The performance of robot manipulators with nonadaptive controllers might degrade significantly due to the open loop unstable system and the effect of some uncertainties on the robot model or environment. A novel Neural Network PID controller (NNP) is proposed in order to improve the system performance and its robustness. The Neural Network (NN) technique is applied to compensate for the effect of the uncertainties of the robot model. With the NN compensator introduced, the system errors and the NN weights with large dispersion are guaranteed to be bounded in the Lyapunov sense. The weights of the NN compensator are adaptively tuned.The simulation results show the effectiveness of the model validation approach and its efficiency to guarantee a stable and accurate trajectory tracking process in the presence of uncertainties.

In this paper a modified technique for 3D camera calibration is applied on a stereo-camera, in order to get accurate depth information of a desired object on the scene. The technique requires observing a scene or a planar pattern with known geometry at some different orientations. A curvature based corner detection technique is applied to detect both fine and coarse features at low computational cost to obtain the intrinsic and extrinsic parameters of cameras and achieve the translation between the world and image coordinates. SURF matching approach is aimed for optimizing the translation rule from the image to spatial coordinates by using a set of rotation-invariant interest points on stereo images. The result together with accuracy and calculation speed is reported.

There is an extensive literature regarding multi-robot simultaneous localization and mapping (MRSLAM). In most part of the research, the environment is assumed to be static, while the dynamic parts of the environment degrade the estimation quality of SLAM algorithms and lead to inherently fragile systems. To enhance the performance and robustness of the SLAM in dynamic environments (SLAMIDE), a novel cooperative approach named parallel-map (p-map) SLAM is introduced in this paper. The objective of the proposed method is to deal with the dynamics of the environment, by detecting dynamic parts and preventing the inclusion of them in SLAM estimations. In this approach, each robot builds a limited map in its own vicinity, while the global map is built through a hybrid centralized MRSLAM. The restricted size of the local maps, bounds computational complexity and resources needed to handle a large scale dynamic environment. Using a probabilistic index, the proposed method differentiates between stationary and moving landmarks, based on their relative positions with other parts of the environment. Stationary landmarks are then used to refine a consistent map. The proposed method is evaluated with different levels of dynamism and for each level, the performance is measured in terms of accuracy, robustness, and hardware resources needed to be implemented. The method is also evaluated with a publicly available real-world data-set. Experimental validation along with simulations indicate that the proposed method is able to perform consistent SLAM in a dynamic environment, suggesting its feasibility for MRSLAM applications.

No abstract