This article studies the moving-target enclosing control problem for a group of mobile agents, under the assumption that the amplitude of the target's velocity is unknown a priori . The target's velocity is time-varying and can be viewed as a signal generated by an exogenous linear system. The neighbor topology of the agents is determined by their positions relative to the target at each time instant. Based on the relative positions, a distributed observer is proposed for the agents to cooperatively estimate the velocity of the target. Then, a novel distributed control law is proposed based on the obtained estimates. It is shown that under the proposed control law, the agents eventually move along a common circle of the desired radius around the moving target and achieve any desired spaced pattern along the circle. Furthermore, conditions are derived under which the collision between each agent and the target is avoided, and the interagent collision avoidance is also guaranteed by preserving the counterclockwise order of the agents around the target. Finally, a simulation example illustrates the effectiveness of the proposed control law.
Motion planning for quadrupedal robots on unstructured terrains demands the consideration of torso terrain adaptation and safety foothold placement. This paper presents an autonomous and safety stair climbing control strategy, aiming to simultaneously optimize body and leg movements. An improved mobility metric taking into account leg mobility and maintaining stability margin, is introduced. By penalizing deviations from the current joint angle and nominal leg configuration in the cost function, the stance legs can naturally extend to lifting body, and the projection of the center of mass is always inside the support region. Then, the reference climbing velocity is automatically determined based on the geometric information of the stairs. With these reference velocities, we solve a two-layer control framework which couples nonlinear model predictive control (MPC) with whole-body control (WBC), and add several different control barrier functions (CBF) constraints to ensure safety foot placement and edge avoidance. Note that each constrained act on the kinematics or dynamics level depends on the order of the system dynamics. In the simulation, we demonstrate that the robot efficiently climbs several different size stairs with 10 cm height (25% of the maximum leg length) and the maximum climbing velocity reaches 0.64m/s.
Autonomous driving can enhance travel efficiency and reduce traffic accidents. An essential aspect of autonomous driving research is the identification of obstacles in front of the vehicle. Using vision technology to identify obstacles in front of the vehicle has become a hot topic in current research. Convolutional neural networks (CNN) can train end-to-end detection models with a large amount of labeled obstacle image data. However, previous CNN-based obstacle detection models did not account for the differences between various channels in the images, which led to the neglect of critical information regarding obstacles. Additionally, in the real world, obstacles typically come in different scales, and traditional CNN networks extract image features at a single scale, making it difficult to handle obstacles of varying scales. Therefore, in this work, we propose a multi-scale model based on Squeeze and Excitation network (MSMSE) to classify obstacles in front of the vehicle in autonomous driving scenarios. The MSMSE model draws on the ideas of Squeeze and Excitation network and multi-scale feature fusion to address the shortcomings of previous CNN-based obstacle detection models. Specifically, the MSMSE model first employs the Squeeze and Excitation operation to add a channel attention mechanism to obstacle images. This operation allows for better differentiation of obstacle features, thereby enhancing the generalization ability of the MSMSE model and making it more robust. Finally, the MSMSE model integrates image features from different levels to increase the diversity and richness of the image features. This enables the MSMSE model to better learn both global and local features of obstacle images. Experimental results show that the MSMSE model has significant advantages in performance compared with mainstream models such as ViT.
In this article, the distributed leader-following formation control problem of networked mobile robots is investigated. The desired formation is specified by a reference trajectory generated by the leader and the followers' desired relative positions with respect to the leader. On the one hand, for any security-aware multirobot systems, the values of the leader's position and orientation are generally not allowed to be transmitted via the inter-robot communication in the case of the information leakage of formation caused by the possible eavesdropping. On the other hand, relative orientations, different from relative positions, are typically difficult for mobile robots to measure directly, which makes the reference orientation unknown to all followers. In order to track the reference trajectory and form a desired formation in the absence of the reference orientation, followers are divided into two groups according to whether they are able to directly measure the relative positions with respect to the leader. The topology of the sensing/communication network among multirobot systems is described by a directed graph containing a directed spanning tree. Then, two observer-based control laws are proposed for two groups of followers, respectively, in both of which the unknown tracking errors are properly estimated. It is rigorously proven that the resulting closed-loop multirobot system is globally uniformly asymptotically stable. Finally, the effectiveness of our approach is illustrated by an experiment conducted on networked TurtleBot3 Burger mobile robots.
This paper explores the problem of distributed circular formation control for networked dynamic nonholonomic vehicles. The topology among the networked vehicles is described by a directed graph. A backstepping-based distributed control law, which requires each vehicle to use local measurement and communication, is proposed for the kinematic and dynamic models of nonholonomic vehicles. The study demonstrates that all vehicles can achieve global convergence to a predetermined circular trajectory while revolving around the target. Furthermore, they can also globally converge to a desired formation with appropriate spacing along the circular path. Finally, a simulation example is presented to illustrate the result.
In this paper, a computer aided breast reconstruction surgery planning method is proposed, computing the breast shape after excision of one for some diseases such as cancer. In order to achieve a reasonable result, we calculate shape, area, volume and depth of the skin and muscle for the reconstruction, based on another wholesome breast. The solution is described as follows: firstly, the breast's MRI data of patient is input; then, the region of interest is obtained from healthy breast employing balloon segmentation algorithm and retrieve surface mesh data; thirdly, the dimensional surface skin mesh is mapped onto the plane, in order to attain the shape and volume of the flap for breast reconstruction, by the help of deformable model; finally the approximate curve volume shape of flap is calculated. Other contributing methods such as mesh smoothing and cutting of triangulated surface are also discussed. The doctors validation and evaluation process are also provided to ensure the robust and stable result of virtual surgery planning.
This paper investigates the bearing-based formation manoeuvring control problem of multi-agent systems with constant communication delays. Leaders move with time-varying velocities, and followers aim to track leaders' motion and form a bearing-defined moving desired formation. A dynamic controller for each follower is developed based on its position, velocity, its neighbours' position, velocity, acceleration. The motion information of neighbours is obtained via communication, and communication delays are taken into account. Then a rigorous proof is provided to show that tracking errors of the neutral-type system are globally uniformly ultimately bounded. Finally, simulation results are presented to illustrate the effectiveness.
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