![]() The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. Path Planning Algorithms for the Adaptive Sensor Fleet ![]() The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. Unique coding techniques reduce the computational complexity of the algorithm. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. Mobile robot dynamic path planning based on improved genetic algorithm ![]() Simulated annealing is an established technique for avoiding local minima in multidimensional optimization problems, but has not, until now, been applied to planning collision-free robot paths by use of low-power computers. The stochastic aspect lies in the use of simulated annealing to (1) prevent trapping of an optimization algorithm in local minima of an energy-like error measure by which the fitness of a trial solution is evaluated while (2) ensuring that the entire multidimensional configuration and parameter space of the path-planning problem is sampled efficiently with respect to both robot joint angles and computation time. Hence, the present software is better suited for application aboard robots having limited computing capabilities (see figure). Stochastic evolutionary algorithms can be made to produce acceptably close approximations to exact, optimal solutions for path-planning problems while often demanding much less computation than do exhaustive-search and deterministic inverse-kinematics algorithms that have been used previously for this purpose. Stochastic Evolutionary Algorithms for Planning Robot Pathsįink, Wolfgang Aghazarian, Hrand Huntsberger, Terrance Terrile, RichardĪ computer program implements stochastic evolutionary algorithms for planning and optimizing collision-free paths for robots and their jointed limbs. ![]()
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