Aleksandr I. Panov

Multilayer cognitive architecture for UAV control

Extensive use of unmanned aerial vehicles (UAVs) in recent years has induced the rapid growth of research areas related to UAV production. Among these, the design of control systems capable of automating a wide range of UAV activities is one of the most actively explored and evolving. Currently, researchers and developers are interested in designing control systems that can be referred to as intelligent, e.g. the systems which are suited to solve such tasks as planning, goal prioritization, coalition formation, etc. and thus guarantee high levels of UAV autonomy. One of the principal problems in intelligent control system design is tying together various methods and models traditionally used in robotics and aimed at solving such tasks as dynamics modeling, control signal generation, location and mapping, path planning, etc. with the methods of behavior modeling and planning which are thoroughly studied in cognitive science. Our work is aimed at solving this problem. We propose layered architecture-STRL (strategic, tactical, reactive, layered)-of the control system that automates the behavior generation using a cognitive approach while taking into account complex dynamics and kinematics of the control object (UAV). We use a special type of knowledge representation-sign world model-that is based on the psychological activity theory to describe individual behavior planning and coalition formation processes. We also propose path planning methodology which serves as the mediator between the high-level cognitive activities and the reactive control signals generation. To generate these signals we use a state-dependent Riccati equation and specific method for solving it. We believe that utilization of the proposed architecture will broaden the spectrum of tasks which can be solved by the UAVs coalition automatically, as well as raise the autonomy level of each individual member of that coalition.

Behavior and Path Planning for the Coalition of Cognitive Robots in Smart Relocation Tasks

In this paper we outline the approach of solving special type of navigation tasks for robotic systems, when a coalition of robots (agents) acts in the 2D environment, which can be modified by the actions, and share the same goal location. The latter is originally unreachable for some members of the coalition, but the common task still can be accomplished as the agents can assist each other (e.g., by modifying the environment). We call such tasks smart relocation tasks (as they cannot be solved by pure path planning methods) and study spatial and behavior interaction of robots while solving them. We use cognitive approach and introduce semiotic knowledge representation—sign world model which underlines behavioral planning methodology. Planning is viewed as a recursive search process in the hierarchical state-space induced by sings with path planning signs residing on the lowest level. Reaching this level triggers path planning which is accomplished by state-of-the-art grid-based planners focused on producing smooth paths (e.g., LIAN) and thus indirectly guarantying feasibility of that paths against agent’s dynamic constraints.

Spatial Reasoning and Planning in Sign-Based World Model

The paper discusses the interaction between methods of modeling reasoning and behavior planning in a sign-based world model for the task of synthesizing a hierarchical plan of relocation. Such interaction is represented by the formalism of intelligent rule-based dynamic systems in the form of alternate use of transition functions (planning) and closure functions (reasoning). Particular attention is paid to the ways of information representation of the object spatial relationships on the local map and the methods of organizing pseudo-physical reasoning in a sign-based world model. The paper presents a number of model experiments on the relocation of a cognitive agent in different environments and replenishment of the state description by means of the variants of logical inference.

Task and Spatial Planning by the Cognitive Agent with Human-Like Knowledge Representation

The paper considers the task of simultaneous learning and planning actions for moving a cognitive agent in two-dimensional space. Planning is carried out by an agent who uses an anthropic way of knowledge representation that allows him to build transparent and understood planes, which is especially important in case of human-machine interaction. Learning actions to manipulate objects is carried out through reinforcement learning and demonstrates the possibilities of replenishing the agent’s procedural knowledge. The presented approach was demonstrated in an experiment in the Gazebo simulation environment.

Applying a Neural Network Architecture with Spatio-Temporal Connections to the Maze Exploration

We present a model of Reinforcement Learning, which consists of modified neural-network architecture with spatio-temporal connections, known as Temporal Hebbian Self-Organizing Map (THSOM). A number of experiments were conducted to test the model on the maze solving problem. The algorithm demonstrates sustainable learning, building a near to optimal routes. This work describes an agents behavior in the mazes of different complexity and also influence of models parameters at the length of formed paths.

Synthesis of the Behavior Plan for Group of Robots with Sign Based World Model

The paper considers the task of the group’s collective plan intellectual agents. Robotic systems are considered as agents, possessing a manipulator and acting with objects in a determined external environment. The MultiMAP planning algorithm proposed in the article is hierarchical. It is iterative and based on the original sign representation of knowledge about objects and processes, agents knowledge about themselfs and about other members of the group. For distribution actions between agents in general plan signs “I” and “Other” (“They”) are used. In conclusion, the results of experiments in the model problem “Blocksworld” for a group of several agents are presented.