Robotics and Autonomous Systems最新文献

Small-scale jumping robots widely employ the pause-and-leap locomotion strategy. They use elastic elements to enhance the jumping performance, which is promising for locomotion over rugged terrain. However, these robots typically lose a significant amount of mechanical energy during landing, which is initially accumulated for takeoff, resulting in wasted energy. Here, we propose a landing strategy that uses a jumping mechanism with controlled mono-stable or bi-stable characteristics to achieve the energy recoverable landing. By adjusting the jumping mechanism to an appropriate bi-stable state before landing, the robot’s extended leg retracts to its pre-jump configuration upon touchdown, enabling the recapture of mechanical energy within the springs. We develop analytical models for the touchdown collision and landing dynamics. A 165 g robot prototype is constructed, featuring integrated sensing, actuation, and computations. Both simulations and experiments are conducted to explore the effects of various factors on the landing behavior. Experiments demonstrate successful landings with energy recovery ratio exceeding 50% across different landing trajectories. This landing strategy holds significant potential for enhancing the locomotion efficiency of future small-scale jumping robots.

The importance of electrical energy in human life has grown considerably, resulting in a great surge in demand for this energy form. As a consequence, numerous power transmission lines are continuously under construction. Periodic inspections and maintenance of equipment and accessories associated with these lines are crucial to ensuring their proper functioning. Thus, this paper introduces a novel mobile robot designed to remove debris from high-voltage power lines up to 138 kV. This robot offers several advantages, including reduced costs related to maintenance operations that require specialized human labor, increased task efficiency, and reduced risks to human physical integrity by eliminating the need for manual tasks in direct contact with high-voltage lines. This paper outlines the development and validation of a novel robot that can be integrated with a drone for transportation, bringing an innovation to the format for carrying out maintenance in the electric power distribution system. After reviewing robots applied to debris removal, our study presents the novel robot's design and control system. The robot was developed and tested in both simulated and real environments, proving to be efficient and cost-effective in improving debris removal from high-voltage lines while ensuring linemen's safety.

This paper proposes a method for the deployment of a multi-agent system of unmanned aerial vehicles (UAVs) as a shield with potential applications in the protection of infrastructures. The shield shape is modeled as a quadric surface in the 3D space. To design the desired formation (target distances between agents and interconnections), an algorithm is proposed where the input parameters are just the parametrization of the quadric and the number of agents of the system. This algorithm guarantees that the agents are almost uniformly distributed over the virtual surface and that the topology is a Delaunay triangulation. Moreover, a new method is proposed to check if the resulting triangulation meets that condition and is executed locally. Because this topology ensures that the formation is rigid, a distributed control law based on the gradient of a potential function is proposed to acquire the desired shield shape and proofs of stability are provided. Finally, simulation and experimental results illustrate the effectiveness of the proposed approach.

Recently, a repetitive motion planning (RMP) scheme with guaranteed precision has been developed for redundant robot manipulators. However, the RMP scheme does not consider joint constrains. As a result, such a scheme may not perform effectively when the joint configuration exceeds its physical limit. In this paper, we provide a further study by proposing a new discrete-time RMP (DTRMP) scheme based on the pseudoinverse formulation for redundant robot manipulators with joint constrains. Specifically, by using the direct derivation and by introducing the feedback, the handling of joint physical limit is formulated as an implicit dynamic equation. Then, the combined kinematic equation of the constrained redundant robot manipulators is obtained, and the resultant continuous-time RMP (CTRMP) scheme with the pseudoinverse-based formulation is established. By utilizing a numerical difference rule to discretize the CTRMP scheme, the new DTRMP scheme is thus developed for redundant robot manipulators with joint constrains. Theoretical analysis and computer simulations under a constrained five-link robot manipulator validate the effectiveness and superiority of the proposed DTRMP scheme. The experiment results of implementing the proposed scheme on the constrained practical Panda robot manipulator further indicate the scheme applicability and feasibility.

Robots and humans coexist in various social environments. In these contexts, robots predominantly serve as assistants, necessitating communication and understanding capabilities. This paper introduces a biologically inspired model grounded on neuroendocrine substances that facilitate the development of social bonds between robots and individuals. The model simulates the effects of oxytocin, arginine vasopressin, and dopamine on social behavior, acting as modulators for bonding in the interaction between the social robot Mini and its users. Neuroendocrine levels vary in response to circadian rhythms and social stimuli perceived by the robot. If users express care for the robot, a positive bond is established, enhancing human–robot interaction by prompting the robot to engage in cooperative actions such as playing or communicating more frequently. Conversely, mistreating the robot leads to a deterioration of the relationship, causing user rejection. An experimenter-robot interaction scenario illustrates the model’s adaptive mechanisms involving three types of profiles: Friendly, Aversive, and Naive. Besides, a user study with 22 participants was conducted to analyze the differences in Attachment, Social Presence, perceived Anthropomorphism, Likability, and User Experience between a robot randomly selecting its behavior and a robot behaving using the bioinspired pair-bonded method proposed in this contribution. The results show how the pair-bonding with the user regulates the robot’s social behavior in response to user actions. The user study reveals statistical differences favoring the robot using the pair-bonding regulation in Attachment and Social Presence. A qualitative study using an interview-like form suggests the positive effects of creating bonds with bioinspired robots.

Tactile sensing plays an irreplaceable role in robotic material recognition. It enables robots to distinguish material properties such as their local geometry and textures, especially for materials like textiles. However, most tactile recognition methods can only classify known materials that have been touched and trained with tactile data, yet cannot classify unknown materials that are not trained with tactile data. To solve this problem, we propose a tactile Zero-Shot Learning framework to recognise materials when they are touched for the first time, using their visual and semantic information, without requiring tactile training samples. The biggest challenge in tactile Zero-Shot Learning is to recognise disjoint classes between training and test materials, i.e., the test materials that are not among the training ones. To bridge this gap, the visual modality, providing tactile cues from sight, and semantic attributes, giving high-level characteristics, are combined together and act as a link to expose the model to these disjoint classes. Specifically, a generative model is learnt to synthesise tactile features according to corresponding visual images and semantic embeddings, and then a classifier can be trained using the synthesised tactile features for zero-shot recognition. Extensive experiments demonstrate that our proposed multimodal generative model can achieve a high recognition accuracy of 83.06% in classifying materials that were not touched before. The robotic experiment demo and the FabricVST dataset are available at https://sites.google.com/view/multimodalzsl.

Combining camera, IMU and wheel encoder is a wise choice for car positioning because of the low cost and complementarity of the sensors. We propose a novel extended visual-inertial odometry algorithm based on sliding window tightly fusing data from the above three sensors. Firstly we propose an IMU-odometer pre-integration approach utilizing complete IMU measurements and wheel encoder readings, to make scale estimation more accurate in subsequent 4-degrees of freedom (DoF) optimization. Secondly we develop an original initialization module where encoder readings are fully utilized to refine gravity direction and provide an initial value for camera pose in real scale. Thirdly, we design a computationally efficient online extrinsic calibration method by fixing the linearization point for the rotational component of IMU-odometer extrinsic parameters, which is deployed depending on the convergence of accelerometer bias. Fourthly, we give an observability analysis of our optimization based approach under more general assumption. Extensive experiments are performed on two sets of data in various scenes, bringing the state-of-the-art visual odometry and visual-inertial odometry algorithms into comparison. Experimental results prove the overwhelmingly better performance of our proposed approach on the above two sets of data, as well as the robustness of our initialization module and the improvement resulted from online extrinsic calibration.

This study aims to design a test platform for quadcopters, which allows the execution of all rotational movements and prevents translational movements without affecting the dynamics of the system. The methodological approach involves both simulation and the construction of the test platform. Two simulators are developed: (i) a linear simulator, used to assist in determining control parameters, and (ii) a nonlinear simulator, used to model the nonlinearity inherent to the rotational behavior of aircraft. In addition, the control system for the quadcopter is implemented, utilizing proportional, integral, and derivative control principles. By conducting seven experiments on the test platform and in the nonlinear simulator, the obtained results are compared in order to validate the proposed methodology. The mean discrepancy observed between the mean absolute difference obtained by the test platform and by the nonlinear simulator for the angle $\varphi$ was $0.85°$, for the angle $\theta$ was $2.77°$, and for the angle $\psi$ was $4.66°$. When analyzed separately, the mean absolute errors for the angles, using the nonlinear simulator and the test platform, showed differences below 2% in almost all evaluated experiments. The developed test platform preserves the rotational dynamics of the quadcopter as desired, closely approaching the results obtained by the nonlinear simulator. Consequently, this platform can be used to carry out practical tests in a controlled environment.

The low-texture, shape-similar, interconnected and mutual-occlusion nature of truss members poses challenges for simultaneous localization and mapping of biped climbing robots in truss environments. In this paper, we propose BiCR-SLAM, a multi-source fusion SLAM system, to estimate both the distinctive state of the robot and a parametric representation of the truss, going beyond traditional point cloud mapping. The proposed system comprises four modules such as encoder dead reckoning, LiDAR odometry, pole landmark mapping, and global optimization. To address the intricacies of truss environments, we present a pole landmark mapping module with dedicated operations including pole detection, data association, and parameterizations. In the back-end, we formulate the localization problem of biped climbing robots using a multi-source factor graph, encompassing factors including forward kinematics, LiDAR odometry, gripping, and points of poles. Experiments are conducted to evaluate the impact of various factors and to validate the effectiveness and accuracy of the proposed BiCR-SLAM system. A handheld LiDAR experiment in an outdoor large-scale truss environment demonstrates the generalization of our proposed approach.

To increase the efficiency of road transport and reduce exhaust emissions, research has been intensified in assisted control systems, where adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) stand out. While ACC uses data from onboard sensors and radars, CACC also uses data from inter-vehicle wireless communication. In this context, this paper proposes a new control law for ACC systems that provides a competitive performance compared to CACC, despite not requiring wireless communication between vehicles. The new control law formulation employs both the vehicle acceleration and the relative speed, which generalizes similar ones. The design methodology formulated as a convex optimization problem ensures string and internal stability as well closed-loop pole placement if a set of linear matrix inequalities (LMIs) is satisfied. At the end of the paper, simulations demonstrate the effectiveness of the proposed methodology.