This paper studies the problem of bearing-only formation control of multi-agent systems, where the control of each agent merely relies on the relative bearings to its neighbors. Although this problem has received increasing research attention recently, it is still unsolved to a large extent due to its highly nonlinear dynamics. In particular, the existing control approaches are only able to solve the simplest scenario where the target formation is stationary and each agent is modeled as a single integrator. The main contribution of this paper is to propose new bearing-only formation control laws to (i) track moving target formations and (ii) handle a variety of agent models including single-integrator, double-integrator, and unicycle models. These control laws are an important step towards the application of bearing-only formation control in practical tasks. Both numerical simulation and real experimental results are presented to verify the effectiveness of the theoretical results.

Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moments) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. Still, such set of solutions suffers from slowness, especially for the complex models, hindering the utility in real-time applications. In recent years, data-driven methods have emerged as a promising alternative due to the benefits in speedy and simple implementation, but they cannot reflect the underlying neuromechanical processes. This paper proposes a physics-informed deep learning framework for musculoskeletal modelling, where physics-based domain knowledge is brought into the data-driven model as soft constraints to penalise/regularise the data-driven model. We use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. Simultaneously, the physics law between muscle forces and joint kinematics is used the soft constraint. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The experimental results demonstrate the effectiveness and robustness of the proposed framework.

This note considers the distributed optimization problem on directed graphs with nonconvex local objective functions and the unknown network connectivity. A new adaptive algorithm is proposed to minimize a differentiable global objective function. By introducing dynamic coupling gains and updating the coupling gains using relative information of system states, the nonconvexity of local objective functions, unknown network connectivity, and the uncertain dynamics caused by locally Lipschitz gradients are tackled concurrently. Consequently, the global asymptotic convergence is established when the global objective function is strongly convex and the gradients of local objective functions are only locally Lipschitz. When the communication graph is strongly connected and weight-balanced, the algorithm is independent of any global information. Then, the algorithm is naturally extended to unbalanced directed graphs by using the left eigenvector of the Laplacian matrix associated with the zero eigenvalue. Several numerical simulations are presented to verify the results.

EMG-based continuous wrist joint motion estimation has been identified as a promising technique with huge potential in assistive robots. Conventional data-driven model-free methods tend to establish the relationship between the EMG signal and wrist motion using machine learning or deep learning techniques, but cannot interpret the functional relationship between neuro-commands and relevant joint motion. In this paper, an EMG-driven musculoskeletal model is proposed to estimate continuous wrist joint motion. This model interprets the muscle activation levels from EMG signals. A muscle-tendon model is developed to compute the muscle force during the voluntary flexion/extension movement, and a joint kinematic model is established to estimate the continuous wrist motion. To optimize the subject-specific physiological parameters, a genetic algorithm is designed to minimize the differences of joint motion prediction from the musculoskeletal model and joint motion measurement using motion data during training. Results show that mean root-mean-square-errors are 10.08°, 10.33°, 13.22° and 17.59° for single flexion/extension, continuous cycle and random motion trials, respectively. The mean coefficient of determination is over 0.9 for all the motion trials. The proposed EMG-driven model provides an accurate tracking performance based on user’s intention.

This article considers the bearing-only formation control problem, where the control of each agent only relies on relative bearings of their neighbors. A new control law is proposed to achieve target formations in finite time. Different from the existing results, the control law is based on a time-varying scaling gain. Hence, the convergence time can be arbitrarily chosen by users, and the derivative of the control input is continuous. Furthermore, sufficient conditions are given to guarantee almost global convergence and interagent collision avoidance. Then, a leader-follower control structure is proposed to achieve global convergence. By exploring the properties of the bearing Laplacian matrix, the collision avoidance and smooth control input are preserved. A multirobot hardware platform is designed to validate the theoretical results. Both simulation and experimental results demonstrate the effectiveness of our design.