In this paper, we propose an acceleration-level pseudo-dynamic visual servoing structure for the nonholonomic mobile robots, based on which we design two different adaptive controllers-backstepping and dynamic surface control (DSC) in the presence of unknown depth information. Different from existing kinematic controllers, which directly regard linear and angular velocities as control inputs, this paper designs acceleration control that is integrated to easily obtain smooth velocity signals to be accurately executed by the robot. Two controllers are designed and analyzed with Lyapunov techniques: 1) a backstepping controller yielding asymptotical stability and 2) a dynamic surface controller ensuring system errors to be ultimately uniformly bounded. The unknown depth is handled by designing an adaptive parameter estimation law in both methods. Finally, a comparison between backstepping and DSC is given based on the experimental results and the design procedures.

Cost, performance and efficiency of energy usage of a robot system is strongly dependent on its drive chain, i.e. combination of the drive, the motors and the gears. A model is presented that allows accurate simulation of the drive chain in an industrial robot to offer a high degree of optimization in the design process. Simulation results are compared to final data from the developed units and excellent agreements are found.

In this paper, we consider the problem of building a probability map for an unknown hostile environment by utilizing a team of UAVs. Specifically, we first present a centralized map building scheme for the Boeing open experimental platform (OEP) environment, the strategy is then modified into a decentralized map building algorithm to increase the robustness of the system. Some simulation results are provided to demonstrate the validity of the proposed algorithms