IROS 2023 论文列表

In this work, a novel online model-free controller for an underactuated dirigible is developed based on reinforcement learning and optimal control theory. A reinforcement learning structure is used while overcoming the dependence of the value function on future values by introducing a neural network that is adapted using input-output data. The suboptimal critic neural network is structured such that optimality is guaranteed over the interval from which the data is valid. The system performance is validated using a highly realistic physics engine, Gazebo, with the robot operating system (ROS) interface and the results are compared to the performance of a model-based controller specifically designed to control the airship model. It is emphasized that the proposed formulation does not leverage any knowledge of vehicle dynamics and thus is considered a vehicle agnostic control strategy.

State-of-the-art lidar place recognition models exhibit unreliable performance when tested on environments different from their training dataset, which limits their use in complex and evolving environments. To address this issue, we investigate the task of uncertainty-aware lidar place recognition, where each predicted place must have an associated uncertainty that can be used to identify and reject incorrect predictions. We introduce a novel evaluation protocol and present the first comprehensive benchmark for this task, testing across five uncertainty estimation techniques and three large-scale datasets. Our results show that an Ensembles approach is the highest performing technique, consistently improving the performance of lidar place recognition and uncertainty estimation in novel environments, though it incurs a computational cost. Code is publicly available at https://github.com/csiro-robotics/Uncertainty-LPR.

Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has emerged as a low-cost and scalable indoor positioning solution. However, in cluttered environments, the performance of UWB TDOA-based localization deteriorates due to the biased and non-Gaussian noise distributions induced by obstacles. In this work, we present a bi-level optimization-based joint localization and noise model learning algorithm to address this problem. In particular, we use a Gaussian mixture model (GMM) to approximate the measurement noise distribution. We explicitly incorporate the estimated state's uncertainty into the GMM noise model learning, referred to as uncertainty-aware GMM, to improve both noise modeling and localization performance. We first evaluate the GMM noise model learning and localization performance in numerous simulation scenarios. We then demonstrate the effectiveness of our algorithm in extensive real-world experiments using two different cluttered environments. We show that our algorithm provides accurate position estimates with low-cost UWB sensors, no prior knowledge about the obstacles in the space, and a significant amount of UWB radios occluded.

In the context of robotics, accurate ground truth positioning is essential for the development of Simultaneous Localization and Mapping (SLAM) and control algorithms. Robotic Total Stations (RTSs) provide accurate and precise reference positions in different types of outdoor environments, especially when compared to the limited accuracy of Global Navigation Satellite System (GNSS) in cluttered areas. Three RTSs give the possibility to obtain the six-Degrees Of Freedom (DOF) reference pose of a robotic platform. However, the uncertainty of every pose is rarely computed for trajectory evaluation. As evaluation algorithms are getting increasingly precise, it becomes crucial to take into account this uncertainty. We propose a method to compute this six-DOF uncertainty from the fusion of three RTSs based on Monte Carlo (MC) methods. This solution relies on point-to-point minimization to propagate the noise of RTSs on the pose of the robotic platform. Five main noise sources are identified to model this uncertainty: noise inherent to the instrument, tilt noise, atmospheric factors, time synchronization noise, and extrinsic calibration noise. Based on extensive experimental work, we compare the impact of each noise source on the prism uncertainty and the final estimated pose. Tested on more than 50 km of trajectories, our comparison highlighted the importance of the calibration noise and the measurement distance, which should be ideally under 75 m. Moreover, it has been noted that the uncertainty on the pose of the robot is not prominently affected by one particular noise source, compared to the others.

Localization is a fundamental task in robotics for autonomous navigation. Existing localization methods rely on a single input data modality or train several computational models to process different modalities. This leads to stringent computational requirements and sub-optimal results that fail to capitalize on the complementary information in other data streams. This paper proposes UnLoc, a novel unified neural modeling approach for localization with multi-sensor input in all weather conditions. Our multi-stream network can handle LiDAR, Camera and RADAR inputs for localization on demand, i.e., it can work with one or more input sensors, making it robust to sensor failure. UnLoc uses 3D sparse convolutions and cylindrical partitioning of the space to process LiDAR frames and implements ResNet blocks with a slot attention-based feature filtering module for the Radar and image modalities. We introduce a unique learnable modality encoding scheme to distinguish between the input sensor data. Our method is extensively evaluated on Oxford Radar RobotCar, ApolloSouthBay and Perth-WA datasets. The results ascertain the efficacy of our technique. The dataset, results, and codes are available at https://github.com/IbrahimUWA/UnLoc

Ultrafast acoustic holography (AH) enabling dynamic contactless micro-nano robotic manipulation has recently attracted wide attention. As an advanced technique, AH encodes specific three-dimensional (3D) acoustic field on a two-dimensional (2D) hologram whereby realizing holographic reconstruction with high fidelity. However, current approaches face the limitation of encoding time, accuracy and flexibility, thus, leading to inapplicability for dynamic and precise robotic manipulation. Here, we develop an approach to overcome these issues. Its basic idea is to use a convolutional neural network trained in a self-supervised manner with iterative interaction with virtual physical environment. Energy conservation is incorporated to access the physical constrain during wave propagation. The experimental results demonstrate that the proposed method circumvents laborious annotated dataset preparation and boosts the reinforcement from physics model. By the validation and comparison on distinct acoustic fields with various patterns, the accuracy and real-time performance of the proposed method are confirmed supporting dynamic and precise robotic manipulation.

As robotic intelligence increases, so does the im-portance of agents that collect data from real-world environments. When learning in contact with the environment, one must consider how to minimize the impact on the environment and maintain reproducibility. To achieve this, the contact force with the environment must be reduced. One way to achieve this is to reduce the inertia of the arm. In this study, we present an arm we have developed with 6 degrees of freedom and low inertia. The inertia of our arm has been significantly reduced compared to previous research, and experiments have confirmed that it also has low joint friction torque and good contact sensitivity.

Automotive surround-view camera systems have been commonly employed in automated driving to aid in near-field sensing and other perception tasks. Due to the large size of the body and the presence of multiple blind spots, panoramic surround-view systems are particularly crucial for tractor-trailer vehicles. However, the non-rigid body of tractor-trailer vehicles introduces pose changes between cameras, rendering traditional calibration-based methods inadequate. Additionally, cameras mounted separately on the tractor and the trailer will experience independent vibrations, resulting in undesirable shakiness in captured videos. In this paper, we propose a unified video stabilization and stitching method to address these challenges, which can smooth the unsteady frames and align the images from moving cameras. Delving into video stabilization techniques, we extend mesh-based motion model for unified stitching and leverage deep-learning based modules to handle complex real-world scenarios. Moreover, we design a new optimization framework to estimate the optimal displacements of mesh vertices, enabling simultaneous stabilization and stitching of frames. The experimental results, obtained by public datasets and videos captured from a model tractor-trailer vehicle, demonstrate that our approach outperforms previous methods and is highly effective in real-world applications.

This paper introduces UVIO, a multi-sensor framework that leverages Ultra Wide Band (UWB) technology and Visual-Inertial Odometry (VIO) to provide robust and low-drift localization. In order to include range measurements in state estimation, the position of the UWB anchors must be known. This study proposes a multi-step initialization procedure to map multiple unknown anchors by an Unmanned Aerial Vehicle (UAV), in a fully autonomous fashion. To address the limitations of initializing UWB anchors via a random trajectory, this paper uses the Geometric Dilution of Precision (GDOP) as a measure of optimality in anchor position estimation, to compute a set of optimal waypoints and synthesize a trajectory that minimizes the mapping uncertainty. After the initialization is complete, the range measurements from multiple anchors, including measurement biases, are tightly integrated into the VIO system. While in range of the initialized anchors, the VIO drift in position and heading is eliminated. The effectiveness of UVIO and our initialization procedure has been validated through a series of simulations and real-world experiments.

In order for robots to follow open-ended instructions like “go open the brown cabinet over the sink,” they require an understanding of both the scene geometry and the semantics of their environment. Robotic systems often handle these through separate pipelines, sometimes using very different representation spaces, which can be suboptimal when the two objectives conflict. In this work, we present USA-Net, a simple method for constructing a world representation that encodes both the semantics and spatial affordances of a scene in a differentiable map. This allows us to build a gradient-based planner which can navigate to locations in the scene specified using open-ended vocabulary. We use this planner to consistently generate trajectories which are both shorter 5-10% shorter and 10-30% closer to our goal query in CLIP embedding space than paths from comparable grid-based planners which don't leverage gradient information. To our knowledge, this is the first end-to-end differentiable planner optimizes for both semantics and affordance in a single implicit map. Code and visuals are available at our website: usa.bolte.cc

In this paper, a new demonstration-based path-planning framework for the visual inspection of large structures using UAVs is proposed. We introduce UPPLIED: UAV Path PLanning for InspEction through Demonstration, which utilizes a demonstrated trajectory to generate a new trajectory to inspect other structures of the same kind. The demonstrated trajectory can inspect specific regions of the structure and the new trajectory generated by UPPLIED inspects similar regions in the other structure. The proposed method generates inspection points from the demonstrated trajectory and uses standardization to translate those inspection points to inspect the new structure. Finally, the position of these inspection points is optimized to refine their view. Numerous experiments were conducted with various structures and the proposed framework was able to generate inspection trajectories of various kinds for different structures based on the demonstration. The trajectories generated match with the demonstrated trajectory in geometry and at the same time inspect the regions inspected by the demonstration trajectory with minimum deviation. The experimental video of the work can be found at https://youtu.be/YqPx-cLkv04.

Robot teleoperation has been studied for the past 70 years and is relevant in many contexts, such as in the handling of hazardous materials and telesurgery. The COVID19 pandemic has rekindled interest in this topic, but the existing robotic education kits fall short of being suitable for teleoperated robotic manipulator learning. In addition, the global restrictions of motion motivated large investments in online/hybrid education. In this work, a newly developed robotics education kit and its ecosystem are presented which is used as the backbone of an online/hybrid course in teleoperated robots. The students are divided into teams. Each team designs, fabricates (3D printing and assembling), and implements a control strategy for a master device and gripper. Coupling those with the UMIRobot, provided as a kit, the students compete in a teleoperation challenge. The kit is low cost (<100USD), which allows higher-learning institutions to provide one kit per student and they can learn in a risk-free environment. As of now, 73 such kits have been assembled and sent to course participants in eight countries. As major success stories, we show an example of gripper and master designed for the proposed course. In addition, we show a teleoperated task between Japan and Bangladesh executed by course participants. Design files, videos, source code, and more information are available at https://mmmarinho.github.io/UMIRobot/

As the interest in ecosystem protection increases, many researchers are focusing on tracking flying insects to preserve biodiversity. Invasive alien species (IAS) such as Vespa velutina nigrithorax require extensive consideration in this regard owing to size and weight limitations. In this paper, we propose and experimentally validate an unmanned aerial vehicle (UAV)-based trilateration system for localization and tracking of flying insects. The proposed tracking algorithm is based on the use of multiple omnidirectional antennas, and it improves the tracking accuracy and shortens the tracking time compared to those of the existing unidirectional antennas. In addition, we reduce the measurement error and noise by applying a finite impulse response (FIR) filter to the received signal strength (RSS) emitted from the radio-telemetry transmitter. A field experiment involving the proposed trilateration system was conducted in an apiary. This experiment was divided into three sub-experiments as follows: 1) behavior experiment, 2) ground truth experiment, and 3) localization and tracking experiment. The proposed system contributes to the research on UAV-based tracking systems that respond to the speed of spreading of invasive V. velutina.

The existing method of detecting defects in train components, which relies on visual identification, requires extensive involvement from inspectors and presents certain limitations. In this study, a two-stage defect detection based on prior knowledge was developed, which first detects the types and positions of components, and then conducts targeted detection of possible existing defect types. The algorithm introduces the prior knowledge of the relative spatial position relationship of components and optimizes the detection of sub-components by cascaded convolutional neural networks and local scale-up. In this study, three methods were used, including deep learning, template matching, and quantitative evaluation based on prior knowledge, to perform targeted detection of defect types that may occur in components. Experiments have verified the adaptability and accuracy of the method, demonstrating its high value for engineering applications.

The advantages of cable-driven exoskeleton robots with series elastic actuators can be summarized in twofold: 1) the inertia of the robot joint is relatively low, which is more friendly for human-robot interaction; 2) the elastic element is tolerant to impacts and hence provides structural safety. As trade-offs, the overall dynamic model of such a system is of high order and subject to both unmodelled disturbances (due to the cable-driven mechanism) and external torques (due to the human-robot interaction), opening up challenges for the controller development. This paper proposes a new trajectory-tracking control scheme for cable-driven upper-limb exoskeleton robots with series elastic actuators. The control objectives are achieved in two stages: Stage I is to approximate then compensate for unmodelled disturbances with iterative learning techniques; Stage II is to employ a suboptimal model predictive controller to drive the robot to track the desired trajectory. While controlling such a robot is not trivial, the proposed control scheme exhibits the advantages of force-sensorlessness, high accuracy, and low complexity compared with other methods in the real-world experiments.

A problem that plagues robotic grasping is the misalignment of the object and gripper due to difficulties in precise localization, actuation, etc. Under-actuated robotic hands with compliant mechanisms are used to adapt and compensate for these inaccuracies. However, these mechanisms come at the cost of controllability and coordination. For instance, adaptive functions that let the fingers of a two-fingered gripper adapt independently may affect the coordination necessary for grasping small objects. In this work, we develop a two-fingered robotic hand capable of grasping objects that are offset from the gripper's center, while still having the requisite coordination for grasping small objects via a novel gear-type synchronization mechanism with a magnet. This gear synchronization mechanism allows the adaptive finger's tips to be aligned enabling it to grasp objects as small as toothpicks and washers. The magnetic component allows this coordination to automatically turn off when needed, allowing for the grasping of objects that are offset/misaligned from the gripper. This equips the hand with the capability of grasping light, fragile objects (strawberries, creampuffs, etc.) to heavy frying pan lids, all while maintaining their position and posture which is vital in numerous applications that require precise positioning or careful manipulation.

Accurate tracking of transparent objects, such as glasses, plays a critical role in many robotic tasks such as robot-assisted living. Due to the adaptive and often reflective texture of such objects, traditional tracking algorithms that rely on general-purpose learned features suffer from reduced performance. Recent research has proposed to instill trans-parency awareness into existing general object trackers by fusing purpose-built features. However, with the existing fusion techniques, the addition of new features causes a change in the latent space making it impossible to incorporate transparency awareness on trackers with fixed latent spaces. For example, many of the current days' transformer-based trackers are fully pre-trained and are sensitive to any latent space perturbations. In this paper, we present a new feature fusion technique that integrates transparency information into a fixed feature space, enabling its use in a broader range of trackers. Our proposed fusion module, composed of a transformer encoder and an MLP module, leverages key query-based transformations to embed the transparency information into the tracking pipeline. We also present a new two-step training strategy for our fusion module to effectively merge transparency features. We propose a new tracker architecture that uses our fusion techniques to achieve superior results for transparent object tracking. Our proposed method achieves competitive results with state-of-the-art trackers on TOTB, which is the largest transparent object tracking benchmark recently released. Our results and the implementation of code will be made publicly available at https://github.com/kalyan0510/TOTEM.