About the project developing “MIPRA” – the intelligent planner in the state space for vehicles, tractors, and robots based on the architectural solutions of the Mivar systems for traffic enforcement

Abstract

The task of creating a logical “strong” artificial intelligence (AI) to be used as new decision-making systems for autonomous vehicles, tractors, and robotic systems draws increasing attention of scientists around the world. Essentially, it is the creation of “brains” for vehicles and any other transport systems, including cyber-physical systems. The mivar technologies of logical AI allowed solving many problems at a qualitatively new level and reducing the decision-making time from billions of years to hundredths of seconds, which enables real-time vehicle control. This paper shows how the application of mivar products (mivar expert systems) allows solving another class of problems – state-space planning (STRIPS planning). In turn, the solutions of these problems enable the development of driver assistance systems at a new level and creation of a fully autonomous vehicle.

Creating a “Logical intelligent plant care system” in digital agriculture based on Mivar approach

“…The project presented in this work can be used as a hybrid expert system . Advantages of this system include high speed solution development with linear computational complexity, availability of requirements for computing equipment, the ability to enter information into the knowledge base directly to the expert agronomist [16], and the ability to solve and visualize complex logical problems on ultra-large data sets [17]. Thanks to the algorithms based on active trainable evolutionary mivar network, it is possible to implement a plant care system that considers individual characteristics of crop growth, make decisions in conditions of heterogeneous sensor data (data collection), and timely adjust the process of plants growing.…”

A Fast Path Planing Solution For Multiple Obstacle Spaces

“…At present, rapid development and wide application of autonomous robot systems cause rapid development of path planning algorithms [5,6,7]. Most of the existing algorithms generally divide continuous spaces into grids and then search for path on the grid graph by some a shortest path algorithm [8], such as * A algorithm [9] and Dijkstra's algorithm [10].…”

“…Nowadays, although application scenarios, sensor types, driving methods, dimensions of robots are very different, navigation functions are embedded in almost all robots for practical applications. Hence, excellent motion path planning for complex environments becomes more indispensable [4].At present, rapid development and wide application of autonomous robot systems cause rapid development of path planning algorithms [5,6,7]. Most of the existing algorithms generally divide continuous spaces into grids and then search for path on the grid graph by some a shortest path algorithm [8], such as * A algorithm [9] and Dijkstra's algorithm [10].…”