arXiv subjects

Ziqi Zhao

Publications and source records attributed to Ziqi Zhao.

50 records · Page 3Linked to original sources

arXiv2023

Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments.

cs.RO↗

arXiv2023

FIS-ONE: Floor Identification System with One Label for Crowdsourced RF Signals

Floor labels of crowdsourced RF signals are crucial for many smart-city applications, such as multi-floor indoor localization, geofencing, and robot surveillance. To build a prediction model to identify the floor number of a new RF signal upon its measurement, conventional approaches using the crowdsourced RF signals assume that at least few labeled signal samples are available on each floor. In this work, we push the envelope further and demonstrate that it is technically feasible to enable such floor identification with only one floor-labeled signal sample on the bottom floor while having the rest of signal samples unlabeled. We propose FIS-ONE, a novel floor identification system with only one labeled sample. FIS-ONE consists of two steps, namely signal clustering and cluster indexing. We first build a bipartite graph to model the RF signal samples and obtain a latent representation of each node (each signal sample) using our attention-based graph neural network model so that the RF signal samples can be clustered more accurately. Then, we tackle the problem of indexing the clusters with proper floor labels, by leveraging the observation that signals from an access point can be detected on different floors, i.e., signal spillover. Specifically, we formulate a cluster indexing problem as a combinatorial optimization problem and show that it is equivalent to solving a traveling salesman problem, whose (near-)optimal solution can be found efficiently. We have implemented FIS-ONE and validated its effectiveness on the Microsoft dataset and in three large shopping malls. Our results show that FIS-ONE outperforms other baseline algorithms significantly, with up to 23% improvement in adjusted rand index and 25% improvement in normalized mutual information using only one floor-labeled signal sample.

cs.NI↗

arXiv2023

Closed-Loop Magnetic Manipulation for Robotic Transesophageal Echocardiography

This paper presents a closed-loop magnetic manipulation framework for robotic transesophageal echocardiography (TEE) acquisitions. Different from previous work on intracorporeal robotic ultrasound acquisitions that focus on continuum robot control, we first investigate the use of magnetic control methods for more direct, intuitive, and accurate manipulation of the distal tip of the probe. We modify a standard TEE probe by attaching a permanent magnet and an inertial measurement unit sensor to the probe tip and replacing the flexible gastroscope with a soft tether containing only wires for transmitting ultrasound signals, and show that 6-DOF localization and 5-DOF closed-loop control of the probe can be achieved with an external permanent magnet based on the fusion of internal inertial measurement and external magnetic field sensing data. The proposed method does not require complex structures or motions of the actuator and the probe compared with existing magnetic manipulation methods. We have conducted extensive experiments to validate the effectiveness of the framework in terms of localization accuracy, update rate, workspace size, and tracking accuracy. In addition, our results obtained on a realistic cardiac tissue-mimicking phantom show that the proposed framework is applicable in real conditions and can generally meet the requirements for tele-operated TEE acquisitions.

cs.RO↗

arXiv2023

Interpretation of Time-Series Deep Models: A Survey

Deep learning models developed for time-series associated tasks have become more widely researched nowadays. However, due to the unintuitive nature of time-series data, the interpretability problem -- where we understand what is under the hood of these models -- becomes crucial. The advancement of similar studies in computer vision has given rise to many post-hoc methods, which can also shed light on how to explain time-series models. In this paper, we present a wide range of post-hoc interpretation methods for time-series models based on backpropagation, perturbation, and approximation. We also want to bring focus onto inherently interpretable models, a novel category of interpretation where human-understandable information is designed within the models. Furthermore, we introduce some common evaluation metrics used for the explanations, and propose several directions of future researches on the time-series interpretability problem. As a highlight, our work summarizes not only the well-established interpretation methods, but also a handful of fairly recent and under-developed techniques, which we hope to capture their essence and spark future endeavours to innovate and improvise.

cs.LG↗

arXiv2022

Coupled Modeling and Fusion Control for a Multi-modal Deformable Land-air Robot

A deformable land-air robot is introduced with excellent driving and flying capabilities, offering a smooth switching mechanism between the two modes. An elaborate coupled dynamics model is established for the robot, including rotors, chassis, suspension, and the deformable structure. In addition, a model-based controller is designed for landing and mode switching in various unstructured conditions, such as slopes and curved surface. And considering locomotion and complex near-ground situations to achieve cooperation between the two fused modalities. This system was simulated in ADAMS/Simulink and a tested with hardware-in-the-loop system was constructed for testing in various slopes. With a designed controller, the results showed the robot is capable of fast and smooth land-air switching, with a 24.6 % faster landing on slopes. The controller can also reduce landing offset and impact force more effectively than the normal control method at 32.7 % and 34.3 %, respectively.

cs.RO↗

arXiv2022

Robust Binary Models by Pruning Randomly-initialized Networks

Robustness to adversarial attacks was shown to require a larger model capacity, and thus a larger memory footprint. In this paper, we introduce an approach to obtain robust yet compact models by pruning randomly-initialized binary networks. Unlike adversarial training, which learns the model parameters, we initialize the model parameters as either +1 or -1, keep them fixed, and find a subnetwork structure that is robust to attacks. Our method confirms the Strong Lottery Ticket Hypothesis in the presence of adversarial attacks, and extends this to binary networks. Furthermore, it yields more compact networks with competitive performance than existing works by 1) adaptively pruning different network layers; 2) exploiting an effective binary initialization scheme; 3) incorporating a last batch normalization layer to improve training stability. Our experiments demonstrate that our approach not only always outperforms the state-of-the-art robust binary networks, but also can achieve accuracy better than full-precision ones on some datasets. Finally, we show the structured patterns of our pruned binary networks.

cs.LG↗

arXiv2022

GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals

We study the problem of floor identification for radiofrequency (RF) signal samples obtained in a crowdsourced manner, where the signal samples are highly heterogeneous and most samples lack their floor labels. We propose GRAFICS, a graph embedding-based floor identification system. GRAFICS first builds a highly versatile bipartite graph model, having APs on one side and signal samples on the other. GRAFICS then learns the low-dimensional embeddings of signal samples via a novel graph embedding algorithm named E-LINE. GRAFICS finally clusters the node embeddings along with the embeddings of a few labeled samples through a proximity-based hierarchical clustering, which eases the floor identification of every new sample. We validate the effectiveness of GRAFICS based on two large-scale datasets that contain RF signal records from 204 buildings in Hangzhou, China, and five buildings in Hong Kong. Our experiment results show that GRAFICS achieves highly accurate prediction performance with only a few labeled samples (96% in both micro- and macro-F scores) and significantly outperforms several state-of-the-art algorithms (by about 45% improvement in micro-F score and 53% in macro-F score).

cs.NI↗

arXiv2022

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation

Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users' multi-faceted preferences. As dependencies are explicitly exhibited by the multiple types of behaviors, effectively modeling complex behavior dependencies is crucial for multi-behavior prediction. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. However, different behaviors may reflect different aspects of user preference, which means that some irrelevant interactions may play as noises to the target behavior to be predicted. To address the aforementioned limitations, we introduce multi-interest learning to the multi-behavior recommendation. More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors. CKML introduces two advanced modules, namely Coarse-grained Interest Extracting (CIE) and Fine-grained Behavioral Correlation (FBC), which work jointly to capture fine-grained behavioral dependencies. CIE uses knowledge-aware information to extract initial representations of each interest. FBC incorporates a dynamic routing scheme to further assign each behavior among interests. Additionally, we use the self-attention mechanism to correlate different behavioral information at the interest level. Empirical results on three real-world datasets verify the effectiveness and efficiency of our model in exploiting multi-behavior data. Further experiments demonstrate the effectiveness of each module and the robustness and superiority of the shared and specific modelling paradigm for multi-behavior data.

cs.IR↗

arXiv2022

Robotic Autonomous Trolley Collection with Progressive Perception and Nonlinear Model Predictive Control

Autonomous mobile manipulation robots that can collect trolleys are widely used to liberate human resources and fight epidemics. Most prior robotic trolley collection solutions only detect trolleys with 2D poses or are merely based on specific marks and lack the formal design of planning algorithms. In this paper, we present a novel mobile manipulation system with applications in luggage trolley collection. The proposed system integrates a compact hardware design and a progressive perception and planning framework, enabling the system to efficiently and robustly collect trolleys in dynamic and complex environments. For the perception, we first develop a 3D trolley detection method that combines object detection and keypoint estimation. Then, a docking process in a short distance is achieved with an accurate point cloud plane detection method and a novel manipulator design. On the planning side, we formulate the robot's motion planning under a nonlinear model predictive control framework with control barrier functions to improve obstacle avoidance capabilities while maintaining the target in the sensors' field of view at close distances. We demonstrate our design and framework by deploying the system on actual trolley collection tasks, and their effectiveness and robustness are experimentally validated.

cs.RO↗

arXiv2021

Trajectory Following Strategies for Wireless Capsule Endoscopy under Reciprocally Rotating Magnetic Actuation in a Tubular Environment

Currently used wireless capsule endoscopy (WCE) is limited in terms of inspection time and flexibility since the capsule is passively moved by peristalsis and cannot be accurately positioned. Different methods have been proposed to facilitate active locomotion of WCE based on simultaneous magnetic actuation and localization technologies. In this work, we investigate the trajectory following problem of a robotic capsule under rotating magnetic actuation in a tubular environment, in order to realize safe, efficient and accurate inspection of the intestine at given points using wireless capsule endoscopes. Specifically, four trajectory following strategies are developed based on the PD controller, adaptive controller, model predictive controller and robust multi-stage model predictive controller. Moreover, our method takes into account the uncertainty in the intestinal environment by modeling the intestinal peristalsis and friction during the controller design. We validate our methods in simulation as well as in real-world experiments in various tubular environments, including plastic phantoms with different shapes and an ex-vivo pig colon. The results show that our approach can effectively actuate a reciprocally rotating capsule to follow a desired trajectory in complex tubular environments, thereby having the potential to enable accurate and repeatable inspection of the intestine for high-quality diagnosis.

cs.RO↗

arXiv2021

Autonomous Magnetic Navigation Framework for Active Wireless Capsule Endoscopy Inspired by Conventional Colonoscopy Procedures

In recent years, simultaneous magnetic actuation and localization (SMAL) for active wireless capsule endoscopy (WCE) has been intensively studied to improve the efficiency and accuracy of the examination. In this paper, we propose an autonomous magnetic navigation framework for active WCE that mimics the "insertion" and "withdrawal" procedures performed by an expert physician in conventional colonoscopy, thereby enabling efficient and accurate navigation of a robotic capsule endoscope in the intestine with minimal user effort. First, the capsule is automatically propelled through the unknown intestinal environment and generate a viable path to represent the environment. Then, the capsule is autonomously navigated towards any point selected on the intestinal trajectory to allow accurate and repeated inspections of suspicious lesions. Moreover, we implement the navigation framework on a robotic system incorporated with advanced SMAL algorithms, and validate it in the navigation in various tubular environments using phantoms and an ex-vivo pig colon. Our results demonstrate that the proposed autonomous navigation framework can effectively navigate the capsule in unknown, complex tubular environments with a satisfactory accuracy, repeatability and efficiency compared with manual operation.

cs.RO↗

arXiv2021

On Reciprocally Rotating Magnetic Actuation of a Robotic Capsule in Unknown Tubular Environments

Active wireless capsule endoscopy (WCE) based on simultaneous magnetic actuation and localization (SMAL) techniques holds great promise for improving diagnostic accuracy, reducing examination time and relieving operator burden. To date, the rotating magnetic actuation methods have been constrained to use a continuously rotating permanent magnet. In this paper, we first propose the reciprocally rotating magnetic actuation (RRMA) approach for active WCE to enhance patient safety. We first show how to generate a desired reciprocally rotating magnetic field for capsule actuation, and provide a theoretical analysis of the potential risk of causing volvulus due to the capsule motion. Then, an RRMA-based SMAL workflow is presented to automatically propel a capsule in an unknown tubular environment. We validate the effectiveness of our method in real-world experiments to automatically propel a robotic capsule in an ex-vivo pig colon. The experiment results show that our approach can achieve efficient and robust propulsion of the capsule with an average moving speed of $2.48 mm/s$ in the pig colon, and demonstrate the potential of using RRMA to enhance patient safety, reduce the inspection time, and improve the clinical acceptance of this technology.

cs.RO↗

arXiv2021

Adaptive Simultaneous Magnetic Actuation and Localization for WCE in a Tubular Environment

Simultaneous Magnetic Actuation and Localization (SMAL) is a promising technology for active wireless capsule endoscopy (WCE). In this paper, an adaptive SMAL system is presented to efficiently propel and precisely locate a capsule in a tubular environment with complex shapes. In order to track the capsule with high localization accuracy and update frequency in a large workspace, we propose a mechanism that can automatically activate a sub-array of sensors with the optimal layout during the capsule movement. The improved multiple objects tracking (IMOT) method is simplified and adapted to our system to estimate the 6-D pose of the capsule in real time. Also, we study the locomotion of a magnetically actuated capsule in a tubular environment, and formulate a method to adaptively adjust the pose of the actuator to improve the propulsion efficiency. Our presented methods are applicable to other permanent magnet-based SMAL systems, and help to improve the actuation efficiency of active WCE. We verify the effectiveness of our proposed system in extensive experiments on phantoms and ex-vivo animal organs. The results demonstrate that our system can achieve convincing performance compared with the state-of-the-art ones in terms of actuation efficiency, workspace size, robustness, localization accuracy and update frequency.

cs.RO↗

arXiv2020

Joint Inference on Truth/Rumor and Their Sources in Social Networks

In the contemporary era of information explosion, we are often faced with the mixture of massive \emph{truth} (true information) and \emph{rumor} (false information) flooded over social networks. Under such circumstances, it is very essential to infer whether each claim (e.g., news, messages) is a truth or a rumor, and identify their \emph{sources}, i.e., the users who initially spread those claims. While most prior arts have been dedicated to the two tasks respectively, this paper aims to offer the joint inference on truth/rumor and their sources. Our insight is that a joint inference can enhance the mutual performance on both sides. To this end, we propose a framework named SourceCR, which alternates between two modules, i.e., \emph{credibility-reliability training} for truth/rumor inference and \emph{division-querying} for source detection, in an iterative manner. To elaborate, the former module performs a simultaneous estimation of claim credibility and user reliability by virtue of an Expectation Maximization algorithm, which takes the source reliability outputted from the latter module as the initial input. Meanwhile, the latter module divides the network into two different subnetworks labeled via the claim credibility, and in each subnetwork launches source detection by applying querying of theoretical budget guarantee to the users selected via the estimated reliability from the former module. The proposed SourceCR is provably convergent, and algorithmic implementable with reasonable computational complexity. We empirically validate the effectiveness of the proposed framework in both synthetic and real datasets, where the joint inference leads to an up to 35\% accuracy of credibility gain and 29\% source detection rate gain compared with the separate counterparts.

cs.SI↗