Autonomous robots are intended to help humans performing a lot of different tasks in a safer and more efficient way. Some of those tasks must be solved by a group of autonomous robots. Also, when the task can be solve...
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ISBN:
(纸本)9781728142685
Autonomous robots are intended to help humans performing a lot of different tasks in a safer and more efficient way. Some of those tasks must be solved by a group of autonomous robots. Also, when the task can be solved only by one robot, for cost constraints, it is cheaper (for development and maintenance) to implement solutions including a group of simple robots. Solutions including multiple robots have to solve group problems like communication and coordination;also, common problems of autonomous robots like the widely studied problem of pathplanning must be rethought. In this case, finding a collision-free path is not enough because each robot also has to avoid collisions with other robots (by coordinating their movements). In this scenario, the pathplanning problem turns into the multi-robot motion planning (MRMP) problem. There are two approaches for solving the MRMP problem: coupled and decoupled. This work is focused on the decoupled approach because it has the potential to solve MRMP not only in a centralized way but, also in a concurrent or distributed way. In this sense, a new parallelizable algorithm, called Local Coordination Diagrams - LCD, is presented in this paper. Experimental results show that our approach can be applied efficiently to a large number of robots.
Mobile robots are the robots that can move through the environment and be used in many applications, including the industrial environment, planet exploration, warehousing, and daily household chores. They can be contr...
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Mobile robots are the robots that can move through the environment and be used in many applications, including the industrial environment, planet exploration, warehousing, and daily household chores. They can be controlled by an operator, set to do some specific jobs, or work autonomously. robotpathplanning is the task of an autonomous robot to move safely from one position to another. In this paper, three new objective functions are introduced in the structure of improved grey wolf optimizer (IGWO) and improved particle swarm optimization (IPSO) for the robotpathplanning problems. As another part of our proposed method, a reduction of laser range finder (LRF) data is performed, and the avoidance collision approach is also introduced. robots determine the next position by using LRF data and IGWO (IPSO) algorithms in a local approach. The initial and the goal positions are predefined for each robot. Moreover, the location of static obstacles and other robots are unknown for each robot. Finally, the experimental results of the robotpathplanning using IGWO are compared to different algorithms. The results indicate that the proposed method performs better in determining an optimal, short, safe, and smooth path. Also, it has less power and time consumption than other methods. All the algorithms are implemented in the V-REP robot simulator.
pathplanning is a problem where the objective to reach up to target from source without collide with obstacle This problem would be complex when it is considered with multirobot and unknown environment. Reaching up ...
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ISBN:
(纸本)9788132222026;9788132222019
pathplanning is a problem where the objective to reach up to target from source without collide with obstacle This problem would be complex when it is considered with multirobot and unknown environment. Reaching up to the target is considered as an optimization problem where the objective to minimize the distance, time and energy. This paper use the Bat algorithm (BA) for the movement of robot form one location to next location with optimizes the time, distance and energy. Here the direction of the movement is given by clustering based distribution factors (CBDF) that guide the robot to move in different direction. Different parameters are calculated during the moving of robots that help to analyze the process of target searching and tracking. Simulation is done with both simple and complex environment and results shows that the method works in both cases in searching and tracking.
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