In this paper we present a novel planning approach, based on wellknown techniques such as goal decomposition and heuristic planning, aimed at working in highly dynamic environments with time constraints. Our contribution is a domain-independent planner to incrementally generate plans under a deliberative framework for reactive domains. The planner follows the anytime principles, i.e a first sol...

Stochastic assembly approaches can reduce the power, computation, and/or actuation demands on assembly systems by taking advantage of probabilistic processes. At the same time, however, they relinquish the efficiency and predictability of deterministic alternatives. This makes planning error-free assembly sequences challenging, particularly in the face of changing environmental conditions or go...

In highly dynamic environments with uncertainty the elaboration of long or rigid plans is useless because the constructed plans are frequently dismissed by the arrival or new unexpected situations; in these cases, a “second-best” plan could rescue the situation. We present a new real-time planning method where we take into consideration the number and quality of future options of the next actio...

In this thesis, we investigate the problem of Unmanned Aerial Vehicle (UAV) path planning in search or surveillance mission, when some a priori information about the targets and the environment is available. A search operation that utilizes the available a priori information about the initial location of the targets, terrain data, and information from reasonable assumptions about the targets mo...

Proper planning of assembly lines is one of the production managers’ concerns at the tactical level so that it would be possible to use the machine capacity, reduce operating costs and deliver customer orders on time. The lack of an efficient method in balancing assembly line can create threatening problems for manufacturing organizations. The use of assembly line balancing methods cannot balan...

In this paper three methods for on-line identification of first markov parameter at linear multivariable plants are presented. In these methods input-output data are used far the on-line identification of the first markov parameter.