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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)
— 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)
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)
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)
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