This paper addresses a safe planning and control problem for mobile robots operating in communication- and sensor-limited dynamic environments. In this case the robots cannot sense the objects around them and must instead rely on intermittent, external information about the environment, as e.g., in underwater applications. The challenge in this case is that the robots must plan using only this stale data, while accounting for any noise in the data or uncertainty in the environment. To address this challenge we propose a compositional technique which leverages neural networks to quickly plan and control a robot through crowded and dynamic environments using only intermittent information. Specifically, our tool uses reachability analysis and potential fields to train a neural network that is capable of generating safe control actions. We demonstrate our technique both in simulation with an underwater vehicle crossing a crowded shipping channel and with real experiments with ground vehicles in communication- and sensor-limited environments.
In this paper, we develop a distributed intermittent communication and task planning framework for mobile robot teams. The goal of the robots is to accomplish complex tasks, captured by local Linear Temporal Logic formulas, and share the collected information with all other robots and possibly also with a user. Specifically, we consider situations where the robot communication capabilities are not sufficient to form reliable and connected networks while the robots move to accomplish their tasks. In this case, intermittent communication protocols are necessary that allow the robots to temporari…
KTH Royal Institute of Technology（瑞典皇家理工学院）；Cyprus University of Technology（塞浦路斯理工大学）；University of Cyprus（塞浦路斯大学）；Aalto University（阿尔托大学）
We propose a multisensor fusion framework for onboard real-time navigation of a quadrotor in an indoor environment, by integrating sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localization system. The sensor readings from the camera-based object detection algorithm and the UWB localization system arrive intermittently, since the measurements are not readily available. We design a Kalman filter that manages intermittent observations in order to handle and fuse the readings and estimate the pose of the quadrotor f…
In this paper, we extend the recent body of work on planning under uncertainty to include the fact that sensors may not provide any measurement owing to misdetection. This is caused either by adverse environmental conditions that prevent the sensors from making measurements or by the fundamental limitations of the sensors. Examples include RF-based ranging devices that intermittently do not receive the signal from beacons because of obstacles; the misdetection of features by a camera system in detrimental lighting conditions; a LIDAR sensor that is pointed at a glass-based material such as a w…
Virginia Tech（弗吉尼亚理工大学）；University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Oregon State University（俄勒冈州立大学）；University of Maryland, College Park（马里兰大学帕克分校）
Monitoring the health and vigor of grasslands is vital for informing management decisions to optimize rotational grazing in agriculture applications. To take advantage of forage resources and improve land productivity, we require knowledge of pastureland growth patterns that is simply unavailable at state of the art. In this paper, we propose to deploy a team of robots to monitor the evolution of an unknown pastureland environment to fulfill the above goal. To monitor such an environment, which usually evolves slowly, we need to design a strategy for rapid assessment of the environment over la…
University of Bremen（不来梅大学）；Technical University of Munich（慕尼黑工业大学）；Fyuse Inc.（Fyuse公司）；German Aerospace Center (DLR)（德国航空航天中心（DLR））
A pressing question when designing intelligent autonomous systems is how to integrate the various subsystems concerned with complementary tasks. More specifically, robotic vision must provide task-relevant information about the environment and the objects in it to various planning related modules. In most implementations of the traditional Perception-Cognition-Action paradigm these tasks are treated as quasi-independent modules that function as black boxes for each other. It is our view that perception can benefit tremendously from a tight collaboration with cognition. We present RoboSherlock,…
This paper addresses the task of joint multi-agent perception and planning, especially as it relates to the real-world challenge of collision-free navigation for connected self-driving vehicles. For this task, several communication-enabled vehicles must navigate through a busy intersection while avoiding collisions with each other and with obstacles. To this end, this paper proposes a learnable costmap-based planning mechanism, given raw perceptual data, that is (1) distributed, (2) uncertainty-aware, and (3) bandwidth-efficient. Our method produces a costmap and uncertainty-aware entropy map …
Seattle Children’s Research Institute, University of Washington（华盛顿大学西雅图儿童研究所）；Monash University（莫纳什大学）；University of Salford（索尔福德大学）
Advances in hardware technology have enabled more integration of sophisticated software, triggering progress in the development and employment of Unmanned Vehicles (UVs), and mitigating restraints for onboard intelligence. As a result, UVs can now take part in more complex mission where continuous transformation in environmental condition calls for a higher level of situational responsiveness. This paper serves as an introduction to UVs mission planning and management systems aiming to highlight some of the recent developments in the field of autonomous underwater and aerial vehicles in additi…
University of British Columbia（不列颠哥伦比亚大学）；The Chinese University of Hong Kong（香港中文大学）
Unmanned Aerial Vehicles (UAVs) have been implemented for environmental monitoring by using their capabilities of mobile sensing, autonomous navigation, and remote operation. However, in real-world applications, the limitations of on-board resources (e.g., power supply) of UAVs will constrain the coverage of the monitored area and the number of the acquired samples, which will hinder the performance of field estimation and mapping. Therefore, the issue of constrained resources calls for an efficient sampling planner to schedule UAV-based sensing tasks in environmental monitoring. This paper pr…
Motivated by exploration of communication-constrained underground environments using robot teams, we study the problem of planning for intermittent connectivity in multi-agent systems. We propose a novel concept of information-consistency to handle situations where the plan is not initially known by all agents, and suggest an integer linear program for synthesizing information-consistent plans that also achieve auxiliary goals. Furthermore, inspired by network flow problems we propose a novel way to pose connectivity constraints that scales much better than previous methods. In the second part…
This paper considers the problem of distributed state estimation using multi-robot systems. The robots have limited communication capabilities and, therefore, communicate their measurements intermittently only when they are physically close to each other. To decrease the distance that the robots need to travel only to communicate, we divide them into small teams that can communicate at different locations to share information and update their beliefs. Then, we propose a new distributed scheme that combines (i) communication schedules that ensure that the network is intermittently connected, an…
Cellular-enabled Collaborative Robots Planning and Operations for Search-and-Rescue Scenarios
i2CAT Foundation（i2CAT基金会）；NEC Laboratories Europe GmbH（欧洲NEC实验室）；Polytechnic University of Catalonia, UPC（加泰罗尼亚理工大学（UPC））；Institut Català de Recerca i Estudis Avançats, ICREA（加泰罗尼亚高级研究所（ICREA））
Multi-Vehicle Trajectory Planning at V2I-enabled Intersections based on Correlated Equilibrium
Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能自主系统研究院）；National Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统全国重点实验室）；Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education（教育部智能自主系统前沿科学中心）
Sparse-Graph-Enabled Formation Planning for Large-Scale Aerial Swarms
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工业控制国家重点实验室（ cyber-systems and control研究所））；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）
Large Language Model Enhanced Differentiable Trajectory Planning for IoT-Enabled Autonomous Driving
State Key Laboratory of Automotive Simulation and Control, Jilin University（吉林大学汽车仿真与控制国家重点实验室）；Center of Research for Cyber Security and Network (CSNET), Faculty of Computer Science and Information Technology, Universiti Malaya（马来西亚大学计算机科学与信息技术学院网络安全与网络研究中心）；Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心）；IMT, Department of Humanities and Technology, Roskilde University（罗斯基勒大学人文与技术系IMT）；Center for Security, Theory and Algorithmic Research, International Institute of Information Technology（国际信息技术研究所安全、理论与算法研究中心）；Department of Computer Science and Engineering, College of Informatics, Korea University（韩国大学信息学院计算机科学与工程系）
Constraining contacts to remain fixed on an object during manipulation limits the potential workspace size, as motion is subject to the hand's kinematic topology. Finger gaiting is one way to alleviate such restraints. It allows contacts to be freely broken and remade so as to operate on different manipulation manifolds. This capability, however, has traditionally been difficult or impossible to practically realize. A finger gaiting system must simultaneously plan for and control forces on the object while maintaining stability during contact switching. This work alleviates the traditional req…
This paper presents a human-robot trust integrated task allocation and motion planning framework for multi-robot systems (MRS) in performing a set of tasks concurrently. A set of task specifications in parallel are conjuncted with MRS to synthesize a task allocation automaton. Each transition of the task allocation automaton is associated with the total trust value of human in corresponding robots. Here, the human-robot trust model is constructed with a dynamic Bayesian network (DBN) by considering individual robot performance, safety coefficient, human cognitive workload and overall evaluatio…
From Prompt to Physical Actuation: Holistic Threat Modeling of LLM-Enabled Robotic Systems
School of Informatics, Computing, and Cyber Systems, Northern Arizona University（信息学、计算与网络系统学院，北亚利桑那大学）；Department of Software Science, Tallinn University of Technology（软件科学系，塔林技术大学）
Path Planning Optimisation for SParse, AwaRe and Cooperative Networked Aerial Robot Teams (SpArC-NARTs): Optimisation Tool and Ground Sensing Coverage Use Cases
LLM A*: Human in the Loop Large Language Models Enabled A* Search for Robotics
Department of Computing and Mathematics, Manchester Metropolitan University（计算与数学系，曼彻斯特 Metropolitan 大学）；Manchester Metropolitan University（曼彻斯特 Metropolitan 大学）；Johnson Matthey Technology Centre, Johnson Matthey（Johnson Matthey 技术中心，Johnson Matthey）
University of Michigan（密歇根大学）；University of Tuebingen（图宾根大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；New York University（纽约大学）
Humanoid robots dynamically navigate an environment by interacting with it via contact wrenches exerted at intermittent contact poses. Therefore, it is important to consider dynamics when planning a contact sequence. Traditional contact planning approaches assume a quasi-static balance criterion to reduce the computational challenges of selecting a contact sequence over a rough terrain. This however limits the applicability of the approach when dynamic motions are required, such as when walking down a steep slope or crossing a wide gap. Recent methods overcome this limitation with the help of …
Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
In this paper, we solve a multi-robot informative path planning (MIPP) task under the influence of uncertain communication and adversarial attackers. The goal is to create a multi-robot system that can learn and unify its knowledge of an unknown environment despite the presence of corrupted robots sharing malicious information. We use a Gaussian Process (GP) to model our unknown environment and define informativeness using the metric of mutual information. The objectives of our MIPP task is to maximize the amount of information collected by the team while maximizing the probability of resilien…
Rutgers University（罗格斯大学）；Massachusetts Institute of Technology（麻省理工学院）
microMVP is an affordable, portable, and open source micro-scale mobile robot platform designed for robotics research and education. As a complete and unique multi-vehicle platform enabled by 3D printing and the maker culture, microMVP can be easily reproduced and requires little maintenance: a set of six micro vehicles, each measuring $8\times 5\times 6$ cubic centimeters and weighing under $100$ grams, and the accompanying tracking platform can be fully assembled in under two hours, all from readily available components. In this paper, we describe microMVP's hardware and software architectur…
Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Texas A&M University（得克萨斯农工大学）
We study planning problems faced by robots operating in uncertain environments with incomplete knowledge of state, and actions that are noisy and/or imprecise. This paper identifies a new problem sub-class that models settings in which information is revealed only intermittently through some exogenous process that provides state information periodically. Several practical domains fit this model, including the specific scenario that motivates our research: autonomous navigation of a planetary exploration rover augmented by remote imaging. With an eye to efficient specialized solution methods, w…
Rutgers, the State University of New Jersey（罗格斯大学新泽西州立大学）
We propose a novel centralized and decoupled algorithm, DDM, for solving multi-robot path planning problems in grid graphs, targeting on-demand and automated warehouse-like settings. Two settings are studied: a traditional one whose objective is to move a set of robots from their respective initial vertices to the goal vertices as quickly as possible, and a dynamic one which requires frequent re-planning to accommodate for goal configuration adjustments. Among other techniques, DDM is mainly enabled through exploiting two innovative heuristics: path diversification and optimal sub-problem solu…
Georgia Institute of Technology（佐治亚理工学院）；George W. Woodruff School of Mechanical Engineering（乔治·W·伍德拉夫机械工程学院）；Department of Electrical and Computer Engineering（电气与计算机工程系）
As robots move from the laboratory into the real world, motion planning will need to account for model uncertainty and risk. For robot motions involving intermittent contact, planning for uncertainty in contact is especially important, as failure to successfully make and maintain contact can be catastrophic. Here, we model uncertainty in terrain geometry and friction characteristics, and combine a risk-sensitive objective with chance constraints to provide a trade-off between robustness to uncertainty and constraint satisfaction with an arbitrarily high feasibility guarantee. We evaluate our a…
University of Baghdad（巴格达大学）；Bahria University（巴里亚大学）
This paper outlines a modification on the Bat Algorithm (BA), a kind of swarm optimization algorithms with for the mobile robot navigation problem in a dynamic environment. The main objectives of this work are to obtain the collision-free, shortest, and safest path between starting point and end point assuming a dynamic environment with moving obstacles. A New modification on the frequency parameter of the standard BA has been proposed in this work, namely, the Modified Frequency Bat Algorithm (MFBA). The path planning problem for the mobile robot in a dynamic environment is carried out using …
A defining feature of sampling-based motion planning is the reliance on an implicit representation of the state space, which is enabled by a set of probing samples. Traditionally, these samples are drawn either probabilistically or deterministically to uniformly cover the state space. Yet, the motion of many robotic systems is often restricted to "small" regions of the state space, due to, for example, differential constraints or collision-avoidance constraints. To accelerate the planning process, it is thus desirable to devise non-uniform sampling strategies that favor sampling in those regio…
Safe Planning in Unknown Environments Using Conformalized Semantic Maps
Department of Electrical and Systems Engineering, Washington University in St. Louis（华盛顿大学圣路易斯分校电气与系统工程系）；Department of Electrical and Computer Engineering, University of California, San Diego（加州大学圣地亚哥分校电气与计算机工程系）；Department of Electrical and Systems Engineering, University of Pennsylvania（宾夕法尼亚大学电气与系统工程系）
To move through the world, mobile robots typically use a receding-horizon strategy, wherein they execute an old plan while computing a new plan to incorporate new sensor information. A plan should be dynamically feasible, meaning it obeys constraints like the robot's dynamics and obstacle avoidance; it should have liveness, meaning the robot does not stop to plan so frequently that it cannot accomplish tasks; and it should be optimal, meaning that the robot tries to satisfy a user-specified cost function such as reaching a goal location as quickly as possible. Reachability-based Trajectory Des…
