Reviewerly/exHarmony huggingface dataset:
Safety-Critical Path Planning of Autonomous Surface Vehicles Based on Rapidly-Exploring Random Tree Algorithm and High Order Control Barrier Functions

This paper presents a maritime path planning algorithm for autonomous surface vehicles (ASVs). A improved RRT algorithm for generating sample nodes is proposed. The HOCBF-RRT algorithm leverages high order control barrier functions (HOCBFs) to take into account the dynamics of the ASV and produce a path that prioritizes safety. In addition, to complete the path optimization, combined with control Lyapunov functions (CLFs), the expansion to the target point is accelerated and the planning time is reduced. In simulation experiments, it is observed that the HOCBF-RRT algorithm and HOCBF-CLF-RRT algorithm offer improvements over traditional RRT algorithms. Specifically, the HOCBF-RRT algorithm is found to enhance the smoothness of paths, while the HOCBF-CLF-RRT algorithm effectively optimizes the expansion of the random tree towards the target point.
Monte-Carlo Method-Based Collaborative Detection of Underwater Targets by Multiple Air-Water Amphibious Robots

Aiming at the problem of emergency detection and search in national marine related professional fields, a Monte-Carlo method based multiple Air-Water Amphibious Robots collaborative detection method for underwater targets is studied in this paper. Firstly, a multiple Air-Water Amphibious Robots cooperative detection formation was established, and the overlapping shadow areas of different arrays were explored, and conditions were set for the search area and the Air-Water Amphibious Robots cooperative detection formation. After simulation research, parameters of each multiple Air-Water Amphibious Robots were selected and assigned, and then a set of simulation process was designed to solve the problem. Finally, the proposed method is used to simulate the relationship between detection probability and search time for 8 formations. Through the analysis of simulation results, it is concluded that the corresponding formation can be adopted to detect and track different tasks and different stages of tasks to achieve the best detection effect.
Efficient 2D Simulators for Deep-Reinforcement-Learning-based Training of Navigation Approaches

In recent years, Deep Reinforcement Learning (DRL) has emerged as a competitive approach for mobile robot navigation. However, training DRL agents often comes at the cost of difficult and tedious training procedures in which powerful hardware is required to conduct oftentimes long training runs. Especially, for complex environments, this proves to be a major bottleneck for widespread adoption of DRL approaches into industries. In this paper we integrate an efficient 2D simulator into the Arena-Rosnav framework of our previous work as an alternative simulation platform to train and develop DRL agents. Therefore, we utilized the provided API to integrate necessary components into the ecosystem of Arena-Rosnav. We evaluated our simulator by training a DRL agent within that platform and compared the training and navigational performance against the baseline 2D simulator Flatland, which is the default simulating platform of Arena-Rosnav. Results demonstrate that using our Arena2D simulator results in substantially faster training times and in some scenarios better agents. This proves to be an important step towards resource-efficient DRL training, which accelerates training times and improve the development cycle of DRL agents for navigation tasks. We made our simulator openly available at https://github.com/Arena-Rosnav/arena2d.
THE-Planner: Topological and Hierarchical Exploration Path Planner for Fast 3D Mapping of Outdoor Structures with UAVs

In recent years, exploration methods have been extensively researched for the purpose of mapping unknown environments with autonomous robots, aiming to replace humans. However, existing exploration methods primarily focus on indoor environments, which may not be sufficient for fast and efficient mapping of outdoor environments. In this paper, we propose a novel exploration path planner, THE-Planner, which utilizes a topological and hierarchical approach to map unknown outdoor environments quickly and efficiently. THE-Planner builds a topological map with local and global graphs. It generates global graph nodes with the robot's positions over time. At every node, it checks the visibility of points to calculate and update frontiers. With the newly calculated frontiers, a local graph is generated, and graph-based local and global path planning is used to explore the environments. The effectiveness of the proposed method is validated in the simulations by comparing with the state-of-the-art methods.
Calculating Class-Wise Weighted Feature Norm for Detecting Out-of-Distribution Samples

This paper focuses on the field of out-of-distribution (OOD) detection in neural networks. OOD detection is crucial in robotic systems as it helps identify unexpected situations and respond accordingly, ensuring safety and reliability. The FeatureNorm framework is a post-hoc OOD detection method, which does not modify the training stage, based on the norm of feature maps obtained from a suitable block selected using the NormRatio. However, FeatureNorm has limitations in detecting high-complexity OOD images. In this study, we propose a class-wise weighted feature norm (CWFN) method to improve OOD detection performance of FeatureNorm by selectively choosing important filters without the need for OOD samples. The proposed method is based on the idea that convolutional layers contain important filters for object classification and results in improved OOD detection performance. The experiment on the CIFAR10 OOD detection benchmark confirms the effectiveness of the proposed method. Finally, the experiment on the hand gesture recognition (HGR) system demonstrate that the proposed method can be used for robotic system that utilize HGR for interface.
A Dual-Cycled Cross-View Transformer Network for Unified Road Layout Estimation and 3D Object Detection in the Bird's-Eye-View

The bird's-eye-view (BEV) representation allows robust learning of multiple tasks for autonomous driving including road layout estimation and 3D object detection. However, contemporary methods for unified road layout estimation and 3D object detection rarely handle the class imbalance of the training dataset and multi-class learning to reduce the total number of networks required. To overcome these limitations, we propose a unified model for road layout estimation and 3D object detection inspired by the transformer architecture and the CycleGAN learning framework. The proposed model deals with the performance degradation due to the class imbalance of the dataset utilizing the focal loss and the proposed dual cycle loss. Moreover, we set up extensive learning scenarios to study the effect of multi-class learning for road layout estimation in various situations. To verify the effectiveness of the proposed model and the learning scheme, we conduct a thorough ablation study and a comparative study. The experiment results attest the effectiveness of our model; we achieve state-of-the-art performance in both the road layout estimation and 3D object detection tasks.
Development of Low-Power Intelligent Video Surveillance System Using Embedded Computer

There has been a surge in criminal activity due to rapid urbanization, which poses a threat to community safety. Centralized video surveillance systems using computer vision have been introduced to monitor and respond to such activities. However, their implementation is limited by the substantial costs of both initial installation and maintenance. To tackle this issue, low-cost distributed embedded video surveillance systems have been proposed, utilizing low-power processors to minimize costs but with limited computational capabilities. To overcome this limitation, this study introduces an intelligent video surveillance algorithm that uses effective and simple algorithms, such as background subtraction and Frame-Skipping, to detect loitering, intrusion, and arson in embedded computing environments. As a result, a low-power distributed video surveillance system that can sustain 29 frames per second within the embedded system environment has been developed and experimentally verified.
What if a Vacuum Robot has an Arm?

In this paper, we present a modular add-on system that can be seamlessly integrated with an off-the-shelf vacuum robot to expand its capabilities beyond vacuuming. Employing the add-on system enables seamless integration of a manipulator, which can be effortlessly attached and detached with minimal alterations to the vacuum robot, without compromising its original functionality. Furthermore, the add-on system allows for the incorporation of a potent manipulator capable of performing household tasks, demonstrating its potential for practical applications. The system augments the vacuum robot's capability by adding a 6-DOF manipulator, an RGB-D camera for superior perception, and a LiDAR sensor for enhanced navigation. This allows users to upgrade their existing vacuum robots, enabling them to handle a more extensive array of household tasks. We showcase the system's ability to navigate to specific locations on a map, open doors, grasp objects, and place them in designated locations through a basic demonstration, highlighting its improved perception and manipulation abilities. Our approach presents a practical solution for homeowners seeking an affordable and versatile household cleaning robot with extended functionality.
A simple baseline for domain generalization of action recognition and a realistic out-of-domain scenario

In this study, we tackle the Domain Generalization (DG) challenge in the field of video action recognition. Previous works on DG for action recognition mostly focus on the Single Domain Generalization (SDG) problem, which is difficult to achieve due to domain biases present in datasets. Thus, we aim to address this challenge by re-defining DG for video action recognition. Another limitation of existing research is the lack of realistic out-of-domain scenarios. To overcome this, we introduce a new benchmark for DG in video action recognition named ENT dataset that contains realistic domain shifts. Additionally, we propose a straightforward baseline for DG in the video domain that helps improve the performance of action recognition models in unseen domains. Our proposed approach adapts the image-based methods to the video domain, leading to improved DG performance.
Design and Evaluation of a Bioinspired Tendon-Driven 3D-Printed Robotic Eye with Active Vision Capabilities

The field of robotics has seen significant advancements in recent years, particularly in the development of humanoid robots. One area of research that has yet to be fully explored is the design of robotic eyes. In this paper, we propose a computer-aided 3D design scheme for a robotic eye that incorporates realistic appearance, natural movements, and efficient actuation. The proposed design utilizes a tendon-driven actuation mechanism, which offers a broad range of motion capabilities. The use of the minimum number of servos for actuation, one for each agonist-antagonist pair of muscles, makes the proposed design highly efficient. Compared to existing ones in the same class, our designed robotic eye comprises aesthetic and realistic features. We evaluate the robot's performance using a vision-based controller, which demonstrates the effectiveness of the proposed design in achieving natural movement, and efficient actuation. The experiment code, toolbox, and printable 3D sketches of our design have been open-sourced.
Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigation

In reality, it is often more efficient to ask for help than to search the entire space to find an object with an unknown location. We present a learning framework that enables an agent to actively ask for help in such embodied visual navigation tasks, where the feedback informs the agent of where the goal is in its view. To emulate the real-world scenario that a teacher may not always be present, we propose a training curriculum where feedback is not always available. We formulate an uncertainty measure of where the goal is and use empirical results to show that through this approach, the agent learns to ask for help effectively while remaining robust when feedback is not available.
Development of a Sphere Motion Measurement System and Evaluation of the Roller Arrangement Requirement

To conserve human resources and labor, there is a demand for robots to transport objects. Compared to previous object transport methods, sphere transport by a mobile robot is efficient for transporting objects without loading them and while on the ground. However, previous research on sphere transport has not clarified the methodology for designing and controlling sphere transport based on kinematic and dynamic properties. Therefore, this study attempts to develop a rollersphere motion measurement method for focusing on the roller angular velocity and to evaluate the effectiveness of the roller arrangement requirement. In particular, this study evaluates the “roller arrangement requirement,” which is necessary for roller arrangement (position/orientation) considering kinematic characteristics in sphere transport. Experiments have shown the effectiveness of the roller arrangement requirement with a novel roller-sphere motion measurement method.
D-TSM: Discriminative Temporal Shift Module for Action Recognition

Action recognition is one of the representative perception tasks for robot application, but it still remains challenging due to complex temporal dynamics. Although temporal shift module (TSM) has been considered to be one of the best 2D CNN based architecture for temporal modeling, its inherent structural simplicity limits performance and has room for improvement. To mitigate this issue while following TSM's philosophy, this paper presents a variant of TSM, termed as Discriminative TSM (D-TSM), with a focus on capturing dis-criminative features for motion pattern. Going further from the naive shift operation in TSM, our D-TSM explicitly transforms shifted features by applying element-wise subtraction. This simple approach is effective to create discriminative features between adjacent frames with a small extra computational cost and zero parameter. The experiments on Something-Something and Jester datasets demonstrate that our D-TSM outperforms TSM and achieves competitive performance with low FLOPs against other methods.
Navigating Off-Roads: Using Deep Neural Networks for Rock Detection in Off-Road Autonomous Driving with Unimog

An off-road environment is characterized by multiple objects, often ambiguous in nature. The challenges associated with autonomous navigation in off-road environments are numerous, and detecting rocks, a critical element, can be particularly difficult due to their variability. This paper provides insights into the challenges associated with rock detection in Earth's off-road environments and the capabilities of Deep Neural Networks (DNNs) (with a considerably smaller dataset) to perform this task. Three state-of-the-art instance segmentation networks are trained on the rock dataset and compared. The best network is then implemented (in Unimog's robotic framework) and 3D bounding boxes (in laser point cloud) are generated. The generated boxes can be used to create a 3D rock dataset capturing their characteristics in more detail. This pipeline serves as a key component of the environment perception package, ensuring safe navigation of the Unimog U5023 in off-road environments. The networks are further tested against different domains and qualitative and quantitative results are provided through real-world driving tests.
Clustered Lidar-Based Potential Field Path Planning

Mobile service robots operating in complex indoor environments require collision-free path planning to reach their goals. The artificial potential field (APF) path planning method is a commonly used technique that considers the robot as a point in the potential field and is easy to implement using lidar sensor data. Since the APF method has a significant disadvantage of local minima, many APF studies have focused on solving it. In this paper, however, we take a different approach to improve the performance of the APF. We propose a Clustered LiDAR-based Potential Field path planning method that introduces 2D lidar clustering to distinguish obstacles in partially observable environments. The proposed method is implemented in ROS and demonstrated using a simulated mobile manipulator in Gazebo.
Conflict-Based Search with Partitioned Groups of Agents for Real-World Scenarios

Multi-agent pathfinding (MAPF) is the problem of planning optimal paths for a group of agents without conflict. Because of the high computational complexity of MAPF, several powerful techniques have been developed to reduce the runtime under assumptions, such as the ideal kinematics of point-like agents and a discretized time domain. However, these assumptions restrict the applicability of the MAPF algorithms to real-world scenarios. Continuous-time conflict-based search (CCBS), a variant of CBS, was recently proposed to generalize MAPF problems to continuous-time and non-grid domains. In this study, we propose CCBS with partitioned groups of agents (CCBS-PGA), a hybrid approach that uses CCBS as the main MAPF solver and adopts the idea of prioritized planning (PP) for enhanced scalability. CCBS-PGA runs CCBS for each group of the partitioned agents and assigns unique priorities to the individual group. We also introduce two features that reflect realistic robot kinematics; rotational velocities of robots and safety distances. This approach does not rely on the simplified assumptions while alleviating the computational complexity of MAPF problems involving numerous agents. We demonstrate its effectiveness in various environments using differential-wheeled robots.
Solving Challenging Control Problems via Learning-based Motion Planning and Imitation

We present a deep reinforcement learning (deep RL) algorithm that consists of learning-based motion planning and imitation to tackle challenging control problems. Deep RL has been an effective tool for solving many high-dimensional continuous control problems, but it cannot effectively solve challenging problems with certain properties, such as sparse reward functions or sensitive dynamics. In this work, we propose an approach that decomposes the given problem into two deep RL stages: motion planning and motion imitation. The motion planning stage seeks to compute a feasible motion plan by leveraging the powerful planning capability of deep RL. Subsequently, the motion imitation stage learns a control policy that can imitate the given motion plan with realistic sensors and actuation models. This new formulation requires only a nominal added cost to the user because both stages require minimal changes to the original problem. We demonstrate that our approach can solve challenging control problems, rocket navigation, and quadrupedal locomotion, which cannot be solved by the monolithic deep RL formulation or the version with Probabilistic Roadmap.
Review on Blockchain Based Food Supplychain

The review will examine the application of blockchain technology in supply chain management and compare it to conventional practices. Centralized supply chains have issues with data manipulation and traceability, while blockchain provides a decentralized, secure, transparent, and auditable solution through its distributed ledger technology. The integration of IoT with blockchain-based traceability helps monitor product state and improve customer trust. In the review, we are going to study application of block chain technology in SCM and compare with present supply chain management.
A Machine Vision-Based Character Recognition System for Suspension Insulator Iron Caps

Character recognition on the surfaces of iron caps is pivotal in the automated production of suspension insulators. This paper proposes a machine vision-based character recognition system for the iron caps of suspension insulators in order to solve the problem of recognizing curved characters with three-dimensional (3D) structures and severe surface reflections. In this system, firstly, 3D character extraction system (3D-CES) converts 3D characters on curved surfaces to flat two-dimensional (2D) characters. Then, the 2D characters are segmented into individual characters using an improved Drop-Fall (IDF) segmentation algorithm. Finally, the individual characters are recognized using a pruned Visual Geometry Group 16 (VGG-16) network. This paper tested the proposed system on real iron cap data, and a recognition accuracy of 98.3% were achieved. The performance of the proposed system is higher than existing deep-learning-based text character recognition methods by 2.9%.
Certificateless Provable Data Possession Scheme With Provable Security in the Standard Model Suitable for Cloud Storage

Provable data possession (PDP) solves the problem of determining whether the data stored in the cloud is maintained in its entirety. Certificateless cryptography simultaneously tackles two challenges: certificate management and key escrow. There are still three deficiencies in the current known certificateless provable data possession (CL-PDP) schemes. First, security proofs are typically conducted in the random oracle model (ROM). Second, some schemes are incapable of preventing third-party auditor (TPA) from recovering user data blocks. Third, most schemes require hash-to-point operations, which result in lower computational efficiency. In this article, we demonstrated two kinds of attacks targeting the scheme Ji et al. 2020. Subsequently, we put forward a new CL-PDP scheme and showed security proofs in the standard model (SM). Finally, we conducted performance analysis on ten CL-PDP schemes. Our scheme outperforms others in terms of efficiency, as it minimizes computational overhead by requiring only three pairing operations and eliminating the need for hash-to-point operations.
Robot–Camera Calibration in Tightly Constrained Environment Using Interactive Perception

Manipulation in tight environment is challenging but increasingly common in vision-guided robotic applications. The significantly reduced amount of available feedback (limited visual cues, field of view, robot motion space, etc.) hinders solving the hand-eye relationship accurately. In this article, we propose a new generic approach for online robot–camera calibration that could deal with the least feedback input available in tight environment: an arbitrarily restricted motion space and a single feature point with unknown position for the robot end-effector. We introduce the interactive perception to generate prescribed but tunable robot motions to reveal high-dimensional sensory feedback, which is not obtainable from static images. We then define the interactive feature plane (IFP), whose spatial property corresponds to the robot-actuating trajectories. A depth-free adaptive controller is proposed based on image feedback, where the converged orientation of IFP directly harvests the data for solving the hand–eye relationship. Our algorithm requires neither external calibration sensors/objects nor large-scale data acquisition process. Simulations demonstrate the validity of our method to accurately calibrate different types of robot under various system set-ups. In experiments, we show good results of our algorithm in terms of accuracy and consistency under tight motion space compared to existing approaches using external objects and/or optimization.