Development of a sensor-based approach for local minima recovery in unknown environments

This paper introduces a new methodology for escaping from local minima using an actual-virtual target switching strategy. In particular, this approach proposes suitable steps to detect trap situations and guide the robot away from local minima even when the environment is completely unknown. In this work the navigation system consists of two layers. In the low-level layer, a Nearest Virtual Target (NVT) approach is adapted as a reactive collision avoidance method for mobile robot navigation to achieve collision free motion in cluttered, dense and troublesome scenarios. Where the robot is surrounded by obstacles and a trap situation is likely to occur, the high-level layer becomes responsible to plan a path to pull the robot out of the trap. Finally, the performance of the proposed approach is validated by simulation results

Rapid 3D Modeling and Parts Recognition on Automotive Vehicles Using a Network of RGB-D Sensors for Robot Guidance

This paper presents an approach for the automatic detection and fast 3D profiling of lateral body panels of vehicles. The work introduces a method to integrate raw streams from depth sensors in the task of 3D profiling and reconstruction and a methodology for the extrinsic calibration of a network of Kinect sensors. This sensing framework is intended for rapidly providing a robot with enough spatial information to interact with automobile panels using various tools. When a vehicle is positioned inside the defined scanning area, a collection of reference parts on the bodywork are automatically recognized from a mosaic of color images collected by a network of Kinect sensors distributed around the vehicle and a global frame of reference is set up. Sections of the depth information on one side of the vehicle are then collected, aligned, and merged into a global RGB-D model. Finally, a 3D triangular mesh modelling the body panels of the vehicle is automatically built. The approach has applications in the intelligent transportation industry, automated vehicle inspection, quality control, automatic car wash systems, automotive production lines, and scan alignment and interpretation

Automatic Temporal Location and Classification of Human Actions Based on Optical Features

Abstract—This paper presents a method for automatic temporal location and recognition of human actions. The data are obtained from a motion capture system. They are then animated and optical flow vectors are subsequently calculated. The system performs in two phases. The first phase employs nearest neighbor search to locate an action along the temporal axis taking into account both the angle and length of the vectors, while the second classifies the action using artificial neural networks. Principal Component Analysis (PCA) plays a significant role in discarding correlated flow vectors. We perform a statistical analysis in order to achieve an efficient, adaptive and targeted PCA. This will greatly improve the configuration of flow vectors which we have used to train both the locating and classifying systems. Experimental results confirm the significance of our proposed method for locating and classifying a specific action from among a sequential combination of actions. Keywords-temporal location; classification; human actions; neural networks; principal component analysis. I

3D Modeling from Multiple Views with Integrated Registration and Data Fusion

This paper presents an integrated modeling system capable of generating coloured three dimensional representations of a scene observed from multiple viewpoints. Emphasis is given to the integration of the components and to the algorithms used for acquisition, registration and final surface mapping. First, a sensor operating with structured light is used to acquire 3D and colour data of a scene from multiple views. Second, a frequency-domain based registration algorithm computes the transformation between pairs of views from the raw measurements and without a priori knowledge on the transformation parameters. Finally, the registered views are merged together and refined to create a rich 3D model of the objects. Real world modeling examples are presented and analyzed to validate the operation of the proposed integrated modeling system

Haptic Human Interfaces for Robotic Telemanipulation

Abstract -This paper proposes that there are ultimately only two topi cal tactile feedback generation modalities for haptic human interfaces which allow the human operator to handle either (i) temporary VR-based material replicas of the local geometric and/or force profile at the contact areas of an unlimited set of generic objects that could virtually be handled during the manipulation, or (ii) permanent material replicas of a limited set of typical objects. Examples of tactile human interfaces developed by the authors for telerobotic blind tactile exploration of objects, for telerobotic hapto-visual stylus-style tool manipulation are presented to illustrate the proposed approach. A NN architecture allowing for the modelling of the elastic properties of 3D objects from experimental tactile and range imaging data is also presented

A hybrid control architecture for autonomous mobile robot navigation in unknown dynamic environment

This paper introduces a new hybrid control architecture for solving the navigation problem of mobile robot in an unknown dynamic environment based on an actual-virtual target switching strategy. This hybrid architecture is a combination of deliberative and reactive architectures which consists of three layers: modeling, planning and reaction. The deliberative architecture produces collision-free with shortest-distance path, while using the reactive architecture generates safe and time minimal navigation path. The proposed approach differs from previous ones in its integration architecture, the control techniques implemented in each module, and interfaces between the deliberative and reactive components. Validity and feasibility of the proposed approach are verified through simulation and real robot experiments

Robust nonlinear trajectory controllers for a single-rotor UAV with particle swarm optimization tuning

This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller connected in cascade with a P-PI attitude controller. Three different control approaches (classical PID, Super Twisting, and Adaptive Sliding Mode) are compared for the translation control. The parameters of such controllers are hard to tune by using a trial-and-error procedure, so we use an automated tuning procedure based on the Particle Swarm Optimization (PSO) method. The controllers were simulated in scenarios with wind gust disturbances, and a performance comparison was made between the different controllers with and without optimized gains. The results show a significant improvement in the performance of the PSO-tuned controllers.Peer ReviewedPostprint (published version