In this paper, we solve a joint cooperative localization and path planning problem for a group of Autonomous Aerial Vehicles (AAVs) in GPS-denied areas using nonlinear model predictive control (NMPC). A moving horizon estimator (MHE) is used to estimate the vehicle states with the help of relative bearing information to known landmarks and other vehicles. The goal of the NMPC is to devise optimal paths for each vehicle between a given source and destination while maintaining desired localization accuracy. Estimating localization covariance in the NMPC is computationally intensive, hence we develop an approximate analytical closed form expression based on the relationship between covariance and path lengths to landmarks. Using this expression while computing NMPC commands reduces the computational complexity significantly. We present numerical simulations to validate the proposed approach for different numbers of vehicles and landmark configurations. We also compare the results with EKF-based estimation to show the superiority of the proposed closed form approach.
From Field to Planning: Learning Temporal Polar Gradient Fields for Vision-based AAV Trajectory Planning in Dynamic Environments
College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）；College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化学院）
Efficient and Safe Trajectory Planning for Autonomous Agricultural Vehicle Headland Turning in Cluttered Orchard Environments
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学ZJU-杭州全球科技创新中心）；University of California, Davis（加州大学戴维斯分校）；College Of Biosystems Engineering And Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）
TRUST-Planner: Topology-guided Robust Trajectory Planner for AAVs with Uncertain Obstacle Spatial-temporal Avoidance
School of Aerospace Engineering, Beijing Institute of Technology（北京理工大学航空航天工程学院）；Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education（教育部飞行器动力学与控制重点实验室）
Research Institute for Trustworthy Autonomous Systems, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；University of Macau（澳门大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of New South Wales（新南威尔士大学）
KTH Royal Institute of Technology（KTH皇家理工学院）；School of Electrical Engineering and Computer Science（电气工程与计算机科学学院）
Control and planning of multi-agent systems is an active and increasingly studied topic of research, with many practical applications such as rescue missions, security, surveillance, and transportation. This thesis addresses the planning and control of multi-agent systems under temporal logic tasks. The considered systems concern complex, robotic, manipulator-endowed systems, which can coordinate in order to execute complicated tasks, including object manipulation/transportation. Motivated by real-life scenarios, we take into account high-order dynamics subject to model uncertainties and unkno…
Tsinghua University（清华大学）；Beijing Institute of Control Engineering（北京控制工程研究所）；City University of Hong Kong（香港城市大学）
Recent years have seen the emergence of non-cooperative objects in space, like failed satellites and space junk. These objects are usually operated or collected by free-float dual-arm space manipulators. Thanks to eliminating the difficulties of modeling and manual parameter-tuning, reinforcement learning (RL) methods have shown a more promising sign in the trajectory planning of space manipulators. Although previous studies demonstrate their effectiveness, they cannot be applied in tracking dynamic targets with unknown rotation (non-cooperative objects). In this paper, we proposed a learning …
Skypilot: Fine-Tuning LLM with Physical Grounding for AAV Coverage Search
College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）；College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（自动化工程学院，南京航空航天大学）；China Academy of Launch Vehicle Technology（中国运载火箭技术研究院）
We investigate the distributed planning of robot trajectories for optimal execution of cooperative tasks with time windows. In this setting, each task has a value and is completed if sufficiently many robots are simultaneously present at the necessary location within the specified time window. Tasks keep arriving periodically over cycles. The task specifications (required number of robots, location, time window, and value) are unknown a priori and the robots try to maximize the value of completed tasks by planning their own trajectories for the upcoming cycle based on their past observations i…
Path Planning Optimisation for SParse, AwaRe and Cooperative Networked Aerial Robot Teams (SpArC-NARTs): Optimisation Tool and Ground Sensing Coverage Use Cases
Daimler R&D（戴姆勒研发中心）；FZI Research Center for Information Technology（FZI信息技术研究中心）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
Cooperative motion planning is still a challenging task for robots. Recently, Value Iteration Networks (VINs) were proposed to model motion planning tasks as Neural Networks. In this work, we extend VINs to solve cooperative planning tasks under non-holonomic constraints. For this, we interconnect multiple VINs to pay respect to each other's outputs. Policies for cooperation are generated via iterative gradient descend. Validation in simulation shows that the resulting networks can resolve non-holonomic motion planning problems that require cooperation.
Motion Planning of Cooperative Nonholonomic Mobile Manipulators
Department of Mechanical Engineering, Indian Institute of Technology Bombay（印度理工学院班加罗尔机械工程系）；Center for Systems and Control, Indian Institute of Technology Bombay（印度理工学院班加罗尔系统控制中心）
We introduce the Cooperative Multi-Agent Path Finding (Co-MAPF) problem, an extension to the classical MAPF problem, where cooperative behavior is incorporated. In this setting, a group of autonomous agents operate in a shared environment and have to complete cooperative tasks while avoiding collisions with the other agents in the group. This extension naturally models many real-world applications, where groups of agents are required to collaborate in order to complete a given task. To this end, we formalize the Co-MAPF problem and introduce Cooperative Conflict-Based Search (Co-CBS), a CBS-ba…
FZI Research Center for Information Technology（FZI 信息技术研究中心）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
While motion planning techniques for automated vehicles in a reactive and anticipatory manner are already widely presented, approaches to cooperative motion planning are still remaining. In this paper, we present an approach to enhance common motion planning algorithms, that allows for cooperation with human-driven vehicles. Unlike previous approaches, we integrate the prediction of other traffic participants into the motion planning, such that the influence of the ego vehicle's behavior on the other traffic participants can be taken into account. For this purpose, a new cost functional is pre…
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Robert Bosch GmbH（罗伯特·博世有限公司）
Cooperative trajectory planning methods for automated vehicles can solve traffic scenarios that require a high degree of cooperation between traffic participants. However, for cooperative systems to integrate into human-centered traffic, the automated systems must behave human-like so that humans can anticipate the system's decisions. While Reinforcement Learning has made remarkable progress in solving the decision-making part, it is non-trivial to parameterize a reward model that yields predictable actions. This work employs feature-based Maximum Entropy Inverse Reinforcement Learning combine…
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学控制科学与工程学院 cyber系统研究所）；Polytechnic Institute of Zhejiang University（浙江大学理工学院）；Shanghai AI Laboratory（上海人工智能实验室）
An Online Optimization-Based Trajectory Planning Approach for Cooperative Landing Tasks
Institute of Engineering and Computational Mechanics, University of Stuttgart（斯图加特大学工程与计算力学研究所）；Department of Mechanical Engineering, LUT University（卢托拉大学机械工程系）
This article addresses the problem of Cooperative Coverage Path Planning (C-CPP) for the inspection of complex infrastructures (offline 3D reconstruction) by utilizing multiple Unmanned Autonomous Vehicles (UAVs). The proposed scheme, based on a priori 3D model of the infrastructure under inspection, is able to generate multiple paths for UAVs in order to achieve a complete cooperative coverage in a short time. Initially the infrastructure under inspection is being sliced by horizontal planes, which has the capability of recognizing the branches of the structure and these branches will be hand…
Automated vehicles require the ability to cooperate with humans for smooth integration into today's traffic. While the concept of cooperation is well known, developing a robust and efficient cooperative trajectory planning method is still a challenge. One aspect of this challenge is the uncertainty surrounding the state of the environment due to limited sensor accuracy. This uncertainty can be represented by a Partially Observable Markov Decision Process. Our work addresses this problem by extending an existing cooperative trajectory planning approach based on Monte Carlo Tree Search for conti…
Tokyo Institute of Technology（东京工业大学）；OMRON SINIC X（欧姆龙先尼克斯）
Multi-agent path planning (MAPP) in continuous spaces is a challenging problem with significant practical importance. One promising approach is to first construct graphs approximating the spaces, called roadmaps, and then apply multi-agent pathfinding (MAPF) algorithms to derive a set of conflict-free paths. While conventional studies have utilized roadmap construction methods developed for single-agent planning, it remains largely unexplored how we can construct roadmaps that work effectively for multiple agents. To this end, we propose a novel concept of roadmaps called cooperative timed roa…
Learning-Accelerated Optimization-based Trajectory Planning for Cooperative Aerial-Ground Handover Missions
Institute of Engineering and Computational Mechanics, University of Stuttgart, 70569 Stuttgart, Germany（工程与计算力学研究所，斯图加特大学，德国斯图加特70569）；Mechanical Engineering, LUT University, 53850 Lappeenranta, Finland（机械工程，卢蒂大学，芬兰拉佩恩兰塔53850）
SAP-CoPE: Social-Aware Planning using Cooperative Pose Estimation with Infrastructure Sensor Nodes
Mechanical and Mechatronics Eng. Department, University of Waterloo（机械与机电工程系，滑铁库大学）；Department of Data and Systems Engineering, University of Hong Kong（数据与系统工程系，香港大学）；Mechanical Engineering Department, University of Alberta（机械工程系，阿尔伯塔大学）
CAMP: Cooperative Arm-Hand Motion Planning in Constrained Spaces
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Great Bay University（大湾区大学）；China Pharmaceutical University（中国药科大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；The Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）；University of Belgrade（贝尔格莱德大学）
Zhejiang University（浙江大学）；Huzhou Institute of Zhejiang University（浙江大学湖州研究院）；Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory（复杂系统控制与智能协同技术重点实验室）
Efficient trajectory generation in complex dynamic environments remains an open problem in the unmanned surface vehicle (USV). The perception of the USV is usually interfered with by the swing of the hull and the ambient weather, making it challenging to plan the optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for the coupled USV-UAV system is proposed to ensure that USV can execute a safe and smooth path in the process of autonomous advance in multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing …
We introduce a prioritized system-optimal algorithm for mandatory lane change (MLC) behavior of connected and automated vehicles (CAV) from a dedicated lane. Our approach applies a cooperative lane change that prioritizes the decisions of lane changing vehicles which are closer to the end of the diverging zone (DZ), and optimizes the predicted total system travel time. Our experiments on synthetic data show that the proposed algorithm improves the traffic network efficiency by attaining higher speeds in the dedicated lane and earlier MLC positions while ensuring a low computational time. Our a…
Path planning is a fundamental and extensively explored problem in robotic control. We present a novel economic perspective on path planning. Specifically, we investigate strategic interactions among path planning agents using a game theoretic path planning framework. Our focus is on economic tension between two important objectives: efficiency in the agents' achieving their goals, and safety in navigating towards these. We begin by developing a novel mathematical formulation for path planning that trades off these objectives, when behavior of other agents is fixed. We then use this formulatio…
Multi-goal Rapidly Exploring Random Tree with Safety and Dynamic Constraints for UAV Cooperative Path Planning
Vietnam National University, Hanoi（越南河内国家大学）；Thai Nguyen University of Technology, Thai Nguyen University（太原大学太原工业学院）；University of Transport and Communications（交通通信大学）；Fulbright University Vietnam（越南富布赖特大学）
Balancing Efficiency and Fairness: An Iterative Exchange Framework for Multi-UAV Cooperative Path Planning
The Hong Kong University of Science and Technology（香港科技大学）；College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化工程学院）；College of Computer Science and Engineering, Chongqing Three Gorges University（重庆三峡大学计算机科学与工程学院）；Chinese Academy of Sciences, New Territories, Hong Kong（中国科学院香港新 Territories 分院）；City University of Hong Kong, Kowloon, Hong Kong（香港城市大学）
The Hong Kong University of Science and Technology（香港科技大学）
Developments in cooperative trajectory planning of connected autonomous vehicles (CAVs) have gathered considerable momentum and research attention. Generally, such problems present strong non-linearity and non-convexity, rendering great difficulties in finding the optimal solution. Existing methods typically suffer from low computational efficiency, and this hinders the appropriate applications in large-scale scenarios involving an increasing number of vehicles. To tackle this problem, we propose a novel decentralized iterative linear quadratic regulator (iLQR) algorithm by leveraging the dual…
Cooperative Control of Multi-Quadrotors for Transporting Cable-Suspended Payloads: Obstacle-Aware Planning and Event-Based Nonlinear Model Predictive Control
Toronto Metropolitan University（多伦多都会大学）；University of Alberta（阿尔伯塔大学）
Massachusetts Institute of Technology（麻省理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；NVIDIA（英伟达）
Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs. However, effectively planning for these systems in a manner that ensures each robot is simultaneously productive, and not idle, is challenging due to (1) the close proximity that the robots must operate in to manipulate the structure and (2) the inherent structural partial orderings on when each part can be installed. In this paper, we present a task and motion planning framework that jointly plans safe, low-makespa…
Efficient aerial data collection is important in many remote sensing applications. In large-scale monitoring scenarios, deploying a team of unmanned aerial vehicles (UAVs) offers improved spatial coverage and robustness against individual failures. However, a key challenge is cooperative path planning for the UAVs to efficiently achieve a joint mission goal. We propose a novel multi-agent informative path planning approach based on deep reinforcement learning for adaptive terrain monitoring scenarios using UAV teams. We introduce new network feature representations to effectively learn path pl…
Czech Technical University in Prague（捷克布拉格技术大学）；Delft University of Technology（代尔夫特理工大学）
Cooperative pathfinding is a problem of finding a set of non-conflicting trajectories for a number of mobile agents. Its applications include planning for teams of mobile robots, such as autonomous aircrafts, cars, or underwater vehicles. The state-of-the-art algorithms for cooperative pathfinding typically rely on some heuristic forward-search pathfinding technique, where A* is often the algorithm of choice. Here, we propose MA-RRT*, a novel algorithm for multi-agent path planning that builds upon a recently proposed asymptotically-optimal sampling-based algorithm for finding single-agent sho…
Autonomous Systems Lab, ETH Zürich（苏黎世联邦理工学院自主系统实验室）；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（德国航空航天中心机器人与机电一体化研究所）；Massachusetts Institute of Technology（麻省理工学院）
Virginia Tech（弗吉尼亚理工大学）；California Institute of Technology（加州理工学院）
This paper presents a layered control approach for real-time trajectory planning and control of robust cooperative locomotion by two holonomically constrained quadrupedal robots. A novel interconnected network of reduced-order models, based on the single rigid body (SRB) dynamics, is developed for trajectory planning purposes. At the higher level of the control architecture, two different model predictive control (MPC) algorithms are proposed to address the optimal control problem of the interconnected SRB dynamics: centralized and distributed MPCs. The distributed MPC assumes two local quadra…
FLIP: Real-Time and Resilient Formation Planning for Large-Scale DIstributed Swarms via Point Cloud Registration
Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院智能系统与控制研究所）；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）
Safe and reliable autonomy solutions are a critical component of next-generation intelligent transportation systems. Autonomous vehicles in such systems must reason about complex and dynamic driving scenes in real time and anticipate the behavior of nearby drivers. Human driving behavior is highly nuanced and specific to individual traffic participants. For example, drivers might display cooperative or non-cooperative behaviors in the presence of merging vehicles. These behaviors must be estimated and incorporated in the planning process for safe and efficient driving. In this work, we present…
A Human-Oriented Cooperative Driving Approach: Integrating Driving Intention, State, and Conflict
School of Software Engineering, Xi’an Jiaotong University（软件工程学院，西安交通大学）；School of Artificial Intelligence, Xi’an Jiaotong University（人工智能学院，西安交通大学）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（机械与航空航天工程学院，南洋理工大学）
