Planning in a hierarchical nested controller for autonomous robots

Intelligent systems:

“…In 1986 it was demonstrated that substantial complexity reduction could be achieved when the search is applied in a multiresolutional fashion [109]. In 1987 the same concept was proposed in the AI community [110] without reference to the original paper [109], During the subsequent ten years, the concept of multiresolutional combinatorial search (CS) was confirmed in multiple applications.…”

“…In 1987 the same concept was proposed in the AI community [110] without reference to the original paper [109], During the subsequent ten years, the concept of multiresolutional combinatorial search (CS) was confirmed in multiple applications. In 1996 the same concept was proposed again in [111] for neuro-fuzzy paradigm, and in [112] by machine learning community.…”

“…Meystel [107] and [109] Multiresolutional methods of world representation were quickly incorporated by researchers in computer vision and image processing [137][138][139]. Similar research was conducted in the area of finite element algorithms [140].…”

“…• In 1986, A. Meystel demonstrated that the most efficient (least computational complexity) functioning of multilevel leaming/control systems with search for the planning could be provided by a proper choice of a ratio of lower level/higher level of resolution [109]. This concept of planning/control hierarchy became a strong theoretical support for the hierarchical architecture of intelligent system.…”

Intelligent Mobile Autonomous System

“…Force( t + At) = Force ( t ) + (Jerk ( t + At) * At ) (19) Velocity ( t + At ) =Velocity (t) + ((Force ( t + At ) + Resistance ( t + At)) * At)/ Mass (20) Distance ( t + At ) = Distance ( t ) + (Velocity ( t + At )*At) (21) Hence using these equations one find the state of the simulated system at each instance of the system. This simulations assumes that the increase of time At is small and hence the assumption that the system behaves linearly during this time.…”