IROS 2024 Papers

Exploration robots are typically equipped with either wheels or legs for mobility. However, these conventional designs inherently possess certain limitations, such as restricted ability to navigate through steep slopes, soft soil or muddy watery terrain. Despite the advancements in robotics technoloy, these challenges persist in both terrestrial and extraterrestrial environments. To overcome these obstacles, we have proposed a novel solution in the form of a spherical robot with a soft shell. This paper provides an overview of the construction of this spherical robot and the design of its soft shell for adapting to varying terrain. Furthermore, we analyze and characterize its performance across various terrain types as the shell pressure is changed. By employing this approach, the paper establishes the suitability and efficiency of the spherical robot in traversing different terrain by adapting the shell pressure.

Soft robotic technologies are laying the foundations for a wide range of applications involving manipulation, gripping, and locomotion. In particular, soft pneumatic actuators have been very popular for their high power-to-weight ratio and ability to yield significantly large motions. However, these actuators are usually limited in delivering reconfigurable deformation, limiting their versatility. A recent direction to rectify this limitation is to couple pneumatic actuation with active materials. Proof-of-the-concept designs has been developed to demonstrate that the actuator’s deformation characteristics could be configured by selectively softening structural elements in an ON-OFF manner. In this paper, following this idea, we propose a novel soft pneumatic actuator coupled with stiffness programmable polylactic acid (PLA) elements. The proposed design is based on heating wires embedded in the PLA elements. Changes in the elastic properties of PLA near the glass transition temperature (Tg) coupled with pneumatic actuation are exploited to activate the actuator’s variable stiffness features. This study shows that the stiffness of 3D-printed elements made of PLA can be tuned by means of temperature controlled heaters. The proposed actuator is able to extend by 19.5% of its original length and produce 15◦ angular deformation. We demonstrate the actuator’s ability to perform different reconfigurable orientations based on different temperature and pressure inputs. The proposed idea could be considered as an actuation unit that can deliver complex deformations when configured in multitudes.

A system is expected to facilitate coordination among multiple construction machines or robots, enabling them to adaptively perform various tasks in disaster sites and unknown environments. Prior research has generally adopted a model-based approach to designing cooperative behavior. However, it is difficult to adapt to environments and scenarios that cannot be predicted by the model. In recent years, it has been reported that a robot equipped with foundation models can adapt to unknown (open) environments and unpredictable situations. However, there has been little discussion on foundation models for multiple robot systems; a flow that cooperatively handles unexpected events does not exist. In this paper, we propose the system flow that enables multiple robots to adaptively coordinate to unforeseen scenarios based on the functional expressions of each other and environment understanding utilizing GPT-4 and GPT-4V. Through experimentation, we verify that the proposed flow is able to adapt to an unforeseen environment, particularly path obstruction via robot experiments. Furthermore, we examine the validity of the proposed flow by varying the robots’ functional expressions and sensor information for the environment.

This paper proposes a novel design framework for a fully actuated drone with non-isotropic wrench shape. Conventional fully actuated drones face challenges related to high energy consumption during aerial contact manipulation, particularly when force exertion along a specific direction is required. This challenge arises from the balanced tilting of propellers, which leads to an isotropic wrench shape. To address this limitation, we explicitly define the required wrench set (RW) for aerial contact manipulation and integrate it into an optimization problem. To ensure the generation of the RW, we employ the hyperplane shifting method, commonly used for verifying wrench feasibility in cable-driven robots. The optimization aims to minimize hovering energy consumption while ensuring wrench feasibility. Consequently, the proposed design demonstrates a significant improvement over the typical fully actuated drone, with hovering and contact force efficiency more than doubled and nearly 1.4 times higher, respectively. The effectiveness of our design is also validated through simulation.

Exoskeletons for robot operation necessitate shoulders with high range of motions and high degrees of freedom to fit the operator’s shoulder girdle. These shoulder joints need high torque for force feedback on the operator. Existing exoskeletons struggle to simultaneously meet these requirements of high DOFs, wide ROM, and high torque due to spatial constraints. This study introduces an exoskeleton with a distal branching link mechanism that addresses this issue by concentrating on each link’s absolute and relative degrees of freedom. In the proposed exoskeleton, the end-effector’s absolute DOF, the forearm’s absolute DOF, and the end-effector and forearm’s relative DOF are matched between the operator and the exoskeleton. This is achieved while reducing the overall DOF by sharing the root link system’s DOF. Furthermore, by avoiding direct attachment of the operator to the exoskeleton’s shoulder, the design can accommodate the human shoulder’s high torque and high ROM. The study demonstrates that the branching exoskeleton outperforms existing link-fixed exoskeletons in terms of tracking the operator’s arms and the torque required by the exoskeleton’s joints. Utilizing this exoskeleton, we successfully maneuvered an actual humanoid robot to perform daily activities where the forearm posture is crucial.

This work presents the design of Stickbug, a six-armed, multi-agent, precision pollination robot that combines the accuracy of single-agent systems with swarm parallelization in greenhouses. Precision pollination robots have often been proposed to offset the effects of a decreasing population of natural pollinators, but they frequently lack the required parallelization and scalability. Stickbug achieves this by allowing each arm and drive base to act as an individual agent, significantly reducing planning complexity. Stickbug uses a compact holonomic Kiwi drive to navigate narrow greenhouse rows, a tall mast to support multiple manipulators and reach plant heights, a detection model and classifier to identify Bramble flowers, and a felt-tipped end-effector for contact-based pollination. Initial experimental validation demonstrates that Stickbug can attempt over 1.5 pollinations per minute with a 49% success rate. Additionally, a Bramble flower perception dataset was created and is publicly available alongside Stickbug’s software and design files.

Regular maintenance and inspection of cables are essential for cable-stayed bridges and suspension bridges, as cables serve as the core components. In order to enable automated detection of cables, this paper proposes a novel wheeled cable inspection robot. The robot utilizes a bilateral wheel structure and is composed of four independent suspension mechanisms. By collaborating with a lifting mechanism, the robot achieves functions such as adhesion, climbing, and obstacle overcoming. The robot is powered by a lithium polymer battery and operated via wireless control by ground personnel. This paper provides a detailed exposition on the structure design and control system of robots, and conducts a mechanical analysis of the suspension mechanism of robots. The maximum obstacle-negotiation height of the robot is calculated, and a mechanical model for cable climbing is established. During prototype testing, the robot demonstrated a mass of 6.7kg, a maximum payload capacity of 6kg, a maximum obstacle height of 10mm, and a fastest climbing speed of 14m/min. These specifications meet the requirements of practical inspections.

This work addresses the critical challenge of integrating drones into human-aerial robot interaction by presenting a novel Soft Flexible Aerial Robotics (SFAR) design. SFAR features an innovative low-pressure inflatable airbag structure that replaces traditional rigid frames, enhancing safety by mitigating collision risks with humans and payloads. To control this unconventional aerial platform, we present a control strategy based on a virtual link dynamics model that exploits the drone’s unique design. Our contributions include the pioneering design of an aerial robot specifically for Human-Aerial Robot Interaction (HARI), a novel control framework that balances flight performance with passive safety, and the validation of SFAR through real-world experiments, demonstrating its ability to perform at par with traditional rigid-body drones while offering enhanced safety features for seamless and safe integration into human environments.

This paper presents the development of a quadrotor vehicle capable of movement between air and water environments. The design challenges, including torque matching in the propulsion system, balance maintenance in different mediums, and waterproofing requirements, are discussed. A prototype is constructed to demonstrate the feasibility of the proposed design. Through the tilting mechanism in four rotors, the vehicle can not only fly in air like a normal quadrotor vehicle, but also propel in water with two configured rear thrusters. Moreover, the four tilted rotors cooperate in aligning the center of gravity and buoyancy, addressing limitations observed in traditional aerial-aquatic vehicles. The prototype has been verified through experiments in both air and water.

Cable-driven continuum robots in bronchoscopic procedures hold immense potential to revolutionize the diagnosis and treatment of lung cancer. However, robotic bronchoscopes in current studies are typically large in size and inflexible. Therefore, this article introduces a novel cable-driven continuum robot bronchoscopy system that achieves modular design between the actuation and operation ends. A continuum structure with a dual-segment notched flexible skeleton, featuring a wall thickness of 0.45 mm, has been designed to perform bending movements exceeding 190°. This enhances flexibility and increases the spatial capacity of the working channels. A kinematic model was developed, integrating the actuation force and the mechanical characteristics of the driving cables for error compensation, estimating the correlation between the displacement of the driving cables and the position of the continuum robot’s end-effector. The verification showed that the root mean square error (RMSE) of the end-effector position is 2.57 mm, which accounts for 4.8% of the continuum’s length. A prototype of the robotic bronchoscopy system was created, and its performance and potential applications in bronchoscopic intervention surgeries were validated through vivo pig intervention experiments.

Tactile interfaces, that can convey information to humans via tactile feedback, are still relatively rare. In this study we present a prototype ‘tactile scanner’, that fixes onto a user’s arm, and using arrays of capacitive sensors and vibratory motors, provides users with a sense of the proximity of objects near their arm. Through two experiments, we show that the device enables users to detect not just the position of objects, but also estimate their shapes and orientations. Finally, in a third experiment, we compare the user accuracy with the tactile scanner and their accuracy with real touch.

The challenge of human-robot collaboration, particularly in the context of enhancing the productivity of work processes, has been a pivotal area of research and development for many years. Despite significant advancements, there remains a substantial gap in the design of these systems to cater specifically to individuals with disabilities. This paper presents an innovative approach in assistive robotics, focusing on the development of a work cell designed to facilitate individuals with single-arm functionality. Through the integration of depth camera technology, machine learning algorithms, and Mediapipe human tracking, our system is capable of interpreting human intentions, thereby making interactions with robots more intuitive and effective. Central to our research is the design of a specialized workspace that assists in object handling and incorporates a fully functional One-Handed Soldering Tool, integrated within a robotic arm setup. This work cell is tailored for users with limited arm functionality, demonstrating the system’s versatility and providing invaluable insights into the practical implementation of applied robotics to bridge the theoretical and practical aspects of assistive technology.

Since the shape of industrial endoscopes is passively altered according to the contact around it, manual inspection approaches of aeroengines through the inspection ports have unreachable areas, and it’s difficult to traverse multistage blades and inspect them simultaneously, which requires engine disassembly or the cooperation of multiple operators, resulting in efficiency decline and increased costs. To this end, this paper proposes a novel continuum manipulator with push-pull multisection structure which provides a potential solution for the disadvantages mentioned above due to its higher flexibility, passability, and controllability in confined spaces. The ultra-slender design combined with a tendon-driven mechanism makes the manipulator acquire enough workspace and more flexible postures while maintaining a light weight. Considering the coupling between the tendons in multisection, a innovative kinematics decoupling control method is implemented, which can realize real-time control in the case of limited computational resources. A prototype is built to validate the capabilities of mechatronic design and the performance of the control algorithm. The experimental results demonstrate the advantages of our continuum manipulator in the in-situ inspection of aeroengines’ multistage blades, which has the potential to be a replacement solution for industrial endoscopes.

Supernumerary robotic limbs (SRLs) provide additional wearable limbs to enhance the user’s physical abilities. Most SRLs employ rigid structures, resulting in uncomfortable wearing experience and insufficient flexible manipulation. As a new type of SRL, soft SRLs offer operational flexibility, lightweight structure, and wearing safety, compensating for the shortcomings of rigid SRLs. However, due to the complex actuation mechanisms, soft SRLs pose challenges in multiple deformations and accurate controlling. In this paper, a soft SRL actuated by fiber-reinforced actuators (FRAs) is proposed. A kinematic model is established to capture the posture of the SRL. A control system is proposed to adjust the SRL posture precisely by configuration of the FRAs. Finally, the accuracy of the proposed control strategy is verified through experiments, and the SRL prototype exhibits flexibility and adaptability to various scenarios, effectively assisting users in accomplishing complex tasks.

This paper presents a study on the design and control of a novel soft-rigid lower limb exoskeleton robot. First, based on anatomy, a novel exoskeleton structure design is proposed that applies Curl Pneumatic Artificial Muscles (CPAMs) to lower limb joints to actuate lower limb movement, and transmit force and motion through rigid parts. A phenomenological characteristic model of the CPAM based on experimental tests and fitting methods is constructed for exoskeleton control. Then, a feedforward-feedback hybrid control method based on four CPAM characteristic models is proposed to improve the control accuracy and stability of the exoskeleton. A human-in-the-loop control method based on human-robot hybrid dynamics modeling and human intentions and states is proposed to improve the human-robot interaction performance of exoskeletons. Experimental results show that feedforward-feedback hybrid control can reduce the maximum tracking error of the exoskeleton to 3.4% for hip, 2.9% for knee, and 4.7% for ankle joint. The exoskeleton can achieve intentional control based on EMG signals. With the assistance of the exoskeleton, the muscle activity of the human lower limbs is reduced by an average of 32.2%. The proposed soft-rigid lower limb exoskeleton robot has the advantages of being lightweight, having good flexibility, being comfortable wearing and having good human-computer interaction, which can improve efficiency. In the future, it will provide more effective intelligent rehabilitation equipment for patients with lower limb movement disorders.

Robotic arms are key components in fruit-harvesting robots. In agricultural settings, conventional serial or parallel robotic arms often fall short in meeting the demands for a large workspace, rapid movement, enhanced capability of obstacle avoidance and affordability. This study proposes LingXtend, a novel hybrid six-degree-of-freedom (DoF) robotic arm that combines the advantages of parallel and serial mechanisms. Inspired by yoga, we designed two sliders capable of moving independently along a single rail, acting as two feet. These sliders are interconnected with linkages and a meshed-gear set, allowing the parallel mechanism to lower itself and perform a split to pass under obstacles. This unique feature allows the arm to avoid obstacles such as pipes, tables and beams typically found in greenhouses. Integrated with serially mounted joints, the patented hybrid arm is able to maintain the end’s pose even when it moves with a mobile platform, facilitating fruit picking with the optimal pose in dynamic conditions. Moreover, the hybrid arm’s workspace is substantially larger, being almost three times the volume of UR3 serial arms and fourteen times that of the ABB IRB parallel arms. Experiments show that the repeatability errors are 0.017 mm, 0.03 mm and 0.109 mm for the two sliders and the arm’s end, respectively, providing sufficient precision for agricultural robots.

The shoulder glenohumeral complex stands out as one of the most complex structures within the human body. Designing a system that can effectively interface with it poses a significant challenge for researchers. In this study, we propose a methodology based on evaluating various metrics to assess the performance of new kinematic solutions for mimicking the glenohumeral complex. The proposed method is demonstrated on an existing design (Float) of an upper-limb exoskeleton. The results show a successful expansion of the reachable workspace and enhancement of the shoulder internal-external rotation. The improvements ensure the necessary range-of-motion for the patient’s natural use of the exoskeleton. Specifically, the existing Eulerian wrist architecture is replaced with a 3-degree-of-freedom RPY wrist to better resemble the glenohumeral shoulder joint complex. This study also explores the trade-offs between these enhancements and the desired system manipulability.

3D perception ability is crucial for generalizable robotic manipulation. While recent foundation models have made significant strides in perception and decision-making with RGB-based input, their lack of 3D perception limits their effectiveness in fine-grained robotic manipulation tasks. To address these limitations, we propose a Depth Information Injection (DI2) framework that leverages the RGB-Depth modality for policy fine-tuning, while relying solely on RGB images for robust and efficient deployment. Concretely, we introduce the Depth Completion Module (DCM) to extract the spatial prior knowledge related to depth information and generate virtual depth information from RGB inputs to aid policy deployment. Further, we propose the Depth-Aware Codebook (DAC) to eliminate noise and reduce the cumulative error from the depth prediction. In the inference phase, this framework employs RGB inputs and accurately predicted depth data to generate the manipulation action. We conduct experiments on simulated LIBERO environments and real-world scenarios, and the experiment results prove that our method could effectively enhance the pre-trained RGB-based policy with 3D perception ability for robotic manipulation. The website is released at https://gewu-lab.github.io/DepthHelps-IROS2024.

3D LiDAR-based perception has made remarkable advancements, leading to the widespread adoption of LiDAR in autonomous driving systems. Despite these technological strides, variations in LiDAR sensors and environmental conditions can significantly deteriorate the performance of perception models, primarily due to changes in the density of point clouds. Recent studies in domain generalization have aimed to mitigate this challenge; however, they often rely on the availability of sequential data and ego-motion, which limits their applicability. To address these limitations, we propose two novel methods that enable network operation in a density-aware fashion without any constraints, thereby ensuring consistent performance despite fluctuations in point cloud density. First, we design the network to be density-aware by utilizing the kernel occupancy information from the 3D sparse convolution as geometric features. Subsequently, we further enhance density awareness by incorporating voxel-wise density prediction as an auxiliary task in a self-supervised manner. Our method demonstrates superior performance over current state-of-the-art approaches, achieving this without the need for specific data prerequisites. Our approach is compatible with a variety of 3D backbone architectures, enhancing domain generalization performance by 18.4% while adding a minimal computational overhead of only 7ms.