Path planning is an important component of au- tonomous mobile sensing systems. This paper studies upper and lower bounds of communication performance over Gaussian sen- sor networks, to drive power-distortion metrics for path planning problems. The Gaussian multiple-access channel is employed as a channel model and two source models are considered. In the first setting, the underlying source is estimated with minimum mean squared error, while in the second, reconstruction of a random spatial field is considered. For both problem settings, the upper and the lower bounds of sensor power-distortion curve are derived. For both settings, the upper bounds follow from the amplify-and-forward scheme and the lower bounds admit a unified derivation based on data processing inequality and tensorization property of the maximal correlation measure. Next, closed-form solutions of the optimal power allocation problems are obtained under a weighted sum-power constraint. The gap between the upper and the lower bounds is analyzed for both weighted sum and individual power constrained settings. Finally, these metrics are used to drive a path planning algorithm and the effects of power-distortion metrics, network parameters, and power optimization on the optimized path selection are analyzed.
University of Hamburg（汉堡大学）；TU Darmstadt（达姆施塔特工业大学）；Tampere University（坦佩雷大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
3D scene models are useful in robotics for tasks such as path planning, object manipulation, and structural inspection. We consider the problem of creating a 3D model using depth images captured by a team of multiple robots. Each robot selects a viewpoint and captures a depth image from it, and the images are fused to update the scene model. The process is repeated until a scene model of desired quality is obtained. Next-best-view planning uses the current scene model to select the next viewpoints. The objective is to select viewpoints so that the images captured using them improve the quality…
Communication-Aware Map Compression for Online Path-Planning: A Rate-Distortion Approach
Department of Aerospace Engineering of Georgia Institute of Technology（佐治亚理工学院航空航天工程系）；Department of Electrical and Computer Engineering, University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校电气与计算机工程系）
This paper addresses the problem of planning for a robot with a directional obstacle-detection sensor that must move through a cluttered environment. The planning objective is to remain safe by finding a path for the complete robot, including sensor, that guarantees that the robot will not move into any part of the workspace before it has been seen by the sensor. Although a great deal of work has addressed a version of this problem in which the "field of view" of the sensor is a sphere around the robot, there is very little work addressing robots with a narrow or occluded field of view. We giv…
In this letter, a resilient path planning scheme is proposed to navigate a UAV to the planned (nominal) destination with minimum energy-consumption in the presence of a smart attacker. The UAV is equipped with two sensors, a GPS sensor, which is vulnerable to the spoofing attacker, and a well-functioning Ultra-Wideband (UWB) sensor, which is possible to be fooled. We show that a covert attacker can significantly deviate the UAV's path by simultaneously corrupting the GPS signals and forging control inputs without being detected by the UWB sensor. The prerequisite for the attack occurrence is 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…
SHRUMS: Sensor Hallucination for Real-time Underwater Motion Planning with a Compact 3D Sonar
Department of Computer Science and Engineering, Texas A&M University（计算机科学与工程系，德克萨斯A&M大学）；Aquaculture Robotics and Automation, SINTEF Ocean（水产养殖机器人与自动化，SINTEF海洋）
Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE（弗劳恩霍夫通信、信息处理与工效学研究所FKIE）；University of Bonn（波恩大学）
Safe navigation of Micro Aerial Vehicles (MAVs) requires not only obstacle-free flight paths according to a static environment map, but also the perception of and reaction to previously unknown and dynamic objects. This implies that the onboard sensors cover the current flight direction. Due to the limited payload of MAVs, full sensor coverage of the environment has to be traded off with flight time. Thus, often only a part of the environment is covered. We present a combined allocentric complete planning and trajectory optimization approach taking these sensor visibility constraints into acco…
Motion planning at urban intersections that accounts for the situation context, handles occlusions, and deals with measurement and prediction uncertainty is a major challenge on the way to urban automated driving. In this work, we address this challenge with a sampling-based optimization approach. For this, we formulate an optimal control problem that optimizes for low risk and high passenger comfort. The risk is calculated on the basis of the perception information and the respective uncertainty using a risk model. The risk model combines set-based methods and probabilistic approaches. Thus, …
This paper presents a decentralized Gaussian Process (GP) learning, fusion, and planning (RESIN) formalism for mobile sensor networks to actively learn target motion models. RESIN is characterized by both computational and communication efficiency, and the robustness to rumor propagation in sensor networks. By using the weighted exponential product rule and the Chernoff information, a rumor-robust decentralized GP fusion approach is developed to generate a globally consistent target trajectory prediction from local GP models. A decentralized information-driven path planning approach is then pr…
Faculty of Information Technology, Monash University, Australia（澳大利亚莫纳什大学信息技术学院）
This paper describes a reflexive multilayered mission planner with a mounted energy efficient local path planner for Unmanned Underwater Vehicle's (UUV) navigation throughout the complex subsea volume in a time-variant semi-dynamic operation network. The UUV routing protocol in Underwater Wireless Sensor Network (UNSW) is generalized with a homogeneous Dynamic Knapsack-Traveler Salesman Problem emerging with an adaptive path planning mechanism to address UUV's long-duration missions on dynamically changing subsea volume. The framework includes a base layer of global path planning, an inner lay…
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Tokyo Institute of Technology（东京工业大学）
We propose a path planning methodology for a mobile robot navigating through an obstacle-filled environment to generate a reference path that is traceable with moderate sensing efforts. The desired reference path is characterized as the shortest path in an obstacle-filled Gaussian belief manifold equipped with a novel information-geometric distance function. The distance function we introduce is shown to be an asymmetric quasi-pseudometric and can be interpreted as the minimum information gain required to steer the Gaussian belief. An RRT*-based numerical solution algorithm is presented to sol…
One of the major impacts of climatic changes is due to destroying of forest. Destroying of forest takes place in many ways but the majority of the forest is destroyed due to wild forest fires. In this paper we have presented a path planning algorithm for extinguishing fires which uses Wireless Sensor and Actor Networks (WSANs) for detecting fires. Since most of the works on forest fires are based on Wireless Sensor Networks (WSNs) and a collection of work has been done on coverage, message transmission, deployment of nodes, battery power depletion of sensor nodes in WSNs we focused our work in…
University of California at Berkeley（加州大学伯克利分校）；California PATH, University of California at Berkeley（加州大学伯克利分校加州PATH）
Autonomous cars have to navigate in dynamic environment which can be full of uncertainties. The uncertainties can come either from sensor limitations such as occlusions and limited sensor range, or from probabilistic prediction of other road participants, or from unknown social behavior in a new area. To safely and efficiently drive in the presence of these uncertainties, the decision-making and planning modules of autonomous cars should intelligently utilize all available information and appropriately tackle the uncertainties so that proper driving strategies can be generated. In this paper, …
Industrial robots are widely used in various manufacturing environments due to their efficiency in doing repetitive tasks such as assembly or welding. A common problem for these applications is to reach a destination without colliding with obstacles or other robot arms. Commonly used sampling-based path planning approaches such as RRT require long computation times, especially in complex environments. Furthermore, the environment in which they are employed needs to be known beforehand. When utilizing the approaches in new environments, a tedious engineering effort in setting hyperparameters ne…
Learning to Optimize UAV Path Planning for Data Sensing in Wireless Sensor Networks
South China University of Technology（华南理工大学）；South China Normal University（华南师范大学）；Aerospace Information Research Institute, CAS（中国科学院空天信息创新研究院）；Nankai University（南开大学）
Safe and efficient path planning is crucial for autonomous mobile robots. A prerequisite for path planning is to have a comprehensive understanding of the 3D structure of the robot's environment. On MAVs this is commonly achieved using low-cost sensors, such as stereo or RGB-D cameras. These sensors may fail to provide depth measurements in textureless or IR-absorbing areas and have limited effective range. In path planning, this results in inefficient trajectories or failure to recognize a feasible path to the goal, hence significantly impairing the robot's mobility. Recent advances in deep l…
Digital Twin-Guided Robot Path Planning: A Beta-Bernoulli Fusion with Large Language Model as a Sensor
Department of Civil, Construction, and Environmental Engineering, San Diego State University, San Diego, CA, United States（土木、建设与环境工程系，圣地亚哥州立大学，美国圣地亚哥，加利福尼亚州）；Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA, United States（电气与计算机工程系，加州大学圣地亚哥分校，美国圣地亚哥，加利福尼亚州）
Informative path planning (IPP) is used to design paths for robotic sensor platforms to extract the best/maximum possible information about a quantity of interest while operating under a set of constraints, such as the dynamic feasibility of vehicles. The key challenges of IPP are the strong coupling in multiple layers of decisions: the selection of locations to visit, the allocation of sensor platforms to those locations; and the processing of the gathered information along the paths. This paper presents a systematic procedure for IPP and environmental mapping using multiple UAV sensor platfo…
Bayesian Networks for Path-Based Sensors: Gathering Information and Path Planning in Communication Denied Environments
University of Maryland, College Park, MD, US.（美国马里兰大学学院公园分校）；Temple University, Philadelphia, PA, US.（美国 Temple 大学）；Colorado School of Mines, Golden, CO, US.（科罗拉多矿业学院）；U.S. Naval Research Lab (Retired), DC, US.（美国海军研究实验室（退休））
Inria（法国国家信息与自动化研究所）；École normale supérieure（巴黎高等师范学院）；CNRS（法国国家科学研究中心）；PSL Research University（巴黎文理研究大学）；University Grenoble Alpes（格勒诺布尔大学）；Grenoble INP（格勒诺布尔理工学院）
Motion planning and obstacle avoidance is a key challenge in robotics applications. While previous work succeeds to provide excellent solutions for known environments, sensor-based motion planning in new and dynamic environments remains difficult. In this work we address sensor-based motion planning from a learning perspective. Motivated by recent advances in visual recognition, we argue the importance of learning appropriate representations for motion planning. We propose a new obstacle representation based on the PointNet architecture and train it jointly with policies for obstacle avoidance…
Fast and reliable trajectory planning is a key requirement of autonomous vehicles. In this paper we introduce a novel technique for planning the route of an autonomous vehicle on a straight rural road using the Spin model checker. We show how we can combine Spins ability to identify paths violating temporal properties with sensor information from a 3D Unity simulation of an autonomous vehicle, to plan and perform consecutive overtaking manoeuvres on a traffic heavy road. This involves discretising the sensory information and combining multiple sequential Spin models with a Linear Time Temporal…
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）
EURECOM（欧洲通信学院）；Technical University of Munich（慕尼黑工业大学）
Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods. We propose a new end-to-end reinforcement learning (RL) approach to UAV-enabled data collection from Internet of Things (IoT) devices in an urban environment. An autonomous drone is tasked with gathering data from distributed sensor nodes subject to limited flying time and obstacle avoidance. While previous approaches, learning and non-learning based, must perform expensive recomputations or relearn a behavior when important scenario paramet…
The University of Adelaide（阿德莱德大学）；Curtin University（科廷大学）
We consider the challenging problem of online planning for a team of agents to autonomously search and track a time-varying number of mobile objects under the practical constraint of detection range limited onboard sensors. A standard POMDP with a value function that either encourages discovery or accurate tracking of mobile objects is inadequate to simultaneously meet the conflicting goals of searching for undiscovered mobile objects whilst keeping track of discovered objects. The planning problem is further complicated by misdetections or false detections of objects caused by range limited s…
Flippers are essential components of tracked robot locomotion systems for unstructured terrain, especially within a rescue scenario. Achieving full and semi-autonomy for such rescue robots is the goal of many research efforts. In this work, we propose an algorithm to plan the morphologies of a small rescue robot with four flippers over 3D ground without any extra sensor, such as pressure sensor. To achieve the goal, we simplify the rescue robot as a skeleton on inflated terrain. Its morphology can be represented by configurations of several parameters. Then we plan the mobile movement on 3D te…
NATO STO Centre for Maritime Research and Experimentation (CMRE)（北约科技组织海事研究与实验中心）
In this paper we address the mine countermeasures (MCM) search problem for an autonomous underwater vehicle (AUV) surveying the seabed using a side-looking sonar. We propose a coverage path planning method that adapts the AUV track spacing with the objective of collecting better data. We achieve this by shifting the coverage overlap at the tail of the sensor range where the lowest data quality is expected. To assess the algorithm, we collected data from three at-sea experiments. The adaptive survey allowed the AUV to recover from a situation where the sensor range was overestimated and resulte…
We consider the problem of sample-based feedback motion planning from measurements affected by systematic errors. Our previous work presented output feedback controllers that use measurements from landmarks in the environment to navigate through a cell-decomposable environment using duality, Control Lyapunov and Barrier Functions (CLF, CBF), and Linear Programming. In this paper, we build on this previous work with a novel strategy that allows the use of measurements affected by systematic errors in perceived depth (similarly to what might be generated by vision-based sensors), as opposed to a…
University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）；University of Washington（华盛顿大学）
Intelligent mobile sensors, such as uninhabited aerial or underwater vehicles, are becoming prevalent in environmental sensing and monitoring applications. These active sensing platforms operate in unsteady fluid flows, including windy urban environments, hurricanes, and ocean currents. Often constrained in their actuation capabilities, the dynamics of these mobile sensors depend strongly on the background flow, making their deployment and control particularly challenging. Therefore, efficient trajectory planning with partial knowledge about the background flow is essential for teams of mobile…
Embark Trucks Inc.（Embark卡车公司）；Scania CV（斯堪尼亚CV公司）；Autonomous Transport Solutions（自主运输解决方案部门）
Maneuvering a general 2-trailer with a car-like tractor in backward motion is a task that requires significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path-following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice-based path planning framework is developed in order to generate kinematically feasible and collision-free paths and a path-following controller is designed to stabi…
We consider the problem of autonomous navigation using limited information from a remote sensor network. Because the remote sensors are power and bandwidth limited, we use event-triggered (ET) estimation to manage communication costs. We introduce a fast and efficient sampling-based planner which computes motion plans coupled with ET communication strategies that minimize communication costs, while satisfying constraints on the probability of reaching the goal region and the point-wise probability of collision. We derive a novel method for offline propagation of the expected state distribution…
RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment
The Hong Kong University of Science and Technology（香港科技大学）；Division of Emerging Interdisciplinary Areas（新兴跨学科领域）；Robotics and Autonomous Systems Thrust（机器人与自主系统方向）；Department of Electronic and Computer Engineering（电子与计算机工程系）
California Institute of Technology（加州理工学院）；Stanford University（斯坦福大学）；NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；University of California Los Angeles（加州大学洛杉矶分校）
We present a method for solving the coverage problem with the objective of autonomously exploring an unknown environment under mission time constraints. Here, the robot is tasked with planning a path over a horizon such that the accumulated area swept out by its sensor footprint is maximized. Because this problem exhibits a diminishing returns property known as submodularity, we choose to formulate it as a tree-based sequential decision making process. This formulation allows us to evaluate the effects of the robot's actions on future world coverage states, while simultaneously accounting for …
Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Florida（佛罗里达大学）；Providence College（普罗维登斯学院）；Florida International University（佛罗里达国际大学）
This paper presents an integrated system for performing precision harvesting missions using a legged harvester. Our harvester performs a challenging task of autonomous navigation and tree grabbing in a confined, GPS denied forest environment. Strategies for mapping, localization, planning, and control are proposed and integrated into a fully autonomous system. The mission starts with a human mapping the area of interest using a custom-made sensor module. Subsequently, a human expert selects the trees for harvesting. The sensor module is then mounted on the machine and used for localization wit…
University of Technology, Sydney（悉尼科技大学）；ETH Zürich（苏黎世联邦理工学院）
Unmanned Aerial Vehicles (UAVs) represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general Informative Path Planning (IPP) framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori . The approach is capable of learning and focusing on regions of interest via adaptatio…
We present a motion planning algorithm for a class of uncertain control-affine nonlinear systems which guarantees runtime safety and goal reachability when using high-dimensional sensor measurements (e.g., RGB-D images) and a learned perception module in the feedback control loop. First, given a dataset of states and observations, we train a perception system that seeks to invert a subset of the state from an observation, and estimate an upper bound on the perception error which is valid with high probability in a trusted domain near the data. Next, we use contraction theory to design a stabil…
Centre for Wireless Communications (CWC), University of Oulu（奥卢大学无线通信中心（CWC））
Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion. \textit{Age of Information} (AoI) is a metric that quantifies information timeliness, i.e., the freshness of the received information or status update. This work considers a setup of deployed sensors in an IoT network, where multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of t…
If a robot can predict crowds in parts of its environment that are inaccessible to its sensors, then it can plan to avoid them. This paper proposes a fast, online algorithm that learns average crowd densities in different areas. It also describes how these densities can be incorporated into existing navigation architectures. In simulation across multiple challenging crowd scenarios, the robot reaches its target faster, travels less, and risks fewer collisions than if it were to plan with the traditional A* algorithm.
We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements from an underlying random process, code and transmit those measurement samples to a collector node in a cooperative multiple access channel with potential feedback, and reconstruct the entire random process at the collector node. We provide lower and upper bounds for the minimum achievable expected distortion when the underlying random process is Gaussian. When the Gaussian random process satisfies some general condi…
We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements from an underlying random process, code and transmit those measurement samples to a collector node in a cooperative multiple access channel with imperfect feedback, and reconstruct the entire random process at the collector node. We provide lower and upper bounds for the minimum achievable expected distortion when the underlying random process is Gaussian. In the case where the random process satisfies some general c…
We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements from an underlying random process, code and transmit those measurement samples to a collector node in a cooperative multiple access channel with feedback, and reconstruct the entire random process at the collector node. We provide lower and upper bounds for the minimum achievable expected distortion when the underlying random process is stationary and Gaussian. In the case where the random process is also Markovian, …
Sequential rate-distortion (SRD) theory provides a framework for studying the fundamental trade-off between data-rate and data-quality in real-time communication systems. In this paper, we consider the SRD problem for multi-dimensional time-varying Gauss-Markov processes under mean-square distortion criteria. We first revisit the sensor-estimator separation principle, which asserts that considered SRD problem is equivalent to a joint sensor and estimator design problem in which data-rate of the sensor output is minimized while the estimator's performance satisfies the distortion criteria. We t…
In this paper, we investigate the problem of jointly selecting a predefined number of energy-harvesting (EH) sensors and computing the optimal power allocation. The ultimate goal is to minimize the reconstruction distortion at the fusion center. This optimization problem is, unfortunately, non-convex. To circumvent that, we propose two suboptimal strategies: (i) a joint sensor selection and power allocation (JSS-EH) scheme that, we prove, is capable of iteratively finding a stationary solution of the original problem from a sequence of surrogate convex problems; and (ii) a separate sensor sele…
This paper is concerned with decentralized estimation of a Gaussian source using multiple sensors. We consider a diversity scheme where only the sensor with the best channel sends their measurements over a fading channel to a fusion center, using the analog amplify and forwarding technique. The fusion centre reconstructs an MMSE estimate of the source based on the received measurements. A distributed version of the diversity scheme where sensors decide whether to transmit based only on their local channel information is also considered. We derive asymptotic expressions for the expected distort…
This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplify-and-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing dist…
We consider the distributed source coding problem in which correlated data picked up by scattered sensors has to be encoded separately and transmitted to a common receiver, subject to a rate-distortion constraint. Although near-tooptimal solutions based on Turbo and LDPC codes exist for this problem, in most cases the proposed techniques do not scale to networks of hundreds of sensors. We present a scalable solution based on the following key elements: (a) distortion-optimized index assignments for low-complexity distributed quantization, (b) source-optimized hierarchical clustering based on t…
*The following abbreviates the abstract. Please refer to the thesis for the full abstract.* After a disaster, locating and extracting victims quickly is critical because mortality rises rapidly after the first two days. To assist search and rescue teams and improve response times, teams of camera-equipped aerial robots can engage in tasks such as mapping buildings and locating victims. These sensing tasks encapsulate difficult (NP-Hard) problems. One way to simplify planning for these tasks is to focus on maximizing sensing performance over a short time horizon. Specifically, consider the prob…
We consider online scheduling for an energy harvesting communication system where a sensor node collects samples from a Gaussian source and sends them to a destination node over a Gaussian channel. The sensor is equipped with a finite-sized battery that is recharged by an independent and identically distributed (i.i.d.) energy harvesting process over time. The goal is to minimize the long term average distortion of the source samples received at the destination. We study two problems: the first is when sampling is cost-free, and the second is when there is a sampling cost incurred whenever sam…
One of the main challenges facing wireless sensor networks (WSNs) is the limited power resources available at small sensor nodes. It is therefore desired to reduce the power consumption of sensors while keeping the distortion between the source information and its estimate at the fusion centre (FC) below a specific threshold. In this paper, given the channel state information at the FC, we propose a subset selection algorithm of sensor nodes to reduce the average transmission power of the WSN. We assume the channels between the source and the sensors to be correlated fading channels, modeled b…
