do not require active online coordination (thereby avoiding the computational burden and risk associated with it). This paper presents a learning based approach to train such reciprocal maneuvers. Neuroevolution, which uses evolutionary algorithms to simultaneously optimize the topolog…
Merry-Go-Round: Safe Control of Decentralized Multi-Robot Systems with Deadlock Prevention
Dept. of Artificial Intelligence, Sogang University（人工智能系，ソガン大学）；Dept. of Electronic Engineering, Sogang University（电子工程系，ソガン大学）；Dept. of Computer Science, University of Illinois Chicago（计算机科学系，伊利诺伊大学香槟分校）
This paper presents a reactive planning system that enriches the topological representation of an environment with a tightly integrated semantic representation, achieved by incorporating and exploiting advances in deep perceptual learning and probabilistic semantic reasoning. Our architecture combines object detection with semantic SLAM, affording robust, reactive logical as well as geometric planning in unexplored environments. Moreover, by incorporating a human mesh estimation algorithm, our system is capable of reacting and responding in real time to semantically labeled human motions and g…
Multi-Agent gatekeeper: Safe Flight Planning and Formation Control for Urban Air Mobility
Department of Electrical & Computer Engineering（电气与计算机工程系）；Department of Robotics（机器人学系）；Department of Robotics and Department of Aerospace Engineering（机器人学系和航空航天工程系）
This paper presents a novel feedback method on the motion planning for unicycle robots in environments with static obstacles, along with an extension to the distributed planning and coordination in multi-robot systems. The method employs a family of 2-dimensional analytic vector fields, whose integral curves exhibit various patterns depending on the value of a parameter lambda. More specifically, for an a priori known value of lambda, the vector field has a unique singular point of dipole type and can be used to steer the unicycle to a goal configuration. Furthermore, for the unique value of l…
Energy efficiency is of critical importance to trajectory planning for UAV swarms in obstacle avoidance. In this paper, we present $E^2Coop$, a new scheme designed to avoid collisions for UAV swarms by tightly coupling Artificial Potential Field (APF) with Particle Swarm Planning (PSO) based trajectory planning. In $E^2Coop$, swarm members perform trajectory planning cooperatively to avoid collisions in an energy-efficient manner. $E^2Coop$ exploits the advantages of the active contour model in image processing for trajectory planning. Each swarm member plans its trajectories on the contours o…
Underwater Embodied Intelligence for Autonomous Robots: A Constraint-Coupled Perspective on Planning, Control, and Deployment
Department of Mechanical Engineering, The University of Hong Kong（香港大学机械工程系）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生学院）；Department of Data and Systems Engineering, The University of Hong Kong（香港大学数据与系统工程系）；Department of Data Science, New Jersey Institute of Technology（新泽西理工学院数据科学系）
Noweigian University of Science and Technology（挪威科技大学）；Carnegie Mellon University（卡内基梅隆大学）
The implementation of optimization-based motion coordination approaches in real world multi-agent systems remains challenging due to their high computational complexity and potential deadlocks. This paper presents a distributed model predictive control (MPC) approach based on convex feasible set (CFS) algorithm for multi-vehicle motion coordination in autonomous driving. By using CFS to convexify the collision avoidance constraints, collision-free trajectories can be computed in real time. We analyze the potential deadlocks and show that a deadlock can be resolved by changing vehicles' desired…
Reliance on external localization infrastructure and centralized coordination are main limiting factors for formation flying of vehicles in large numbers and in unprepared environments. While solutions using onboard localization address the dependency on external infrastructure, the associated coordination strategies typically lack collision avoidance and scalability. To address these shortcomings, we present a unified pipeline with onboard localization and a distributed, collision-free motion planning strategy that scales to a large number of vehicles. Since distributed collision avoidance st…
AMSwarmX: Safe Swarm Coordination in CompleX Environments via Implicit Non-Convex Decomposition of the Obstacle-Free Space
University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；Technical University of Munich（慕尼黑工业大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；University of Tartu（塔尔图大学）
Seconda Università degli Studi di Napoli（那不勒斯第二大学）
A reactive obstacle avoidance method for mobile manipulators is presented. The objectives of the developed algorithm are twofold. The first one is to find a trajectory in the configuration space of a mobile manipulator so as to follow a given trajectory in the task space. The second objective consists in locally adjusting the trajectory in the configuration space in order to avoid collisions with potentially moving obstacles and self-collisions in unstructured and dynamic environments. The perception is exclusively based on a set of proximity sensors distributed on the robot mechanical structu…
Department of Cognitive Robotics, Delft University of Technology（认知机器人学系，代尔夫特理工大学）；Autonomous Systems Lab, ETH Zurich（自主系统实验室，苏黎世联邦理工学院）
Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions as a means to cope with the lack of a central system coordinating the efforts of all robots. Especially in complex dynamic environments, the coordination boost allowed by communication is critical to avoid collisions between cooperating robots. However, the risk of collision between a pair of robots fluctuates through their motion and communication is not always needed. Additionally, constant communication makes much of the still valuable information shared in previous ti…
Forecast-Driven MPC for Decentralized Multi-Robot Collision Avoidance
Department of Computer Science（计算机科学系）；Maharishi International University（玛哈里什国际大学）；Electrical Engineering and Computing（电气工程与计算）；University of Zagreb（扎格雷布大学）；Eastern Illinois University（东部伊利诺伊大学）
VIRGA: Virtual-Agent-Intermediated Riemannian Geometry for Active-Sensing Air-Ground Coordination
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海自主智能无人系统科学中心）；State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University（同济大学自主智能无人系统全国重点实验室）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）
Hybrid Task and Motion Planning with Reactive Collision Handling for Multi-Robot Disassembly of Complex Products: Application to EV Batteries
Extreme Robotics Laboratory, School of Metallurgy and Materials, University of Birmingham（极端机器人实验室，冶金与材料学院，伯明翰大学）；Department of Applied Artificial Intelligence and Robotics, Aston University（应用人工智能与机器人学院，阿斯顿大学）
Anytime Plug-and-Play Control with Contract-Based Distributed MPC
Institute for Dynamic Systems and Control, ETH Zurich（动态系统与控制研究所，苏黎世联邦理工学院）；Institute of Mechanical Engineering, École Polytechnique Fédérale de Lausanne（机械工程研究所，洛桑联邦理工学院）
Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Instituto Tecnológico Vale（淡水河谷技术研究所）；University of Southern California（南加州大学）；Cornell University（康奈尔大学）；University of California, Riverside（加州大学河滨分校）；Polytechnique Montréal（蒙特利尔理工学院）；Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙高等科学研究委员会-加泰罗尼亚理工大学机器人与工业信息研究所）；California Institute of Technology（加州理工学院）
Communication is an important capability for multi-robot exploration because (1) inter-robot communication (comms) improves coverage efficiency and (2) robot-to-base comms improves situational awareness. Exploring comms-restricted (e.g., subterranean) environments requires a multi-robot system to tolerate and anticipate intermittent connectivity, and to carefully consider comms requirements, otherwise mission-critical data may be lost. In this paper, we describe and analyze ACHORD (Autonomous & Collaborative High-Bandwidth Operations with Radio Droppables), a multi-layer networking solution wh…
SMART: Scalable Multi-Agent Reasoning and Trajectory Planning in Dense Environments
Department of Civil and Environmental Engineering University of Wisconsin-Madison（威斯康星大学麦迪逊分校土木与环境工程系）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动性学院）；Department of Civil Engineering, The University of Hong Kong（香港大学土木工程系）
Navigation and guidance of autonomous vehicles is a fundamental problem in robotics, which has attracted intensive research in recent decades. This report is mainly concerned with provable collision avoidance of multiple autonomous vehicles operating in unknown cluttered environments, using reactive decentralized navigation laws, where obstacle information is supplied by some sensor system. Recently, robust and decentralized variants of model predictive control based navigation systems have been applied to vehicle navigation problems. Properties such as provable collision avoidance under distu…
Cognition to Control - Multi-Agent Learning for Human-Humanoid Collaborative Transport
Department of Electrical Engineering, the University of Texas at Arlington（电气工程系，德克萨斯大学阿灵顿分校）；Department of Mechanical Engineering, Carnegie Mellon University（机械工程系，卡内基梅隆大学）
Terrain-Adaptive Mobile 3D Printing with Hierarchical Control
Department of Electrical and Computer Engineering, University of Washington, USA（电气与计算机工程系，华盛顿大学）；Department of Applied Mathematics, University of Washington, USA（应用数学系，华盛顿大学）
ConvoyLLM: Dynamic Multi-Lane Convoy Control Using LLMs
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；Intelligent Transportation Systems Research Center, Wuhan University of Technology（武汉理工大学智能交通系统研究中心）；School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络空间科学与工程学院）
