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Task planning based on the interpretation of spatial structures

机译：基于空间结构解释的任务计划

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In this research, a new task planning algorithm is developed for building a desired object configuration from a given initial unordered object state. The task of the planning algorithm is to find a feasible set of actions, i.e. a finite number of discrete transformations, which can rearrange the objects into a desired ordered final state. The environment is interpreted through the position and orientation of the objects. The solution to the planning problem is proposed as a two-step method. First, a constructive heuristic generates an initial set of good solutions. The constructive heuristic uses only mutations for making an initial population of state transitions. A genetic algorithm is developed for optimizing the initial set of solutions. The genetic algorithm is characterized by a parallel evolutionary strategy, with the aim of spatial transformation of unordered object states into ordered object states. The algorithm can be used for solving the task planning problems represented in the two-dimensional space. Verification of the planning algorithm is done in a virtual environment.
机译：在这项研究中，开发了一种新的任务计划算法，用于从给定的初始无序对象状态构建所需的对象配置。规划算法的任务是找到一组可行的动作，即有限数量的离散转换，这些动作可以将对象重新排列为所需的有序最终状态。通过对象的位置和方向来解释环境。计划问题的解决方案建议采用两步法。首先，建设性的启发式方法会产生一系列好的解决方案。建构式启发式仅使用突变来进行初始状态转换。开发了一种遗传算法来优化初始解集。遗传算法的特征在于并行进化策略，目的是将无序对象状态空间转换为有序对象状态。该算法可用于解决二维空间中表示的任务计划问题。规划算法的验证是在虚拟环境中完成的。

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