engleski

Task planning based on the interpretation of spatial structures

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, able to rearrange the objects into a desired ordered final state. The environment is interpreted through position and orientation of 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 implemented for solving task planning problems represented in to two-dimensional space. A verification of the planning algorithm is done in a virtual environment.

Robotics ; Task planning ; Genetic algorithms

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