Classical Q-learning requires huge computations to attain convergence and a large storage to save the Q-values for all possible actions in a given state. This paper proposes an alternative approach to Q-learning to reduce the convergence time without using the optimal path from a random starting state of a final goal state, when the Q-table is used for path planning of a mobile robot. Further, the proposed algorithm stores the Qvalue for the best possible action at a state, and thus save significant storage. Experiments reveal that the acquired Q-table obtained by the proposed algorithm helps in saving turning angles of the robot in the planning stage. Reduction in turning angles is economic from the point of view of energy consumption by the robot. Thus the proposed algorithm has several merits with respect to classical Q-learning. The proposed algorithm is constructed based on four fundamental properties derived here and the validation of the algorithm is studied with Khepera-II robot.
In this paper a new method of road map based navigation is proposed. A vision based motion planning of a mobile robot is implemented in a predefined road map. Road map is build with the left and right lane at the junction constructed with 90 degree with respect to the main lane. In our realization the robot moves towards a junction and at each junction takes a photograph of the road sign map and an image matching algorithm is performed at the host machine to compare the captured image with the map stored in the memory and decide the next course of action.
In this paper, we study the path planning for khepera II mobile robot in an unknown environment. The well known heuristic D* lite algorithm is implemented to make the mobile robot navigate through static obstacles and find the shortest path from an initial position to a target position by avoiding the obstacles. and to perform efficient re-planning during exploration. The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In our study we have considered the distance metric as the cost function.
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