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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Bourgault, Frederic Chokshi, Aakash Wang, John Shah, Danelle Schoenberg, Jonathan Iyer, Ramnath Cedano, Franco Campbell, Mark Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA

ISBN: (纸本)9781424420575

In this paper, scalable collaborative human-robot systems for information gathering applications are approached as a decentralized Bayesian sensor network problem. Human-computer augmented nodes and autonomous mobile sensor platforms are collaborating on a peer-to-peer basis by sharing information via wireless communication network. For each node, a computer (onboard the platform or carried by the human) implements both a decentralized Bayesian data fusion algorithm and a decentralized Bayesian control negotiation algorithm. The individual node controllers iteratively negotiate anonymously with each other in the information space to find cooperative search plans based on both observed and predicted information that explicitly consider the platforms (humans and robots) motion models, their sensors detection functions, as well as the target arbitrary motion model. The results of a collaborative multi-target search experiment conducted with a team of four autonomous mobile sensor platforms and five humans carrying small portable computers with wireless communication are presented to demonstrate the efficiency of the approach.

关键词： sensor networks

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A next best view algorithm for object reconstruction

A next best view algorithm for object reconstruction

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Conference on sensor fusion amd decentralized control in robotic systems

作者： Wong, LM Dumont, C Abidi, MA Univ Tennessee Dept Elect Engn IRIS Lab Knoxville TN 37996 USA

ISBN: (纸本)0819429848

The goal of sensor planning in 3-D object reconstruction is to acquire enough 2-D information about an object to create a 3-D model of that object. This requires the acquisition of images from different viewpoints, the registration of each view, and the integration of all acquired information into a single model. Our focus is on the acquisition stage;we want to determine sensor poses or viewpoints that provide the next best view in an object reconstruction task. The Next Best View (NBV) is the determination of a new sensor position that reveals an optimal amount of unknown information about the object being modeled. The goal of a NBV reconstruction system is usually to model an object using the smallest number of views. Our approach to complete this task is to study a volumetric representation of the model after a new view is obtained. Since the volumetric model consists of known and unknown information, we want to find the viewpoint from which the largest amount of unknown data can be acquired. The NBV algorithm is integrated into a system that acquires synthetic range data from a computer object model, calculates new viewpoints according to an objective function, and reconstructs a complete volumetric model of the object of interest. The NBV algorithm is described in depth and experimental results are given. Also included is a review of related work.

关键词： next best view sensor planning 3-D reconstruction

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decentralized fusion of Radar and Lidar Data for Tracking Extended Objects

Decentralized Fusion of Radar and Lidar Data for Tracking Ex...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Arora, Prashant Goron, Gael Roose, Trevor Dionne, Greg Kivelevitch, Elad Pellissier, Vincent Gentile, Rick MathWorks 3 Apple Hill Dr Natick MA 01760 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

Object tracking is a crucial task for autonomous systems, which require precise estimation of their surroundings to support decision making, planning, and control. High resolution sensors such as radar and lidar are frequently used in autonomous systems to assist in estimation of the surroundings. This estimation includes not only the kinematics of the objects such as position and velocity, but also their physical attributes such as dimensions and orientation. In this work, we propose a framework to ease the challenge of estimating extended objects by fusing radar data and lidar data. In this framework, the radar data is processed by an extended object tracker, which can process multiple measurements per object. The lidar data is preprocessed to extract bounding boxes and the bounding boxes are tracked using a conventional "point target" tracker. Further, we propose to fuse tracks of extended objects from each system using a decentralized fusion scheme. Our approach is evaluated using simulated sensor data in the context of autonomous driving in highway environments. Simulation results show that fusing extended object tracks from radar and lidar results in better tracking performance as compared to each individual sensor.

关键词： Radar Detection LIDAR sensor Environment sensors autonomous systems Interacting Multiple Model Unscented Kalman Filter Yaw Covariance Intersection Deep Learning Computer Vision

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Distributed Algorithms for Environment Partitioning in Mobile robotic Networks

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IEEE TRANSACTIONS ON AUTOMATIC control 2011年第8期56卷 1834-1848页

作者： Pavone, Marco Arsie, Alessandro Frazzoli, Emilio Bullo, Francesco CALTECH Jet Prop Lab Pasadena CA 91109 USA Univ Toledo Dept Math Toledo OH 43606 USA MIT Lab Informat & Decis Syst Dept Aeronaut & Astronaut Cambridge MA 02139 USA Univ Calif Santa Barbara Ctr Control Engn & Computat Santa Barbara CA 93106 USA

A widely applied strategy for workload sharing is to equalize the workload assigned to each resource. In mobile multiagent systems, this principle directly leads to equitable partitioning policies whereby: 1) the environment is equitably divided into subregions of equal measure;2) one agent is assigned to each subregion;and 3) each agent is responsible for service requests originating within its own subregion. The current lack of distributed algorithms for the computation of equitable partitions limits the applicability of equitable partitioning policies to limited-size multiagent systems operating in known, static environments. In this paper, first we design provably correct and spatially distributed algorithms that allow a team of agents to compute a convex and equitable partition of a convex environment. Second, we discuss how these algorithms can be extended so that a team of agents can compute, in a spatially distributed fashion, convex and equitable partitions with additional features, e.g., equitable and median Voronoi diagrams. Finally, we discuss two application domains for our algorithms, namely dynamic vehicle routing for mobile robotic networks and wireless ad hoc networks. Through these examples, we show how one can couple the algorithms presented in this paper with equitable partitioning policies to make these amenable to distributed implementation. More in general, we illustrate a systematic approach to devise spatially distributed control policies for a large variety of multiagent coordination problems. Our approach is related to the classic Lloyd algorithm and exploits the unique features of power diagrams.

关键词： autonomous systems cooperative control decentralized control multirobot systems sensor networks

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Active-Vision for the autonomous Surveillance of Dynamic, Multi-Object Environments

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JOURNAL OF INTELLIGENT & robotic systems 2009年第4期54卷 567-593页

作者： Bakhtari, Ardevan Mackay, Matthew Benhabib, Beno Univ Toronto Dept Mech & Ind Engn Comp Integrated Mfg Lab Toronto ON M5S 3G8 Canada

This paper presents a novel method for active-vision-based sensing-system reconfiguration for the autonomous surveillance of an object-of-interest as it travels through a multi-object dynamic workspace with an a priori unknown trajectory. Several approaches have been previously proposed to address the problem of sensor selection and control. However, these have primarily relied on off-line planning methods and rarely utilized on-line planning to compensate for unexpected variations in a target's trajectory. The method proposed in this paper, on the other hand, uses a multi-agent system for on-line sensing-system reconfiguration, eliminating the need for any a priori knowledge of the target's trajectory. Thus, it is robust to unexpected variations in the environment. Simulations and experiments have shown that the use of dynamic sensors with the proposed on-line reconfiguration algorithm can tangibly improve the performance of an active-surveillance system.

关键词： Active vision Surveillance sensor fusion Multi-agent system

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Intuitive intelligent sensor fusion with highly autonomous sensors

Intuitive intelligent sensor fusion with highly autonomous s...

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2001 ASME International Mechanical Engineering Congress and Exposition

作者： Yuan, Jane Xiaojing Figueroa, Fernando Tulane University Mechanical Engineering Department New Orleans LA United States

The objective of sensor fusion is to synergistically combine different sources of sensory information into one representational format to provide more complete and precise interpretation of the system. A generic sensor fusion framework based on a highly autonomous sensor (HAS) model is presented. The framework provides freedom to choose different data fusion methods and combine them together to achieve better performance. In the context of HAS's, this paper describes a hierarchical decentralized sensor-fusion approach based on a qualitative theory to interpret measurements, and on qualitative procedures to reason and make decisions based on the measurement interpretations. In this manner, heuristic fusion methods are applied at a high-qualitative level as well as at a numerical level when necessary. This approach implements intuitive (effective) methods to monitor, diagnose, and compensate processes/systems and their sensors.

关键词： sensors

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Guest Editorial Special Issue on control of Very-Large-Scale robotic (VLSR) Networks

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IEEE TRANSACTIONS ON control OF NETWORK systems 2021年第2期8卷 527-529页

作者： Ferrari, Silvia Wettergren, Thomas A. Linares, Richard LeGrand, Keith Cornell Univ Dept Mech & Aerosp Engn Ithaca NY 14853 USA Naval Undersea Warfare Ctr Operat & Informat Sci Newport RI 02841 USA MIT Dept Aeronaut & Astronaut 77 Massachusetts Ave Cambridge MA 02139 USA Cornell Univ Dept Mech & Aerosp Engn Ithaca NY 14850 USA

Future applications of autonomous systems promise to involve increasingly large numbers of collaborative robots individually equipped with onboard sensors, actuators, and wireless communications. By sharing and coordinating information, plans, and decisions, these very-large-scale robotic (VLSR) networks can dramatically improve their performance and operate over long periods of time with little or no human intervention. controlling many collaborative agents to this day presents significant technical challenges. Besides requiring satisfactory communications, the amount of computation associated with most coordinated control algorithms increases with the number of agents. It is well-known, for example, that the optimal control of N collaborative agents for path planning and obstacle avoidance is a PSPACE-hard problem. Also, while necessary for performing basic tasks such as localization and mapping, many sensing and estimation approaches suffer from the curse of dimensionality, and their performance may degrade as uncertainties from disparate sources propagate through the network.

关键词： Special issues and sections Robot sensing systems Wireless sensor networks Mathematical model Collision avoidance Optimization Fault tolerant systems

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Navigation control of a mobile robotic system in semi-structured and dynamic environment for controlled dose delivery of pesticides

Navigation control of a mobile robotic system in semi-struct...

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1st IEEE International Conference on Trends in Automation, Communications and Computing Technologies (I-TACT)

作者： Patel, Manoj Kumar Kathuria, Chiranjiv Singh Singla, Aneesh Sahoo, Hemant Kumar Nayak, Manoj Kumar Ghanshyam, C. AcSIR CSIO Chandigarh 160030 India CSIR CSIO Chandigarh 160030 India Thapar Univ Patiala 147004 Punjab India

ISBN: (纸本)9781467366670

A major portion of the autonomous mobile robotics research has been in the development of reliable means for navigation and control of autonomous systems in a complex, irregular, unstructured and dynamic environment for farm duties such as seeding, pruning, pesticide spraying, reaping etc. Several practices have been established by numerous researchers in the motion planning of mobile robots. This paper presents an ultrasonic multi-sensor fusion based technique for navigation and control of the autonomous mobile robotic system for collision avoidance, target detection and canopy mapping. The autonomous system is a combination of real time monitoring and navigation, detection of the target and canopy mapping which delivers controlled dose of pesticide in a targeted manner according to the information gathered from the sensory attributes of precision farming data set through online and real time monitoring of the objects. Real time monitoring and navigation control, complex algorithm, data acquisition and storage, calculation and processing are designed with motors and sensors interfaced with microcontroller. Test results showed that the ultrasonic sensor fusion technique was capable of performing satisfactory guidance in dynamic environment and desired paths. The sensor fusion navigation was capable of compensating large error and resulting in satisfactory agricultural vehicle guidance.

关键词： Navigation control Multi-sensor fusion Canopy Mapping Precision Farming Data Set (PFDS) Precision Agriculture Data Set (PADS)

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Adaptive velocity control for UAV boat landing: A neural network and particle swarm optimization approach

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JOURNAL OF INTELLIGENT & robotic systems 2024年第1期111卷 1-18页

作者： Wu, Li-Fan Wang, Zihan Rastgaar, Mo Mahmoudian, Nina Purdue Univ W Lafayette IN 47906 USA

Achieving precise landing of Unmanned Aerial Vehicles (UAVs) onto moving platforms, such as autonomous Surface Vehicles (ASVs), is challenging, particularly in GPS-denied environments with dynamic disturbances. Conventional methods often rely on high-level waypoint navigation, extensive manual tuning, and expensive sensors. In this work, we propose an adaptive Proportional-Integral-Derivative (PID) controller optimization using a Neural Network-Particle Swarm Optimization (NN-PSO) algorithm. The algorithm dynamically tunes the PID controller, significantly reducing manual tuning effort, while relying solely on a low-cost camera and altitude sensor. The NN-PSO algorithm allows the UAV to land with an average error of 5 cm on static platforms and 10 cm on moving boats, based on multiple test flights. Our method also increases the maximum landing speed to 80.9% of the UAV's top flight speed, a considerable improvement over existing systems. Our approach not only optimizes landing precision but also introduces techniques for ensuring soft landings, reducing oscillations, and preventing target misses. These enhancements make the method robust across varying flight altitudes and ASV speeds. Furthermore, this approach is applicable to a variety of GPS-denied scenarios, including rescue missions, package deliveries, and workspace inspections, without requiring costly equipment or extensive parameter tuning. Field experiments confirm the precision and stability of the proposed system, validating its performance in real-world conditions. [Video]

关键词： Adaptive control autonomous systems Visual servo control sensor fusion Visual servoing Unmanned aerial vehicles

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An expectation-based framework of object schemas and port-based agents for disparate feedback assimilation

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autonomous ROBOTS 1999年第2期7卷 159-173页

作者： Nelson, BJ Khosla, PK Univ Minnesota Dept Mech Engn Minneapolis MN 55455 USA Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

robotic manipulation systems that operate in unstructured environments must be responsive to feedback from sensors that are disparate in both location and modality. This paper describes a distributed framework for assimilating the disparate feedback provided by force and vision sensors, including active vision sensors, for robotic manipulation systems. The main components of the expectation-based framework include object schemas and port-based agents. Object schemas represent the manipulation task internally in terms of geometric models with attached sensor mappings. Object schemas are dynamically updated by sensor feedback, and thus provide an ability to perform three dimensional spatial reasoning during task execution. Because object schemas possess knowledge of sensor mappings, they are able to both select appropriate sensors and guide active sensors based on task characteristics. Port-based agents are the executors of reference inputs provided by object schemas and are defined in terms of encapsulated control strategies. Experimental results demonstrate the capabilities of the framework in two ways: the performance of manipulation tasks with active camera-lens systems, and the assimilation of force and vision sensory feedback.

关键词： force control visual serving sensor fusion robotic manipulation active sensors

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