This paper proposes and evaluates swarming mechanisms of patrolling unmanned aerial vehicles (UAVs) that can collectively search a region for intruding UAVs. The main contributions include the development of multi-objective searching strategies and investigation of the required sensor configurations for the patrolling UAVs. Numerical results reveal that it is sometimes better to search through a region with a single swarm rather than multiple swarms deployed over sub-regions. Moreover, a large communication range does not necessarily improve search performances, and the patrolling swarm must have a speed close to the speed of the intruding UAVs to maximize the search performances.

This paper addresses the motion planning and control problem of a system of 1-trailer robots navigating a dynamic environment cluttered with obstacles including a swarm of boids. A set of nonlinear continuous control laws is proposed via the Lyapunov-based Control Scheme for collision, obstacle, and swarm avoidances. Additionally, a leader–follower strategy is utilized to allow the flock to split and rejoin when approaching obstacles. The effectiveness of the control laws is demonstrated through numerical simulations, which show the split and rejoin maneuvers by the flock when avoiding obstacles while the swarm exhibits emergent behaviors.

This paper investigates the dynamics of an underwater vehicle-manipulator system (UVMS) consisting of a two-link flexible-joint manipulator affixed to an autonomous underwater vehicle. The quasi-Lagrange formulation is utilized in deriving a realistic mathematical model of the UVMS considering joints’ friction, hysteretic coupling between the joints and links, and the nonlinear hydrodynamic forces acting on the system, such as added mass, viscous damping, buoyancy, drag, and vortex-induced forces. Numerical simulations are performed to demonstrate the effects of hydrodynamic forces and system coupling between the vehicle and the manipulator and the joints and the links on the precise positioning of the end effector.

In this article, we propose a nonlinear Proportional+Derivative (PD) tracking controller with adaptive Fourier series compensation. The proposed controller uses a regressor-free adaptive scheme that relies on a trigonometric polynomial with varying coefficients to solve the control problem. Asymptotic convergence of the position and velocity errors is proven via a formal stability analysis based on Lyapunov and LaSalle theory for discontinuous systems. The proposed controller is validated on a 2-degrees of freedom robot manipulator. The experimental results validate the theoretically obtained results and reflect the effect of certain parameters in the transient behavior of the error dynamics. Certain robustness properties are also observed.

In the current research, kinematic analysis of a humanoid NAO is attempted. Here, both Denavit–Hartenberg (DH) parameter approach and multibody formulation approach have been analyzed. In the DH parameter approach, the NAO robot is solved by separating it into five individual kinematic chains. In the multibody formulation approach, NAO is divided into 15 segments, and each segment is analyzed. Kinematic analysis holds a significant importance; as from the data obtained in the kinematic analysis, the robots can be designed for real-time path planning and navigation. The current analysis is a novel approach to analyze the NAO based on its kinematic constraints.

This paper considers the task allocation problem under the requirement that the assignments of some critical tasks must be maximized when the network capacity cannot accommodate all tasks due to the limited capacity for each unmanned aerial vehicle (UAV). To solve this problem, this paper proposes an extended performance impact algorithm with critical tasks (EPIAC) based on the traditional performance impact algorithm. A novel task list resizing phase is developed in EPIAC to deal with the constraint on the limited capacity of each UAV and maximize the assignments of critical tasks. Numerical simulations demonstrate the outstanding performance of EPIAC compared with other algorithms.

In this article, hybridization of IWD (intelligent water drop) and GA (genetic algorithm) technique is developed and executed in order to obtain global optimal path by replacing local optimal points. Sensors of mobile robots are used for mapping and detecting the environment and obstacles present. The developed technique is tested in MATLAB simulation platform, and experimental analysis is performed in real-time environments to observe the effectiveness of IWD-GA technique. Furthermore, statistical analysis of obtained results is also performed for testing their linearity and normality. A significant improvement of about 13.14% in terms of path length is reported when the proposed technique is tested against other existing techniques.

In this paper, a weighted-neighbor-based cooperation control of multi-quadrotor systems is investigated. A formation tracking problem is treated, where the reference formation trajectory (RFT) is not given a priori. The RFT is only available to some of the quadrotors (i.e. the leaders). In order to attain the fast convergence of the agents, we propose an algorithm to calculate the neighbors’ weights in decentralized way. Then, the weights are used to compose the formation controller. Compared to the widely used average-neighbor-based control method, the proposed control protocol can increase the convergence speed of the cooperation error. Since the formation control is improved in topological scale, the utilization of the proposed algorithms can be extended on any multi-robot systems. We show the improvement of the proposed control protocol by theoretical proof, simulation, and real-time experiments.

Aiming at 3SPS+1PS parallel hip joint simulator, the maximum stress of branched chains under the suggested trajectory is obtained by elastodynamic analysis. Based on Corten-Dolan fatigue damage theory and Rain-flow counting method, the dynamic stress of each branched chain is statistically analyzed. The fatigue life prediction shows that branched-chain A2P2C2 is the weakest component for the simulator. Finally, the fatigue reliability is analyzed and the fatigue life and reliability under different structural parameters are discussed. The study shows that the fatigue life of each branched chain can be increased or balanced by increasing structural parameters or exchanging initial motion parameters.