![]() The Heuristic Function determines the search speed, accuracy, and memory consumption. The performance of A* is heavily dependent on the quality of the Heuristic Function. The FPGA accelerator proposed in this paper achieves more than 8x speed up compared to the software implementation.Ī* is an informed pathfinding algorithm that uses a Heuristic Function to determine the best action to take based on the given information. Moreover, the proposed Genetic Algorithm accelerator can be customized in terms of the population size, number of generations, crossover rates, and mutation rates for flexibility. Therefore, we propose a hardware accelerator architecture for EHA* that is implemented on a Field Programmable Gate Array(FPGA) by employing a combination of pipelining and parallelization to achieve better performance. Although the Genetic Algorithm is proved to be efficient on solving complex problems, the amount of computations and iterations required for this method is enormous. It has been successfully applied on many complex real world applications including VLSI circuit partitioning, Travelling Salesman Problem (TSP), and robotic designs. The Genetic Algorithm is one of the most popular and efficient optimization techniques that is based on the Darwinian principle of survival of the fittest. Evolutionary Heuristic A* (EHA*) search proposed to have a self-evolving Heuristic Function to reduce the engineering efforts on Heuristic Functions design. However, designing new Heuristic Functions is time consuming and difficult. Hence, designing good Heuristic Functions for specific domains becomes the primary research focus on pathfinding algorithms optimization. A complex pathfinding problem requires a well-informed Heuristic Function to efficiently process all data and compute the next move. Compared to a standard delete relaxation Heuristic, while the increased runtime overhead often is detrimental, in some cases the search space reduction is strong enough to result in improved performance overall.Ī* is an informed pathfinding algorithm that depends on an accurate Heuristic Function to search for the shortest path. We evaluate this Heuristic on the planning competition benchmarks. We employ this insight to devise a new red-black plan Heuristic in which variables are painted black starting from the causal graph leaves. We show that, if no red variable relies on black preconditions, then red-black plan generation is tractable in the size of the black state space, i.e., the product of the black variables. Here, we consider cross-dependencies between black and red variables instead. ![]() Prior work has identified such sub-classes based on the black causal graph, i.e., the projection of the causal graph onto the black variables. Practical Heuristic Functions can be generated from tractable sub-classes of red-black planning. ![]() Red-black planning is a recent approach to partial delete relaxation, where red variables take the relaxed semantics (accumulating their values), while black variables take the regular semantics. Finally, experimental results are given to show the effectiveness and efficiency of the proposed method and Heuristic Function. The admissibility of the proposed Heuristic Function is proved. When compared with related approaches, the proposed one can deal with token remaining time, weighted arcs, and multiple resource copies commonly seen in the PN models of RCM systems. To schedule RCM systems, this work proposes an A* search with a new Heuristic Function based on timed PNs. Within their reachability graphs, timed PNs' evolution and intelligent search algorithms can be combined to find an efficient operation sequence from an initial state to a goal one for the underlying systems of the nets. ![]() Timed Petri nets (PNs) are a formalism suitable for graphically and concisely modeling such systems and obtaining their reachable state graphs. System scheduling is a decision-making process that plays an important role in improving the performance of robotic cellular manufacturing (RCM) systems. ![]()
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