Robotics and Autonomous Systems最新文献

In the real world, multi-robot systems need to deal with on-the-fly (runtime) arrivals of new sets of tasks. This entails repeated adjustments of their current task allocations to include the newer ones while also ensuring that the overall performance does not degrade. This paper proposes a decentralized and distributed dynamic task allocation algorithm to handle this issue in a multi-robot scenario. The proposed work provides a conflict-free allocation of a set of tasks constituting a job to robots and minimizes the total execution time. These jobs can comprise multiple independent and/or dependent tasks or a combination thereof, which are injected on-the-fly into a network of robots. The dependent tasks of a job are related by precedence constraints that specify the ordering or dependencies between pairs of tasks. The work also describes a decentralized adaptive energy threshold mechanism for determining whether or not a robot needs to visit a battery stockpile after the execution of a task. Conflicting task selections among the robots in this decentralized set-up are resolved using mobile agents during runtime. Apart from allocating tasks to the robots, these mobile agents exploit the benefits of centralized and decentralized systems and provide an advantage over auction-based task allocation algorithms. The proposed algorithm takes into consideration the energy requirements, both during the task allocation process and actual execution. The proposed algorithm also caters to strategies to deal with delays caused by obstacles and congestion during the actual execution of the tasks. Experiments conducted using Webots, an open-source robot simulator, and Tartarus, a multi-agent platform, authenticate the efficacy of the proposed algorithm compared to other prominent task allocation algorithms in terms of minimization of average waiting time, total task allocation time, total job allocation time, and total execution time of an experiment.

The integration of robotics in nursing is a significant shift in healthcare, driven by the aging global population and the increasing demand for care. Robots in nursing can handle less technical tasks such as patient transport and rehabilitation activities. This support allows caregivers to focus on less strenuous nursing duties and more direct patient care. Human-Robot Interaction (HRI) plays an important role in this challenging context. In this research, we present an autonomous hospital transport system based on the ROS 2 framework, focusing on enhancing HRI in the healthcare environment. It encompasses the development of a control architecture for autonomous robot behavior, the implementation of machine learning for emergency detection, and the creation of a user-friendly interface for both patients and staff. The proposed concepts were validated in real-world scenarios in three different hospitals in Germany. This not only demonstrates the practical application of this system but also shares insights and methods, encouraging further advancement in the field of healthcare robotics.

Stability of obstacle–crossing and structural optimization are important issues in the research of tracked mobile robots. In this paper, in order to fully understand the obstacle–surmounting ability of the robot, the relationship between the position of the center of gravity and the posture of the front and rear swing arms is analyzed. Based on the motion mechanism of the robot crossing obstacles, the geometric model and the dynamic model are established for the key states in the obstacle crossing process. Based on these models, a multi-objective optimization problem for the maximum obstacle–crossing height and minimum driving torque is established during the obstacle crossing process of the robot, which must meet geometric, slip, and stability constraints. To effectively handle the optimization problem of tracked mobile robots, an improved non–dominated sorting genetic algorithm with elite strategy version II based on adaptive genetic strategy (NSGA-II-AGS) is proposed in this paper. Some meaningful relationships between the objective function and the design variables are obtained through sensitivity analysis. Finally, the robot's obstacle-crossing ability was verified through virtual simulation and prototype experiments. These excellent performances enable the proposed NSGA-II-AGS to be qualified for dealing with the multi-objective optimization problem.

This paper presents a novel dynamic motion planner designed to provide safe motions in the context of the Smart Autonomous Robot Assistant Surgeon (SARAS) surgical platform. SARAS is a multi-robot autonomous platform designed to execute auxiliary tasks in Minimally Invasive Surgeries (MIS) with a high degree of autonomy. The development of robotic systems with a high level of autonomy and reliability requires to perceive the workspace and human actions, to contextualize them with the surgical workflow, and, finally, plan and dynamically control the required motions. The autonomous control relies on a multi-level hierarchical Finite State Machine (hFSM) that decides and supervises all robot actions and their transitions. This approach requires multi-granularity decomposition of the surgical procedure and defines different motion profiles to preserve and safely interacts with the patients’ anatomy. The motion planner is developed under the minimally invasive surgery context since it is an extreme use case where the environment is complex, dynamic and unstructured. Moreover, in the SARAS platform the autonomous robots share workspace as well as collaborate with other human-guided robotic instruments. This creates an even more complex working environment and defines a set of hierarchical relationships in which auxiliary instruments have a lower priority. The presented motion planner acts at two levels: Global and Local. The Global Planner generates an initial spline-based trajectory that, defined by a set of Control Points, follows a certain profile determined by the ongoing surgical action and the interaction with the patient’s anatomy. Then, during the execution of the motion, the Local Planner observes the workspace (anatomy and other tools) and applies different virtual potential fields to the control points to dynamically modify their position to avoid potential collisions or tool blocking while maintaining trajectory coherence. At this level, it reactively modifies the trajectory between the tool position and the next control point applying Dynamical Systems based obstacle avoidance. This approach ensures collision free connections between the spline control points. The proposed motion planner is validated in a realistic surgical scenario. The experimental results are analysed from data collected during various Robotic-Assisted Radical Prostatectomy surgeries on manikins, performed with the SARAS SOLO-SURGERY platform: the main surgeon teleoperates a daVinci Research Kit and two robotic arms autonomously perform different auxiliary surgical tasks.

The ability of robots to navigate through doors is crucial for their effective operation in indoor environments. Consequently, extensive research has been conducted to develop robots capable of opening specific doors. However, the diverse combinations of door handles and opening directions necessitate a more versatile door opening system for robots to successfully operate in real-world environments. In this paper, we propose a mobile manipulator system that can autonomously open various doors without prior knowledge. By using convolutional neural networks, point cloud extraction techniques, and external force measurements during exploratory motion, we obtained information regarding handle types, poses, and door characteristics. Through two different approaches, adaptive position-force control and deep reinforcement learning, we successfully opened doors without precise trajectory or excessive external force. The adaptive position-force control method involves moving the end-effector in the direction of the door opening while responding compliantly to external forces, ensuring safety and manipulator workspace. Meanwhile, the deep reinforcement learning policy minimizes applied forces and eliminates unnecessary movements, enabling stable operation across doors with different poses and widths. The RL-based approach outperforms the adaptive position-force control method in terms of compensating for external forces, ensuring smooth motion, and achieving efficient speed. It reduces the maximum force required by 3.27 times and improves motion smoothness by 1.82 times. However, the non-learning-based adaptive position-force control method demonstrates more versatility in opening a wider range of doors, encompassing revolute doors with four distinct opening directions and varying widths.

This research presents a novel robot system that combines active and passive components to enhance compliance and dependability. The system is based on a continuous variable stiffness wrist. A wrist was created that met the requirements and a combination of active and passive control methods was suggested to insert and regulate forces effectively. The control strategy is based on the Cosserat rod model, with the fundamental concept being calculating the position and orientation of the component using data on the force exerted during contact between the parts and the stiffness of the contact between the shaft and hole components. This process converts the hard assembly into a flexible contact. Compliance is monitored via force and vision sensors, which allows for the shaft-hole assembly operation to be carried out even with attitude alignment problems, resulting in a notable decrease in the precision needed for component alignment. Initially, the camera supplies the first positional data of the shaft component for the robotic system. In addition, the performance of the wrist with variable stiffness is evaluated in terms of stiffness. Additionally, the calculation of relative deformation between components is examined using contact force information. Moreover, a robust active/passive hybrid insertion control technique, which relies on contact force, is proposed. Finally, the shaft-hole assembly task substantiates the necessity for contact force monitoring in the insertion assembly process. This control technique has demonstrated its efficacy in ensuring passive-compliant assembly performance. Furthermore, the variable stiffness wrist has been employed in robotic grinding for surfaces with curved contours to validate its effectiveness.

High-temperature industrial inspection has several challenges, especially if it is an autonomous inspection through mobile robots. This paper introduces the mobile robot CRAS (Climbing Robot for Advanced inSpection) for autonomous non-destructive testing (NDT) of weld beads from industrial super-duplex stainless steel vessels. It covers the design process, previous works, main challenges, and field testing. The main objective of the robot is to perform ultrasonic inspection over a heated separator tank while it operates. The metallic surfaces of the structure to be inspected are under constant high temperatures (80 °C–135 °C) when in operation. CRAS presents magnetic wheels as an adhesion method and a perception system able to identify and follow weld beads. The NDT method uses the phased-array ultrasonic technique. This paper approaches and proposes a solution for three challenges due to the high temperature: the loss of robot adhesion, ultrasound signal deformation, and the risk of damaging sensitive equipment such as sensors, cameras, and any electronic component. The CRAS adopted solutions are detailed and future steps of CRAS development are also addressed.

Accurate depth information is crucial for autonomous systems to navigate and interact safely with their surroundings. Passive stereo-vision cameras, such as the ZED 2i, obtain depth information through stereo-image analysis and triangulation. The study measures and assesses the true capabilities of the ZED 2i camera in a real indoor office environment. Furthermore, the study provides a standard test setup to reproduce similar benchmarks with different depth cameras. To achieve the set goals, an experiment was devised and carried out in an office environment to determine the camera depth error and Root Mean Square Error (RMSE) of the depth estimates at different distances using four different image resolutions. The results reveal that the depth error has heavy tails, implying that outliers substantially impact accuracy. Hence, depth errors should not be modeled as normally distributed errors. Moreover, only two out of four resolutions provided the capability of acquiring depth data up to 18 m. These insights provide guidelines for understanding the ZED 2i camera's true capabilities, determining its suitability for different applications and environments, and giving baselines for future tests of other competing sensor units. Furthermore, the study offers a simple, inexpensive, and laboratory space-free, yet effective setup that does not need extensive equipment or complex configurations to facilitate the benchmarking of depth cameras in different working environments.

Multi-robot collaboration has become a needed component in unknown environment exploration due to its ability to accomplish various challenging situations. Potential-field-based methods are widely used for autonomous exploration because of their high efficiency and low travel cost. However, exploration speed and collaboration ability are still challenging topics. Therefore, we propose a Distributed Multi-Robot Potential-Field-Based Exploration (DMPF-Explore). In particular, we first present a Distributed Submap-Based Multi-Robot Collaborative Mapping Method (DSMC-Map), which can efficiently estimate the robot trajectories and construct the global map by merging the local maps from each robot. Second, we introduce a Potential-Field-Based Exploration Strategy Augmented with Modified Wave-Front Distance and Colored Noises (MWF-CN), in which the accessible frontier neighborhood is extended, and the colored noise provokes the enhancement of exploration performance. The proposed exploration method is deployed for simulation and real-world scenarios. The results show that our approach outperforms the existing ones regarding exploration speed and collaboration ability.

This article presents GLIM, a 3D range-inertial localization and mapping framework with GPU-accelerated scan matching factors. The odometry estimation module of GLIM employs a combination of fixed-lag smoothing and keyframe-based point cloud matching that makes it possible to deal with a few seconds of completely degenerated range data while efficiently reducing trajectory estimation drift. It also incorporates multi-camera visual feature constraints in a tightly coupled way to further improve the stability and accuracy. The global trajectory optimization module directly minimizes the registration errors between submaps over the entire map. This approach enables us to accurately constrain the relative pose between submaps with a small overlap. Although both the odometry estimation and global trajectory optimization algorithms require much more computation than existing methods, we show that they can be run in real-time due to the careful design of the registration error evaluation algorithm and the entire system to fully leverage GPU parallel processing.