Multirobot path planning leads multiple robots from start positions to designated goal positions by generating efficient and collision-free paths. Multirobot systems realize coordination solutions and decentralized path planning, which is essential for large-scale systems. The state-of-the-art decentralized methods utilize imitation learning and reinforcement learning methods to teach fully decentralized policies, dramatically improving their performance. However, these methods cannot enable robots to perform tasks efficiently in relatively dense environments without communication between robots. We introduce the transformer structure into policy neural networks for the first time, dramatically enhancing the ability of policy neural networks to extract features that facilitate collaboration between robots. It mainly focuses on improving the performance of policies in relatively dense multirobot environments under conditions where robots do not communicate with each other. Furthermore, a novel imitation reinforcement learning framework is proposed by combining contrastive learning and double deep Q-network to solve the problem of difficulty training policy neural networks after introducing the transformer structure. We present results in the simulation environment and compare the resulting policy against advanced multirobot path-planning methods in terms of success rate. Simulation results show that our policy achieves state-of-the-art performance when there is no communication between robots. Finally, we experimented with a real-world case using a total of three robots in our robotic laboratory.

Most existing formation control approaches are based on the assumption that the global/relative position and/or velocity measurements of mobile robots are directly available. To extend the application domain and to improve the formation control performance, it is extremely necessary to avoid the use of position and velocity measurements in the design of formation controllers. In this paper, we propose new leader-following formation tracking control schemes for nonholonomic mobile robots with onboard perspective cameras, without using both position and velocity measurements. To address the unavailability issue of position measurements, the leader–follower kinematics model in the image space is developed, which can facilitate the complete elimination of measurement/estimation of the position information. Furthermore, feedback information from the perspective camera of the follower robot is used to design adaptive observers to estimate the leader linear velocity for feedforward compensation, which can handle the absence of velocity measurements such that the proposed schemes can be applied to control formations of mobile robots without mutual communication abilities. By using the Lyapunov stability theory, a rigorous stability analysis based on the nonlinear formation dynamics is provided to show that the global stability of the combined observer–controller closed-loop system can be guaranteed. Both simulation and experimental results are also given to demonstrate the performance of the proposed formation tracking control schemes.

Most existing formation control approaches assume that accurate global or local position measurements of the robots are directly available, without giving details about how to obtain these measurements, or only providing Kalman filter-type estimators to get them without considering effects of the estimation on the closed-loop system stability. Hence, developing formation controllers with position estimators that can guarantee overall closed-loop system stability becomes highly desirable. This technical note presents a new formation tracking controller for the nonholonomic mobile robots without using direct position measurements. To deal with the absence of accurate position measurements, feedback information from a perspective camera, the odometry and Attitude and Heading Reference System (AHRS) sensors is used to design an observer to provide online estimates of the relative position of the follower with respect to the leader. Using Lyapunov stability analysis, we show that the combined observer-controller closed-loop system is stable, and both the formation tracking errors and the relative position estimation errors asymptotically converge to zero. The performance of the proposed scheme is illustrated through experimental results.

Lesions in the maxillary sinus area are usually treated with endoscopic interventional surgery. However, target lesions are often hardly accessible with conventional surgical tools. A new continuum module is proposed with a bending manipulator to perform maxillary sinus surgery. The developed manipulator has a NiTiNol tube embedded in its continuum module. It has a diameter of 4 mm and can be bent up to 270 degrees to reach the target position flexibly. In this paper, a kinematic model of the continuum module is presented. An external loading analysis of the robot was formulated to analyze payload capacity and tensions, taking into account friction, actuation, elastic effects and external loadings. Some experiments were carried out to validate the kinematics and to test the robot capabilities of scissoring, payload and manipulation.

In this paper, an image-based impedance control strategy for force tracking of an unmanned aerial manipulator (UAM) is presented. Firstly, image features with nice decoupling characteristics are designed and the relationship between the camera motion and the image features is derived. Then, a two-stage strategy is proposed to achieve force tracking of the UAM on a planar object in an arbitrary pose. The first stage drives the end-effector perpendicular to the object's planer surface by pure visual servoing. To achieve force tracking under the visual guidance, an adaptive visual impedance control method which adjusts the target stiffness according to the force tracking error and the visual feature error is proposed in the second stage. The closed-loop system is proved asymptotically stable by means of Lyapunov analysis. Further, the stability in free flight phase of the stage two is also analyzed and ensured. Finally, experiments were carried out including a whiteboard cleaning task in different poses. The experimental results illustrate the validity and effectiveness of the proposed approach.

Long-Term visual localization under changing environments is a challenging problem in autonomous driving and mobile robotics due to season, illumination variance, etc . Image retrieval for localization is an efficient and effective solution to the problem. In this paper, we propose a novel multi-task architecture to fuse the geometric and semantic information into the multi-scale latent embedding representation for visual place recognition. To use the high-quality ground truths without any human effort, the effective multi-scale feature discriminator is proposed for adversarial training to achieve the domain adaptation from synthetic virtual KITTI dataset to real-world KITTI dataset. The proposed approach is validated on the Extended CMU-Seasons dataset and Oxford RobotCar dataset through a series of crucial comparison experiments, where our performance outperforms state-of-the-art baselines for retrieval-based localization and large-scale place recognition under the challenging environment.