Robotics Community Research Articles

Structural Design and Kinematic Analysis of Cable-Driven Soft Robot

Continuous robots have attracted more and more attention from the robotics community due to their high degree of flexibility and pliability, and have shown great potential for application in a variety of fields. With the continuous progress of material science, control technology, and artificial intelligence, the performance and application range of soft robotics have been further expanded, in which the cable drive has the advantages of large workspace, high flexibility, etc. The cable-driven soft robotic arm serves as an ultra-redundant robot that can operate in cramped and confined environments. In this paper, a cable-driven soft robot based on soft continuums and a cross gimbal is presented. The kinematics of the cable-driven soft robot is modeled and the mapping relations of the kinematics are solved by the D–H method and piecewise constant curvature, and the relations between the cable length, joint angle, and pose are further derived. Finally, the motion space of the cable-driven soft robot in the three-dimensional coordinate system is obtained by MATLAB2021b, and the single-segment soft body is simulated and analyzed using ADAMS to compare the theoretical data with the actual data and verify the reliability of this structure and method.

AutoMine: A Multimodal Dataset for Robot Navigation in Open‐Pit Mines

ABSTRACTIn the past decade, autonomous driving has witnessed significant advancements, largely attributable to the evolution of precise algorithms and efficient computing platforms. Nevertheless, the open‐pit mine, a typical scenario within closed‐field environments, has garnered limited attention in autonomous driving, primarily owing to the scarcity of data and experimental benchmarks. This work presents original data collected from five platforms, comprising one passenger vehicle, three wide‐body trucks, and one mining truck, across eight different mining sites. We provide a comprehensive elucidation of platform types, sensors, calibration methodologies, synchronization techniques, data collection approaches, and a thorough analysis of the data characteristics. In addition, we offer a detailed benchmark comparison of short and long odometry and navigation performance across multiple vehicles in open‐pit mines. With comprehensive data characteristics, experimental performance evaluations, and thorough analysis, we believe that this work establishes a robust research foundation for navigation and fusion methods in open‐pit mines, thereby constituting a significant contribution to the autonomous driving and field robotics communities.

Towards a Verifiable Toolchain for Robotics

There is a growing need for autonomous robots to complete complex tasks robustly in dynamic and unstructured environments. However, current robot performance is limited to simple tasks in controlled environments. To improve robot autonomy in complex environments, the robot's deliberation system must be able to synthesise correct plans for a task and generate contingency plans for handling anomalous scenarios that were not expected at design time. The robustness of such a system can be quantified using techniques for formal verification and validation. This paper outlines the progress of EU project CONVINCE (CONtext-aware Verifiable and adaptIve dyNamiC dEliberation), which aims to develop a software toolchain that aids developers in designing, developing, and deploying robot deliberation systems that are fully verified. We describe our modelling approach, each of the toolchain components, and how they interact. We also discuss survey results which demonstrate the demand for a verifiable toolchain among the robotics community.

Attention-based hand pose estimation with voting and dual modalities

Hand pose estimation has recently emerged as a compelling topic in the robotic research community, because of its usefulness in learning from human demonstration or safe human–robot interaction. Although deep learning-based methods have been introduced for this task and have shown promise, it remains a challenging problem. To address this, we propose a novel end-to-end architecture for hand pose estimation using red-green-blue (RGB) and depth (D) data (RGB-D). Our approach processes the two data sources separately and utilizes a dense fusion network with an attention module to extract discriminative features. The features extracted include both spatial information and geometric constraints, which are fused to vote for the hand pose. We demonstrate that our voting mechanism in conjunction with the attention mechanism is particularly useful for solving the problem, especially when hands are heavily occluded by objects or are self-occluded. Our experimental results on benchmark datasets demonstrate that our approach outperforms state-of-the-art methods by a significant margin.

EPVC: a novel initialization approach of visual-inertial integrated navigation

Abstract The fusion of visual and inertial measurements in robotics community is growing in popularity since both of them have complementary perceptual information. Pre-initializing gyroscope bias and accelerometer bias of the inertial measurement unit (IMU) is a critical issue to achieve a better fusion performance, and the metric scale is another crucial element to be estimated. Current mainstream loosely-coupled initialization methods are unstable as they do not incorporate IMU information into the visual structure from motion. In addition, the accuracy of the tightly-coupled methods is limited since they do not use visual observations to compensate gyroscope bias and usually ignore them in close-form solution. In this paper, a visual-inertial (VI) initialization method which we refer to as epipolar plane normal vectors coplanarity constraint (EPVC) method is proposed to solve gyroscope bias. A step further, a novel analytical solution is presented to optimize other parameters. Comparing the proposed method with VI navigation systems-mono and inertial-only optimization through the publicly available EuRoC dataset, the results demonstrate that the proposed method outperforms the existing methods in estimating the gyroscope bias and scale, and with the increase of initialization time, the accelerometer bias error and gravity direction error have a clear diminishing tendency.

Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction

In this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–inertial odometry and a Truncated Signed Distance Field (TSDF) free online reconstruction module. Firstly, to reduce the odometry drift errors we use scan-to-map matching, and inter-frame inertial information is used to generate prior relative pose estimation for later LiDAR-dominated optimization. Then, based on the motivation that the unevenly distributed residual terms tend to degrade the nonlinear optimizer, a novel residual density-driven Gauss–Newton method is proposed to obtain the optimal pose estimation. Secondly, to achieve fast and accurate 3D reconstruction, compared with TSDF-based mapping mechanism, a more compact map representation is proposed, which only maintains the occupied voxels and computes the vertices’ SDF values of each occupied voxels using an iterative Implicit Moving Least Squares (IMLS) algorithm. Then, marching cube is performed on the voxels and a dense mesh map is generated online. Extensive experiments are conducted on public datasets. The experimental results demonstrate that our method can achieve significant localization and online reconstruction performance improvements. The source code will be made public for the benefit of the robotic community.

A Systematic Review of Rapidly Exploring Random Tree RRT Algorithm for Single and Multiple Robots

Abstract Recent advances in path-planning algorithms have transformed robotics. The Rapidly exploring Random Tree (RRT) algorithm underpins autonomous robot navigation. This paper systematically examines the uses and development of RRT algorithms in single and multiple robots to demonstrate their importance in modern robotics studies. To do this, we have reviewed 70 works on RRT algorithms in single and multiple robot path planning from 2015 to 2023. RRT algorithm evolution, including crucial turning points and innovative techniques, have been examined. A detailed comparison of the RRT Algorithm versions reveals their merits, limitations, and development potential. The review’s identification of developing regions and future research initiatives will enable roboticists to use RRT algorithms. This thorough review is essential to the robotics community, inspiring new ideas, helping problem-solving, and expediting single- and multi-robot system development. This highlights the necessity of RRT algorithms for autonomous and collaborative robotics advancement.

Comparison of Human-Drone Distancing Studies Across In-person and Online Modalities

Human-robot proxemics behaviors can vary based on personal, robot, and environmental factors which, along with their deployment in public-facing interactions, calls for an in-depth exploration. This paper explores the impact of altitude and safety modifications of small unmanned aerial vehicle (sUAV) on users’ comfortable interaction distance. By leveraging interaction techniques from literature like video, sound, and simulations, we explore personal space interactions in online studies (N=376) with the sUAV and the Double telepresence robot. We then compare the findings with our in-person interaction data (N=47). While in-person interactions are the ultimate goal, online methods can be used to reduce resources, allow larger sample sizes, and may lead to a more comprehensive sampling of population than would be expected from in-person studies. The lessons learned from this work are applicable broadly within the social robotics community, even outside those who are interested in proxemics interactions, to conduct future crowd-sourced experiments. The various modalities provided similar trends when compared with data from in-person studies. While the distances may not have been precise compared to those measured in the real world, these experiments are useful to detect patterns in human-robot interactions, and to conduct formative studies before committing resources to in-person testing.

Experimental Comparison of Two 6D Pose Estimation Algorithms in Robotic Fruit-Picking Tasks

This paper presents an experimental comparison between two existing methods representative of two categories of 6D pose estimation algorithms nowadays commonly used in the robotics community. The first category includes purely deep learning methods, while the second one includes hybrid approaches combining learning pipelines and geometric reasoning. The hybrid method considered in this paper is a pipeline of an instance-level deep neural network based on RGB data only and a geometric pose refinement algorithm based on the availability of the depth map and the CAD model of the target object. Such a method can handle objects whose dimensions differ from those of the CAD. The pure learning method considered in this comparison is DenseFusion, a consolidated state-of-the-art pose estimation algorithm selected because it uses the same input data, namely, RGB image and depth map. The comparison is carried out by testing the success rate of fresh food pick-and-place operations. The fruit-picking scenario has been selected for the comparison because it is challenging due to the high variability of object instances in appearance and dimensions. The experiments carried out with apples and limes show that the hybrid method outperforms the pure learning one in terms of accuracy, thus allowing the pick-and-place operation of fruits with a higher success rate. An extensive discussion is also presented to help the robotics community select the category of 6D pose estimation algorithms most suitable to the specific application.

PPP ambiguity resolution based on factor graph optimization

Factor graph optimization has been widely used for state estimation in robotic SLAM community. Extensive algorithms have been proposed for camera/LiDAR/INS based SLAM. However, GNSS positioning based on factor graph optimization is limited, which prevents the introduction of high precise GNSS to robotic SLAM community. The current implementations are focused on pseudorange or RTK based positioning. PPP with ambiguity resolution (AR) is the state-of-the-art positioning technique for the past decade. Therefore, the PPP AR based on factor graph optimization is proposed, in which the pseudorange and carrier phases factors are constructed from the error equations of raw observations, while the ambiguity resolution factor is built from the ambiguity resolution. Results from 80 MGEX stations show that the average accuracy of static PPP is improved from 1.25, 0.61 and 2.29 cm to 0.81, 0.5 and 2.1 cm, corresponding to improvements of 35.1%, 18.7% and 8.7% in east, north, and up directions, respectively. As for kinematic PPP, the average accuracy is improved from 2.62, 2.21 and 5.8 cm to 1.64, 1.74 and 5.37 cm, corresponding to improvements of 37.5%, 21.6% and 7.4% in east, north, and up directions, respectively. The kinematic PPP was also verified with real-world data collected from a moving vehicle. After the first ambiguity fixing, the accuracy of PPP is improved from 3.7, 2.1 and 10.1 cm to 1.6, 2.0 and 9.0 cm for east, north and up component, respectively, corresponding to improvements of 32%, 5% and 11%. The above results confirm the efficiency of the proposed algorithm.

What Is the Impact of Releasing Code With Publications?: Statistics from the Machine Learning, Robotics, and Control Communities

What Is the Impact of Releasing Code With Publications?: Statistics from the Machine Learning, Robotics, and Control Communities

BinVPR: Binary Neural Networks towards Real-Valued for Visual Place Recognition.

Visual Place Recognition (VPR) aims to determine whether a robot or visual navigation system locates in a previously visited place using visual information. It is an essential technology and challenging problem in computer vision and robotic communities. Recently, numerous works have demonstrated that the performance of Convolutional Neural Network (CNN)-based VPR is superior to that of traditional methods. However, with a huge number of parameters, large memory storage is necessary for these CNN models. It is a great challenge for mobile robot platforms equipped with limited resources. Fortunately, Binary Neural Networks (BNNs) can reduce memory consumption by converting weights and activation values from 32-bit into 1-bit. But current BNNs always suffer from gradients vanishing and a marked drop in accuracy. Therefore, this work proposed a BinVPR model to handle this issue. The solution is twofold. Firstly, a feature restoration strategy was explored to add features into the latter convolutional layers to further solve the gradient-vanishing problem during the training process. Moreover, we identified two principles to address gradient vanishing: restoring basic features and restoring basic features from higher to lower layers. Secondly, considering the marked drop in accuracy results from gradient mismatch during backpropagation, this work optimized the combination of binarized activation and binarized weight functions in the Larq framework, and the best combination was obtained. The performance of BinVPR was validated on public datasets. The experimental results show that it outperforms state-of-the-art BNN-based approaches and full-precision networks of AlexNet and ResNet in terms of both recognition accuracy and model size. It is worth mentioning that BinVPR achieves the same accuracy with only 1% and 4.6% model sizes of AlexNet and ResNet.

Long-term navigation for autonomous robots based on spatio-temporal map prediction

The robotics community has witnessed a growing demand for long-term navigation of autonomous robots in diverse environments, including factories, homes, offices, and public places. The core challenge in long-term navigation for autonomous robots lies in effectively adapting to varying degrees of dynamism in the environment. In this paper, we propose a long-term navigation method for autonomous robots based on spatio-temporal map prediction. The time series model is introduced to learn the changing patterns of different environmental structures or objects on multiple time scales based on the historical maps and forecast the future maps for long-term navigation. Then, an improved global path planning algorithm is performed based on the time-variant predicted cost maps. During navigation, the current observations are fused with the predicted map through a modified Bayesian filter to reduce the impact of prediction errors, and the updated map is stored for future predictions. We run simulation and conduct several weeks of experiments in multiple scenarios. The results show that our algorithm is effective and robust for long-term navigation in dynamic environments.

Evaluating behavior trees

Behavior trees (BTs) are increasingly popular in the robotics community. Yet in the growing body of published work on this topic, there is a lack of consensus on what to measure and how to quantify BTs when reporting results. This is not only due to the lack of standardized measures, but due to the sometimes ambiguous use of definitions to describe BT properties. This work provides a comprehensive overview of BT properties the community is interested in, how they relate to each other, the metrics currently used to measure BTs, and whether the metrics appropriately quantify those properties of interest. Finally, we provide the practitioner with a set of metrics to measure, as well as insights into the properties that can be derived from those metrics.By providing this holistic view of properties and their corresponding evaluation metrics, we hope to improve clarity when using BTs in robotics. This more systematic approach will make reported results more consistent and comparable when evaluating BTs.

Closed Twisted Hydrogel Ribbons with Self-Sustained Motions under Static Light Irradiation.

Self-sustained motions are widespread in biological systems by harvesting energy from surrounding environments, which inspire scientists to develop autonomous soft robots. However, most-existing soft robots require dynamic heterogeneous stimuli or complex fabrication with different components. Recently, control of topological geometry has been promising to afford soft robots with physical intelligence and thus life-like motions. Reported here are a series of closed twisted ribbon robots, which exhibit self-sustained flipping and rotation under constant light irradiation. Both Möbius strip and Seifert ribbon robots are devised for the first time by using an identical hydrogel, which responds to light irradiation on either side. Experiment and simulation results indicate that the self-regulated motions of the hydrogel robots are related to fast and reversible response of muscle-like gel, self-shadowing effect, and topology-facilitated refresh of light-exposed regions. The motion speeds and directions of the hydrogel robots can be tuned over a wide range. These closed twisted ribbon hydrogels are further applied to execute specific tasks in aqueous environments, such as collecting plastic balls, climbing a vertical rod, and transporting objects. This work presents new design principle for autonomous hydrogel robots by benefiting from material response and topology geometry, which may be inspirative for the robotics community.

How Challenging is a Challenge? CEMS: a Challenge Evaluation Module for SLAM Visual Perception

Despite promising SLAM research in both vision and robotics communities, which fundamentally sustains the autonomy of intelligent unmanned systems, visual challenges still threaten its robust operation severely. Existing SLAM methods usually focus on specific challenges and solve the problem with sophisticated enhancement or multi-modal fusion. However, they are basically limited to particular scenes with a non-quantitative understanding and awareness of challenges, resulting in a significant performance decline with poor generalization and(or) redundant computation with inflexible mechanisms. To push the frontier of visual SLAM, we propose a fully computational reliable evaluation module called CEMS (Challenge Evaluation Module for SLAM) for general visual perception based on a clear definition and systematic analysis. It decomposes various challenges into several common aspects and evaluates degradation with corresponding indicators. Extensive experiments demonstrate our feasibility and outperformance. The proposed module has a high consistency of 88.298% compared with annotation ground truth, and a strong correlation of 0.879 compared with SLAM tracking performance. Moreover, we show the prototype SLAM based on CEMS with better performance and the first comprehensive CET (Challenge Evaluation Table) for common SLAM datasets (EuRoC, KITTI, etc.) with objective and fair evaluations of various challenges. We make it available online to benefit the community on our website.

3-D LiDAR Localization Based on Novel Nonlinear Optimization Method for Autonomous Ground Robot

3D Light Detection and Ranging (LiDAR)-based localization for autonomous ground robot in unknown environment is a critical ability, which has been extensively studied. In this paper, we propose a novel nonlinear optimization method to promote the 3D LiDAR localization performance. Firstly, due to that the objective function is the summation of point correspondence matching error items, a novel item quality evaluation criterion is proposed. The quality of each item is directly proportional to the degree of its matching error distance descent during the optimization process. Then, an attention mechanism based on the criterion is proposed and the objective function is iteratively refined by putting different attention weights on the matching error items. Thirdly, in the Gauss-Newton optimization framework for LiDAR localization, we point out that the Hessian matrix is essentially the sum of the Hessian matrices of high-quality and low-quality point correspondence subsets. To increase the dominance of the Hessian matrix derived from the high-quality point correspondences, the Hessian matrix of the estimated high-quality point correspondence subset in the last updating step is added to the current Hessian matrix. The proposed LiDAR localization is extensively evaluated on public and custom datasets. The experimental results demonstrate that the proposed mechanisms can effectively promote the LiDAR localization performance. We will make our source code open to serve as a new baseline for the robotic community.

Who’s in charge here? A survey on Trustworthy AI in Variable Autonomy Robotic Systems

This paper surveys the Variable Autonomy (VA) robotics literature that considers two contributory elements to Trustworthy AI: transparency and explainability. These elements should play a crucial role when designing and adopting robotic systems, especially in VA where poor or untimely adjustments of the system’s level of autonomy can lead to errors, control conflicts, user frustration and ultimate disuse of the system. Despite this need, transparency and explainability is, to the best of our knowledge, mostly overlooked in VA robotics literature or is not considered explicitly. In this paper, we aim to present and examine the most recent contributions to the VA literature concerning transparency and explainability. In addition, we propose a way of thinking about VA by breaking these two concepts down based on: the mission of the human-robot team; who the stakeholder is; what needs to be made transparent or explained; why they need it; and how it can be achieved. Last, we provide insights and propose ways to move VA research forward. Our goal with this paper is to raise awareness and inter-community discussions among the Trustworthy AI and the VA robotics communities.

Spatiotemporal Elastic Bands for Motion Planning in Highly Dynamic Environments

Robust motion planning in highly dynamic environments affected by challenging conditions remains an important task for autonomous robots, and an open problem for the robotics community. This paper proposes significant extensions to the elastic band method that gives more robustness to uncertainty in state and tracking performance, and a way to avoid fast-moving obstacles that may move multiple times faster than the vehicle in an efficient and non-conservative way. Particularly, we temporally enhance the algorithm, address future collisions spatiotemporally with continuous guarantees, and adapt the required safety clearance dynamically to address disturbances, control errors, and uncertainty. To validate the proposed method, results from a simulation study are presented, demonstrating the ability to safely plan trajectories in dynamic environments. The motion planner is lightweight and remarkably computationally efficient, with replanning orders of magnitudes faster than real-time needs by reaching and surpassing 1000Hz.

Wild Tech: Exploring South Africa's unique robotics landscape.

South Africa's location-specific opportunities have enabled local researchers to establish a niche in the robotics community.