IET Cybersystems and Robotics最新文献

An artificial potential field method based on global path guidance (G-APF) is proposed for target unreachability and local minima problems of the conventional artificial potential field (APF) method in complex environments with dynamic obstacles. First, for the target unreachability problem, the global path attraction is added to the APF; second, an obstacle detection optimisation method is proposed and the optimal virtual target point is selected by setting the evaluation function to improve the local minima problem; finally, based on the obstacle detection optimisation method, the gravitational and repulsive processes are improved so that the path can pass through the narrow channel smoothly and remain collision-free. Experiments show that the method optimises 40.8% of the total path corners, reduces 81.8% of the number of path oscillations, and shortens 4.3% of the path length in Map 1. It can be applied to the vehicle obstacle avoidance path planning problem in complex environments with dynamic obstacles.

Simultaneous localisation and mapping (SLAM) are the basis for many robotic applications. As the front end of SLAM, visual odometry is mainly used to estimate camera pose. In dynamic scenes, classical methods are deteriorated by dynamic objects and cannot achieve satisfactory results. In order to improve the robustness of visual odometry in dynamic scenes, this paper proposed a dynamic region detection method based on RGB-D images. Firstly, all feature points on the RGB image are classified as dynamic and static using a triangle constraint and the epipolar geometric constraint successively. Meanwhile, the depth image is clustered using the K-Means method. The classified feature points are mapped to the clustered depth image, and a dynamic or static label is assigned to each cluster according to the number of dynamic feature points. Subsequently, a dynamic region mask for the RGB image is generated based on the dynamic clusters in the depth image, and the feature points covered by the mask are all removed. The remaining static feature points are applied to estimate the camera pose. Finally, some experimental results are provided to demonstrate the feasibility and performance.

Precise localisation and navigation are the two most important tasks for mobile robots. Visual simultaneous localisation and mapping (VSLAM) is useful in localisation systems of mobile robots. The wide-angle camera has a broad field of vision and more abundant information on images, so it is widely used in mobile robots, including legged robots. However, wide-angle cameras are more complicated than ordinary cameras in the design of visual localisation systems, and higher requirements and challenges are put forward for VSLAM technologies based on wide-angle cameras. In order to resolve the problem of distortion in wide-angle images and improve the accuracy of localisation, a sampling VSLAM based on a wide-angle camera model for legged mobile robots is proposed. For the predictability of the periodic motion of a legged robot, in the method, the images are sampled periodically, image blocks with clear texture are selected and the image details are enhanced to extract the feature points on the image. Then, the feature points of the blocks are extracted and by using the feature points of the blocks in the images, the feature points on the images are extracted. Finally, the points on the incident light through the normalised plane are selected as the template points; the relationship between the template points and the images is established through the wide-angle camera model, and the pixel coordinates of the template points in the images and the descriptors are calculated. Moreover, many experiments are conducted on the TUM datasets with a quadruped robot . The experimental results show that the trajectory error and translation error measured by the proposed method are reduced compared with the VINS-MONO, ORB-SLAM3 and Periodic SLAM systems.

Squatting is a basic movement of bipedal robots, which is essential in robotic actions like jumping or picking up objects. Due to the intrinsic complex dynamics of bipedal robots, perfect squatting motion requires high-performance motion planning and control algorithms. The standard academic solution combines model predictive control (MPC) with whole-body control (WBC), which is usually computationally expensive and difficult to implement on practical robots with limited computing resources. The real-time kinematic prediction (RKP) method is proposed, which considers upcoming reference motion trajectories and combines it with quadratic programming (QP)-based WBC. Since the WBC handles the full robot dynamics and various constraints, the RKP only needs to adopt the linear kinematics in the robot's task space and to softly constrain the desired accelerations. Then, the computational cost of derived closed-form RKP is greatly reduced. The RKP method is verified in simulation on a heavy-loaded bipedal robot. The robot makes rapid and large-amplitude squatting motions, which require close-to-limit torque outputs. Compared with the conventional QP-based WBC method, the proposed method exhibits high adaptability to rough planning, which implies much less user interference in the robot's motion planning. Furthermore, like the MPC, the proposed method can prepare for upcoming motions in advance but requires much less computation time.

Unmanned robotic systems are expected to liberate people from heavy, monotonous, and dangerous work. However, it is still difficult for robots to work in complicated environments and handle diverse tasks. To this end, a robotic system with four legs, four wheels, and a reconfigurable arm is designed. Special attention has been given to making the robot compact, agile, and versatile. Firstly, by using separate wheels and legs, it removes the coupling in the traditional wheeled–legged system and guarantees highly efficient locomotion in both the wheeled and legged modes. Secondly, a leg–arm reconfiguration design is adopted to extend the manipulation capability of the system, which not only reduces the total weight but also allows for dexterous manipulation and multi-limb cooperation. Thirdly, a multi-task control strategy based on variable configurations is proposed for the system, which greatly enhances the adaptability of the robot to complicated environments. Experimental results are given, which validate the effectiveness of the system in mobility and operation capability.

A systematic solution is developed to improve the autonomous capability of the quadruped robot with a manipulator, such as navigation, recognition and operation. The developed system adopts novel software, hardware system and system architecture, including a specially designed environment awareness system (EAS). Based on the camera and LiDAR on the EAS, the recognition of multiple common targets, such as the leader, door, window and bag, is achieved. In terms of navigation, a location method is built, that combines the laser odometer and global positioning system. A mapping and path planning module is designed by the Robot-centric Elevation Mapping algorithm and the rapidly exploring rand tree algorithm. For operation, a real-time target grasp detection system is proposed based on the You Only Look Once v5 algorithm to improve the success rate of tasks. The whole system is integrated based on the task relevance scheduling strategy to reduce the computational complexity. The tightly integrated system and the subsystems are evaluated by conducting simulations and physical experiments in robot recognition, navigation and operation. Extensive experiments show that the proposed framework can better achieve the autonomous navigation and operation of the quadruped robot with a manipulator. Notably, the proposed framework is still effective when facing dynamic objects. In addition, the system can be easily extended to other forms of mobile robot.

Walking stability is one of the key issues for humanoid robots. A self-stabilised walking gait for a full dynamic model of humanoid robots is proposed. For simplified models, that is, the linear inverted pendulum model and variable-length inverted pendulum model, self-stabilisation of walking gait can be obtained if virtual constraints are properly defined. This result is extended to the full dynamic model of humanoid robots by using an essential dynamic model, which is developed based on the zero dynamics concept. With the proposed method, a robust stable walking for a humanoid robot is achieved by adjusting the step timing and landing position of the swing foot automatically, following its intrinsic dynamic characteristics. This exempts the robot from the time-consuming high-level control approaches, especially when a full dynamic model is applied. How different walking patterns/features (i.e., the swing foot motion, the vertical centre of mass motion, the switching manifold configuration, etc.) affect the stability of the walking gait is analysed. Simulations are conducted on robots Romeo and TALOS to support the results.

Keeping balance in movement is an important premise for biped robots to complete various tasks. Now, the balance control of biped robots mainly depends on the cooperation of various joints of the robot's body. When robots move faster, the adjustment allowance of joints is reduced, and the robot's anti-disturbance ability will inevitably decline. To solve this problem, the control moment gyroscope (CMG) is creatively used as an auxiliary stabilisation device for fully actuated biped robots and the CMG assistance strategy, which can be integrated into the biped's balance control framework, is proposed. This strategy includes model predictive control module, distribution module, and CMG precession controller. Under the command of it, CMGs can effectively assist the robot in resisting impact and returning to initial positions in time. The results of anti-impact simulation on the walking and running biped robot prove that, with the help of CMGs, the robot's ability to resist disturbance and remain stable is significantly improved.

The cover image is based on the Original Article Disturbance rejection for biped robots during walking and running using control moment gyroscopes by Haochen Xu et al., https://doi.org/10.1049/csy2.12070.

The position control problem of differential-driven automated guided vehicles (AGVs) based on the prescribed-time control method is studied. First, an innovative orientation error function is proposed by an auxiliary arcsine function about the orientation angle. Thus, the problem of position control of AGV is transformed into the stabilisation control of the kinematic system. Second, by introducing a reserved time parameter and a smooth switching function, a novel time-varying scaling function is proposed. This novel scaling function avoids the risk of infinite gain in the conventional prescribed-time control method while ensuring the smoothness of control laws. Then, an improved velocity constraint function is proposed using the Gaussian function. Compared with the existing constraint function, the proposed method not only has more smoothness but also solves the balance point errors caused by the large AGV orientation errors. The presented method ensures that the AGV reaches the target position in a prescribed time. Hence, the upper bound of the AGV system state can be determined by adjusting parameters. Matlab simulation results show that the proposed controller can effectively make the AGV system state satisfy the prescribed bound.