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The ability of a robot to plan its own motion seems pivotal to its autonomy, and that is why the motion planning has become part and parcel of modern intelligent robotics. In this paper, about 100 research are reviewed and briefly described to identify and classify the amount of the existing work for each motion planning approach. Meanwhile, around 200(More)
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(More)
Given the importance of an accurate wind speed forecasting for efficient utilization of wind farms, and the volatile nature of wind speed data including its non-linear and uncertain nature, the wind speed forecasting has remained an active field of research. In this study, the non-linearity of wind speed is tackled using artificial neural network and its(More)
— Sampling-based motion planning algorithms have been proven to work well with difficult planning tasks in a variety of problems. Recently, asymptotic optimal algorithms have been proposed to overcome the non-optimality inefficiency of these planners but with extra computational costs associated with the additional processing requirements. In this paper,(More)
Path planning with obstacles avoidance in dynamic environments is a crucial issue in robotics. Numerous approaches have been suggested for the navigation of mobile robots with moving obstacles. In this paper, about 50 articles have been reviewed and briefly described to offer an outline of the research progress in motion planning of mobile robot approaches(More)
The area of robot path planning and navigation has been studied by various researchers over the last decades, resulting in a large number of works. One of the most challenging fields in motion planning is dealing with unknown environment, which is known as online path planning. This paper aims to improve one of the most famous methods for online navigation,(More)
Sampling-based path planning methods for autonomous agents are one of the well-known classes of robotic navigation approaches with significant advantages including ease of implementation and efficiency in problems with high degrees of freedom. However, there are some serious drawbacks like inability to plan in unknown environments, failure in complex(More)