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This paper presents the implementation of a novel technique for sensor based path planning of autonomous mobile robots. The proposed method is based on finding free-configuration eigen spaces (FCE) in the robot actuation area. Using the FCE technique to find optimal paths for autonomous mobile robots, the underlying hypothesis is that in the low-dimensional manifolds of laser scanning data, there lies an eigenvector which corresponds to the free-configuration space of the higher order geometric representation of the environment. The vectorial combination of all these eigenvectors at discrete time scan frames manifests a trajectory, whose sum can be treated as a robot path or trajectory. The proposed algorithm was tested on two different test bed data, real data obtained from Navlab SLAMMOT and data obtained from the real-time robotics simulation program Player/Stage. Performance analysis of FCE technique was done with existing four path planning algorithms under certain working parameters, namely computation time needed to find a solution, the distance travelled and the amount of turning required by the autonomous mobile robot. This study will enable readers to identify the suitability of path planning algorithm under the working parameters, which needed to be optimized. All the techniques were tested in the real-time robotic software Player/Stage. Further analysis was done using MATLAB mathematical computation software.

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An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding local minima. It gives the optimum paths which are always consisting on free trajectories. But the proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to destination due to the sharing of population. The total population is partitioned into a number subgroups to perform the parallel GA. The master thread is the center of information exchange and making selection with fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges quickly with in a less number of iteration.

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Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub) trajectories in the MOD. In order to find the most representative sub trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.

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Abstract Day by day, wireless communication popularity growing fast, it has led wireless communication data rates higher and made its prices cheaper, that’s why wireless communication is growing so fast. The latest technological demands now a day’s arising from laptops, wireless devices such as wireless local area networks (LANs) etc. In this paper we compared different types of node in MANET like static nodes, dynamic nodes and dynamic nodes with trajectory by using Dynamic routing protocol (DSR) and compared the performance of these nodes by using OPNET simulator 14.5. The performance is evaluated under different parameters like Data Dropped (Retry Threshold Exceeded), Load, Media access delay and Network Load by using FTP load. Keywords: MANET, Peak Value, Protocol, Trajectory.

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