Knowledge Autonomous unmanned quadrocopter – manipulator in an uncertain environment problem

We develop a method replenishing the knowledge of the quadrocopter equipped with a manipulator based on the fuzzy inference of plausible conclusions, allowing to obtain the missing information about the objects of the problem environment necessary for the planning of targeted behavior in under-defined operating conditions. Conditions for the application of different similarity assessments for comparing incompletely described objects of the problematic environment are proposed. The conclusion of plausible reasoning is organized on this basis with the required for decision-making degree of truth. The rules for deductive, inductive and transductive reasoning are synthesized using the assumptions formed by indistinctly defined condition-dependent predicates and statements. This allows the intelligent task solver of unmanned aerial vehicles with limited computing resources to fill expeditiously the missing knowledge in various conditions of underdetermined problem environment.