5 research outputs found
ΠΠ΅ΡΠΎΠ΄ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠ±ΠΎΡΠ° Π² ΠΏΠΎΠ»Π΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ²-ΡΠ΅ΠΏΠ΅Π»Π»Π΅ΡΠΎΠ²
Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ° ΠΊΠΎΡΡΠ΅ΠΊΡΠΈΡΠΎΠ²ΠΊΠΈ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΡΠΎΠ±ΠΎΡΠΎΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠ»Π°ΡΡΠΎΡΠΌΡ (Π Π’Π) Π½Π° ΠΏΠ»ΠΎΡΠΊΠΎΡΡΠΈ Ρ ΡΠ΅Π»ΡΡ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΡ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠΈ Π΅Ρ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ/ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ Π² ΠΏΠΎΠ»Π΅ ΠΊΠΎΠ½Π΅ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΡΠ»Π° ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ²-ΡΠ΅ΠΏΠ΅Π»Π»Π΅ΡΠΎΠ². ΠΠ°ΠΆΠ΄ΡΠΉ ΠΈΠ· ΡΠ°ΠΊΠΈΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² ΠΎΠΏΠΈΡΠ°Π½ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΡΡ Π½Π΅ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΡΠ°ΠΊΡΠΎΡΠ° ΠΏΡΠΎΡΠΈΠ²ΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΡΠ΅Π»ΠΎΡΡΠ½ΠΎΡΡΠΈ ΠΈΠ»ΠΈ ΡΠΊΡΡΡΠ½ΠΎΡΡΠΈ Π Π’Π. Π£ΠΊΠ°Π·Π°Π½Π½Π°Ρ ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ° ΠΎΡΠ½ΠΎΠ²Π°Π½Π°, Ρ ΠΎΠ΄Π½ΠΎΠΉ ΡΡΠΎΡΠΎΠ½Ρ, Π½Π° ΠΏΠΎΠ½ΡΡΠΈΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½ΠΎΠΉ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΡΠΈΡΡΠ΅ΠΌΡ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ²-ΡΠ΅ΠΏΠ΅Π»Π»Π΅ΡΠΎΠ², ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅ΠΌ ΠΎΡΠ΅Π½ΠΈΠ²Π°ΡΡ ΡΡΠ΅ΠΏΠ΅Π½Ρ Π²Π»ΠΈΡΠ½ΠΈΡ ΡΡΠΈΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² Π½Π° Π΄Π²ΠΈΠΆΡΡΡΡΡΡ Π Π’Π. ΠΠ· ΡΡΠΎΠ³ΠΎ ΠΏΠΎΠ½ΡΡΠΈΡ Π²ΡΡΠ΅ΠΊΠ°Π΅Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΠ°Ρ Π·Π΄Π΅ΡΡ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠ΅Π»Π΅Π²ΠΎΠΉ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΡ Π΅Ρ ΡΡΠΏΠ΅ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΡ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ. Π‘ Π΄ΡΡΠ³ΠΎΠΉ ΡΡΠΎΡΠΎΠ½Ρ, ΡΡΠ° ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ° Π±Π°Π·ΠΈΡΡΠ΅ΡΡΡ Π½Π° ΡΠ΅ΡΠ΅Π½ΠΈΠΈ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ
ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΎΠ½Π½ΡΡ
Π·Π°Π΄Π°Ρ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΡ
ΠΊΠΎΡΡΠ΅ΠΊΡΠΈΡΠΎΠ²Π°ΡΡ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΠ΅ ΡΡΠ°ΡΡΠΊΠΈ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠΉ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ Ρ ΡΡΠ΅ΡΠΎΠΌ Π½Π°Ρ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ Π² ΠΈΡ
ΠΎΠΊΡΠ΅ΡΡΠ½ΠΎΡΡΡΡ
ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ²ΡΠ΅ΠΏΠ΅Π»Π»Π΅ΡΠΎΠ² Ρ Π·Π°Π΄Π°Π½Π½ΡΠΌΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ. ΠΠ°ΠΆΠ΄ΡΠΉ ΠΈΠ· ΡΠ°ΠΊΠΈΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΠ΅ΡΡΡ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΠΎΠΌ, ΡΠ°ΡΡΠΎΡΠΎΠΉ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΡ, ΡΠ°Π΄ΠΈΡΡΠΎΠΌ Π΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ ΡΠΏΠ°Π΄Π° ΠΏΠΎΠ»Ρ. ΠΠΎΡΡΠ΅ΠΊΡΠΈΡΠΎΠ²ΠΊΠ° ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΠΈΡ ΠΈΡΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎ ΠΈ ΡΡΠΈΡΡΠ²Π°Π΅Ρ ΡΠ΅Π»Π΅Π²ΠΎΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠΈ ΠΏΡΠΎΡ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ. ΠΡΠ½ΠΎΠ²Π½ΡΠΌ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΠ΅ΠΌ Π½Π° Π²Π°ΡΠΈΠ°ΡΠΈΡ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠΉ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎ Π΄ΠΎΠΏΡΡΡΠΈΠΌΠΎΠ΅ ΠΎΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½Π½ΠΎΠΉ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ ΠΎΡ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠΉ. ΠΡΠ»ΠΈ ΡΠ°ΠΊΠΎΠ³ΠΎ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡ Π½Π΅Ρ, ΡΠΎ Π·Π°Π΄Π°ΡΠ° ΠΌΠΎΠΆΠ΅Ρ ΠΏΠΎΡΠ΅ΡΡΡΡ ΡΠΌΡΡΠ», ΠΏΠΎΡΠΊΠΎΠ»ΡΠΊΡ ΡΠΎΠ³Π΄Π° ΠΌΠΎΠΆΠ½ΠΎ Π²ΡΠ΄Π΅Π»ΠΈΡΡ ΠΎΠ±Π»Π°ΡΡΡ, ΠΎΡ
Π²Π°ΡΡΠ²Π°ΡΡΡΡ Π²ΡΠ΅ ΠΏΡΠ΅ΠΏΡΡΡΡΠ²ΠΈΡ ΠΈ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΈ, ΠΈ ΠΎΠ±ΠΎΠΉΡΠΈ Π΅Ρ ΠΏΠΎ ΠΏΠ΅ΡΠΈΠΌΠ΅ΡΡΡ. ΠΠΎΡΡΠΎΠΌΡ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠΎΠΈΡΠΊ ΡΠ°ΠΊΠΎΠ³ΠΎ Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΊΡΡΡΠ΅ΠΌΡΠΌΠ°, ΠΊΠΎΡΠΎΡΡΠΉ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΠ΅Ρ Π΄ΠΎΠΏΡΡΡΠΈΠΌΠΎΠΉ ΠΊΡΠΈΠ²ΠΎΠΉ Π² ΡΠΌΡΡΠ»Π΅ ΡΠΊΠ°Π·Π°Π½Π½ΠΎΠ³ΠΎ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠ°Ρ Π² Π½Π°ΡΡΠΎΡΡΠ΅ΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΠΈΡΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½Π°Ρ ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ° ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΡΡ ΠΏΠΎΠΈΡΠΊ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΡ
Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ
ΠΌΠ°ΠΊΡΠΈΠΌΡΠΌΠΎΠ² Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠΈ ΠΏΡΠΎΡ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ Π Π’Π Π² ΠΏΠΎΠ»Π΅ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ»ΡΠ½ΠΎ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½Π½ΡΡ
ΠΈ ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² Π² Π½Π΅ΠΊΠΎΡΠΎΡΠΎΠΉ ΠΎΠΊΡΠ΅ΡΡΠ½ΠΎΡΡΠΈ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠΉ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ. ΠΠ½Π°ΡΠ°Π»Π΅ ΡΡΠ°Π²ΠΈΡΡΡ ΠΈ ΡΠ΅ΡΠ°Π΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ ΠΏΡΠΈ ΡΡΠ»ΠΎΠ²ΠΈΠΈ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ Π² ΠΏΠΎΠ»Π΅ ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ° Ρ ΠΎΠ±Π»Π°ΡΡΡΡ Π΄Π΅ΠΉΡΡΠ²ΠΈΡ Π² Π²ΠΈΠ΄Π΅ ΠΊΡΡΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΡΠ΅ΠΊΡΠΎΡΠ°, Π·Π°ΡΠ΅ΠΌ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠΉ ΡΠ΅Π·ΡΠ»ΡΡΠ°Ρ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½ΡΠ΅ΡΡΡ Π½Π° ΡΠ»ΡΡΠ°ΠΉ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
Π°Π½Π°Π»ΠΎΠ³ΠΈΡΠ½ΡΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ². ΠΡΠ½ΠΎΠ²Π½ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΎΠΉ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ Π²ΡΠ±ΠΎΡ ΠΎΠ±ΡΠ΅Π³ΠΎ Π²ΠΈΠ΄Π° ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»Π° Π² ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΡΠΎΡΠΊΠ΅ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠΉ ΠΊΡΠΈΠ²ΠΎΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ Π΅Π³ΠΎ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠΎΠ² Π½Π°ΡΡΡΠΎΠΉΠΊΠΈ. ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ Π²ΡΠ±ΠΎΡ ΡΡΠΈΡ
ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠΎΠ² Π½Π°ΡΡΡΠΎΠΉΠΊΠΈ Π΅ΡΡΡ Π°Π΄Π°ΠΏΡΠΈΠ²Π½Π°Ρ ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ°, Π²Ρ
ΠΎΠ΄Π½ΡΠΌΠΈ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΠΌΠΈ ΠΊΠΎΡΠΎΡΠΎΠΉ ΡΠ²Π»ΡΡΡΡΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½ΡΠ΅ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π²Π΅Π»ΠΈΡΠΈΠ½Ρ, ΠΎΠΏΠΈΡΡΠ²Π°ΡΡΠΈΠ΅ ΡΠ΅ΠΊΡΡΡΡ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΡ Π² ΠΏΠΎΠ»Π΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ². ΠΠ»Ρ ΡΡΡΡΠ°Π½Π΅Π½ΠΈΡ ΠΎΡΡΠΈΠ»Π»ΡΡΠΈΠΉ, Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΠΈΡ
Π²ΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠ΅ Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠΉ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ, ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡΡΡ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΠ΅ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΠΌΠ΅Π΄ΠΈΠ°Π½Π½ΠΎΠ³ΠΎ ΡΠ³Π»Π°ΠΆΠΈΠ²Π°Π½ΠΈΡ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ Π²ΡΡΠΎΠΊΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠΉ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ Π΄Π»Ρ ΠΊΠΎΡΡΠ΅ΠΊΡΠΈΡΠΎΠ²ΠΊΠΈ ΡΠ°Π½Π΅Π΅ ΡΠΏΠ»Π°Π½ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ
Optimization of mobile robot movement on a plane with finite number of repeller sources
The paper considers the problem of planning a mobile robot movement in a conflict environment, which is characterized by the presence of areas that impede the robot to complete the tasks. The main results of path planning in the conflict environment are considered. Special attention is paid to the approaches based on the risk functions and probabilistic methods. The conflict areas, which are formed by point sources that create in the general case asymmetric fields of a continuous type, are observed. A probabilistic description of such fields is proposed, examples of which are the probability of detection or defeat of a mobile robot. As a field description, the concept of characteristic probability function of the source is introduced; which allows us to optimize the movement of the robot in the conflict environment. The connection between the characteristic probability function of the source and the risk function, which can be used to formulate and solve simplified optimization problems, is demonstrated. The algorithm for mobile robot path planning that ensures the given probability of passing the conflict environment is being developed. An upper bound for the probability of the given environment passing under fixed boundary conditions is obtained. A procedure for optimizing the robot path in the conflict environment is proposed, which is characterized by higher computational efficiency achieved by avoiding the search for an exact optimal solution to a suboptimal one. A procedure is proposed for optimizing the robot path in the conflict environment, which is characterized by higher computational efficiency achieved by avoiding the search for an exact optimal solution to a suboptimal one. The proposed algorithms are implemented in the form of a software simulator for a group of ground-based robots and are studied by numerical simulation methods
Trajectory Planning Algorithms in Two-Dimensional Environment with Obstacles
This article proposes algorithms for planning and controlling the movement of a mobile robot in a two-dimensional stationary environment with obstacles. The task is to reduce the length of the planned path, take into account the dynamic constraints of the robot and obtain a smooth trajectory. To take into account the dynamic constraints of the mobile robot, virtual obstacles are added to the map to cover the unfeasible sectors of the movement. This way of accounting for dynamic constraints allows the use of map-oriented methods without increasing their complexity. An improved version of the rapidly exploring random tree algorithm (multi-parent nodes RRT β MPN-RRT) is proposed as a global planning algorithm. Several parent nodes decrease the length of the planned path in comprise with the original one-node version of RRT. The shortest path on the constructed graph is found using the ant colony optimization algorithm. It is shown that the use of two-parent nodes can reduce the average path length for an urban environment with a low building density. To solve the problem of slow convergence of algorithms based on random search and path smoothing, the RRT algorithm is supplemented with a local optimization algorithm. The RRT algorithm searches for a global path, which is smoothed and optimized by an iterative local algorithm. The lower-level control algorithms developed in this article automatically decrease the robotβs velocity when approaching obstacles or turning. The overall efficiency of the developed algorithms is demonstrated by numerical simulation methods using a large number of experiments
Spectroscopic study of proflavine adsorption on the carbon nanotube surface
The present paper sheds light on one of the possible mechanisms of interaction between the typical aromatic dye proflavine and the carbon nanotube surface, namely, Ο-stacking between aromatic rings of these compounds. To investigate such a complexation, a qualitative analysis was performed by means of ultraviolet visible, infrared, and nuclear magnetic resonance spectroscopy. The data obtained suggest that Ο-stacking brings the major contribution to the stabilization of the complex between proflavine and the carbon nanotub
Impact of the Feeder Aerodynamics Characteristics on the Power of Control Actions in Steady and Transient Regimes
In this paper we consider one of the problems in the development of control system for the feeder for MAAT transportation system. This problem is connected with estimation of inboard energy requirements. Traditionally such estimation is made on the basis of static relations. They allow assessing the power required to move a solid body with a constant air speed. However a contribution from aerodynamic forces and moments can vary depending on a regime of motion (value of linear and angular accelerations, angle of attack, etc). Because of that fact, this work investigates the estimation of the total required inboard energy and contribution of aerodynamic forces and moments to it in specified feeder motion regimes. The method of assessment is based on the feeder model, which is built on the equations of the rigid body. This paper contains general structure of feeder mathematical model, which includes equations of statics, dynamics and control mechanisms. The example of the exact feeder shape gives the application of this models with the details in terms of aerodynamic characteristics, inertial mass parameters and locations of control mechanisms. Feeder model is complemented by the external environment model, including the wind flow model. Development of the latter models is investigated in the talk βProbabilistic Approaches to Estimation of Flight Environment for Feeder of Multibody Transport Airship Systemβ, presented on this conference. Three main feeder motion regimes were chosen for the estimation of the required power. These three regimes are hovering in one point, motion along a straight line and motion along a specified circle. Steady motion is considered along with transient regimes, when the feeder is moving to the specified trajectories. The results allow assessing the required power for steady and transient regimes for each considered trajectory, different values of air speed, different locations of centre of gravity and different angles of attack. Additionally, study of Kalman controllability and Lyapunov stability was made for the special feeder motion regimes. The conclusions about the optimal feeder shape are given based on this work.Peer reviewe