Abstract
Reactive algorithm in an unknown environment is very useful to deal with dynamic obstacles that may change unexpectantly and quickly because the workspace is dynamic in real-life applications, and this work is focusing on the dynamic and unknown environment by online updating data in each step toward a specific goal; sensing and avoiding the obstacles coming across its way toward the target by training to take the corrective action for every possible offset is one of the most challenging problems in the field of robotics. This problem is solved by proposing an Artificial Intelligence System (AIS), which works on the behaviour of Intelligent Autonomous Vehicles (IAVs) like humans in recognition, learning, decision making, and action. First, the use of the AIS and some navigation methods based on Artificial Neural Networks (ANNs) to training datasets provided high Mean Square Error (MSE) from training on MATLAB Simulink tool. Standardization techniques were used to improve the performance of results from the training network on MATLAB Simulink. When it comes to knowledge-based systems, ANNs can be well adapted in an appropriate form. The adaption is related to the learning capacity since the network can consider and respond to new constraints and data related to the external environment.
Highlights
Navigation is an important challenge for autonomous mobile robots [1]. e problem is divided into positioning and path planning, so the major purpose of usage of the mobile robot is the shortest path from an initial point to the final point in minimum time with high accuracy
Real devices usually operate in systems that are far from ideal, and defects cannot be avoided in real production processes, they still work. is is often associated with the fact that defects produce hidden dynamics, which, appropriately evoked, have an overall positive effect on the device [2]
It is found that if the features are on the same scale during gradient descent, the algorithm appears to perform better than when the features in the same range are not properly scaled. e plot in Figure 7 shows the influence of feature scaling on the contour plot of the cost function of the hypothesis based on these characteristics
Summary
Navigation is an important challenge for autonomous mobile robots [1]. e problem is divided into positioning and path planning, so the major purpose of usage of the mobile robot is the shortest path from an initial point to the final point (target) in minimum time with high accuracy.Real devices usually operate in systems that are far from ideal, and defects cannot be avoided in real production processes, they still work. is is often associated with the fact that defects produce hidden dynamics, which, appropriately evoked, have an overall positive effect on the device [2]. Navigation is an important challenge for autonomous mobile robots [1]. E problem is divided into positioning and path planning, so the major purpose of usage of the mobile robot is the shortest path from an initial point to the final point (target) in minimum time with high accuracy. Real devices usually operate in systems that are far from ideal, and defects cannot be avoided in real production processes, they still work. Is is often associated with the fact that defects produce hidden dynamics, which, appropriately evoked, have an overall positive effect on the device [2]. We discuss an impact strategy to ensure optimal working conditions support triggering the hidden dynamics of defects, characterizing their impact by referring to the characteristics of the control signals and the facility providing the structure. Navigation is one of the most critical problems in designing and improving the intelligent mobile network
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