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.

This paper introduces a manipulator robot surface following algorithm using a 3D model of vehicle body panels acquired by a network of rapid but low resolution RGB-D sensors. The main objective of this work is to scan and dynamically explore regions of interest over an automotive vehicle body under visual guidance by closely following the surface curves and maintaining close proximity to the object. The work is motivated by applications in automated vehicles inspection and screening in security applications. The proposed path planning strategy is developed based on a perceptionmodeling-planning-action approach. Raw data rapidly captured by a calibrated network of Kinect sensors are processed to provide the required 3D surface shape of objects, normal measurements, orientation estimation, and obstacle detection. A robust motion planning method is designed that relies on this information, resulting in a safe trajectory that is planned to follow and explore the curved surfaces while avoiding collision with protruding components of the vehicle. The feasibility and effectiveness of the proposed method is validated through experimental results with a 7-DOF manipulator navigating over automotive body panels.

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Industrial robots have been employed worldwide in the manufacturing sector for performing tasks quickly, repeatedly and accurately in relatively static environments for over 30 years. In recent years, close physical interaction between industrial robots and human operators has attracted researchers' attention and encouraged a number of technological innovations to turn these robots into humanrobot platforms. In this work a specially designed compliant wrist is developed to support dexterous robotic interaction with live proximity and contact feedback. The compliant wrist incorporates a level of compliance into an initially noncompliant manipulator robot which allows the robot to dynamically adapt to the surfaces it approaches or touches. Furthermore, to facilitate human-robot interactions, the robot must be able to adapt its behavior to the human partner. Therefore, a real-time path planning method is developed to generate online motion, adapt the robot to dynamic changes in the environment and ensure smooth interactions. The performance of the proposed method is demonstrated through experimental results on a CRS-F3 manipulator.

Online navigation with known target and unknown obstacles is an interesting problem in mobile robotics. This article presents a technique based on utilization of neural networks and reinforcement learning to enable a mobile robot to learn constructed environments on its own. The robot learns to generate efficient navigation rules automatically without initial settings of rules by experts. This is regarded as the main contribution of this work compared to traditional fuzzy models based on notion of artificial potential fields. The ability for generalization of rules has also been examined. The initial results qualitatively confirmed the efficiency of the model. More experiments showed at least 32 % of improvement in path planning from the first till the third path planning trial in a sample environment. Analysis of the results, limitations, and recommendations is included for future work.