阅读说明：本技术 一种基于行为的多uuv自组织协调控制方法 (Behavior-based multi-UUV self-organizing coordination control method ) 是由 梁洪涛 强宁 于 2018-06-20 设计创作，主要内容包括：本发明涉及一种基于行为的多UUV自组织协调控制方法,包括：步骤1：构建免疫网络与多UUV系统之间的映射关系,并定义相关参数；步骤2：提出基于基本行为动作的协调控制动力学模型,并且实现作业环境误差的分数布朗运动建模；步骤3:给出了离散状态的基于免疫网络的多UUV自组织协调控制,满足分布式结构下多UUV系统的协同控制,实现对敌方目标的围捕作业；本方法具有强大的自组织性、容错性、实时性等特点,而且便于水下无人系统的工程实现。(The invention relates to a behavior-based multi-UUV self-organizing coordination control method, which comprises the following steps: step 1: constructing a mapping relation between an immune network and a multi-UUV system, and defining related parameters; step 2: a coordination control dynamics model based on basic behavior and action is provided, and fractional Brownian motion modeling of operation environment errors is realized; step 3, giving out discrete-state multi-UUV self-organizing coordination control based on an immune network, meeting the cooperative control of a multi-UUV system under a distributed structure, and realizing the enclosure operation of an enemy target; the method has the characteristics of strong self-organization, fault tolerance, instantaneity and the like, and is convenient for the engineering realization of the underwater unmanned system.)

1. A behavior-based multi-UUV self-organizing coordination control method is characterized by comprising the following steps:

step 1: constructing a mapping relation between the immune network and the multi-UUV system, and defining: the multi-UUV system comprises N UVx_k∈[x₁,x₂,…,x_N]UUV basic behavior as A_i∈[A₁,A₂,A₃]The enemy target comprises M enemy invading moving targets y_h∈[y₁,y₂,…,y_M]；

Step 2: according to a dynamic differential mathematical model of a Farmer unique network, constructing and defining an interaction intensity level C as follows:

in the formula (I), the compound is shown in the specification,indicating that the ith basic behavioral action A is taken_iUVx of k_kInteraction intensity level at time t, C₁Representing the excitation and inhibition level of basic behavior actions among UUV in the multi-UUV system; c₂Representing stimulus between an enemy target and a UUVAn interaction intensity level; c₃Representing the level of random error interference C caused by the error model of the ring environment₃；C₄Representing the natural mortality rate caused by the fact that basic behavior actions do not have interaction;

step 3, calculation C, C₁、C₂、C₃、C₄The discrete state equation realizes the self-organizing coordination control of the multiple UUV based on the immune network, and the specific discrete state equation is as follows:

wherein t represents time, k₂Is a constant used to control the magnitude of the natural mortality probability.

2. The behavior-based multi-UUV self-organizing coordination control method according to claim 1, wherein the specific correspondence manner for constructing the mapping relationship between the immune network and the multi-UUV system in the step 1 is as follows:

the immune network corresponds to the multi-UUV system, the multi-UUV system is analogized into the immune network which is composed of a plurality of UUV intelligent agent models to achieve the aim of capturing enemies;

b lymphocyte corresponds to UUV, and the UUV is analogized into B lymphocyte which is an intelligent agent model with immune calculation function;

the antigen corresponds to an enemy target, and the enemy target is similar to the antigen and is opposite to the enemy target except the UUV system;

the antibody corresponds to the UUV basic behavior action, the UUV is similar to the antibody aiming at the basic behavior action of the enemy target, and the capture of the enemy target is realized through the UUV basic behavior action;

the antigen stimulation corresponds to enemy target invasion stimulation, and the enemy target invasion stimulation is similar to the antigen stimulation and is used as an external incentive for causing the coordinated control of the multi-UUV system;

the antibody concentration corresponds to the interaction strength of basic behavior actions, and the magnitude of the behavior action demand is described;

the antibody excitation corresponds to the enhancement of the interaction strength of the basic behavior action, and indicates that more UUV are needed for coordination and cooperation to complete the trapping task;

the antibody inhibition corresponds to the weakening of the interaction strength of the basic behavior action, which indicates that the UUV has enough capacity to complete the trapping task and inhibit other UUV from coming for assistance, otherwise, resource redundancy is caused;

elimination of the antigen corresponds to a target of the surrounding enemy.

3. The method as claimed in claim 2, wherein the UUV basic behavior in step 1 is action a_iThe method comprises the following steps: navigation patrol A₁Sensing and detecting A₂And trend toward target A₃Wherein the voyage patrol A₁The specific behavioral actions of (a) are: the UUV adopts navigation patrol behavior actions to patrol the underwater enemy target in a distributed structure;

the perception detection A₂The specific behavioral actions of (a) are: in the detection range, the UUV executes the action of sensing detection behavior to discover an enemy target, and sends a detection result to an adjacent UUV in a broadcast mode;

the trend target A₃The specific behavioral actions of (a) are: in the communication range, after receiving the information, the adjacent UUV executes the action of tending to an enemy target to form a capture situation, and the other UUV continues to execute the action of navigation patrol behaviors.

4. The method as claimed in claim 3, wherein the excitation and suppression level C of the basic behavior action between UUV in the multi-UUV system in step 2 is calculated by the following formula (4)₁：

In the formula, c_i(t) and c_j(t) respectively indicating that the kth UUV at the time t takes the ith basic behavior action A_iConcentration and other UUV take the jth basic behavior action A_jOf (2)Mutual strength, i, j ═ 1,2,3, C_dIndicating UUV communication range R_cSet cr of number of UUV in C_d，U_ijAnd the interaction coefficient between the basic behavior action of the kth UUV and the basic behavior actions of other UUV in the communication range is represented.

5. The behavior-based multi-UUV self-organizing coordination control method according to claim 3, wherein the stimulation interaction intensity level C between the enemy target and the UUV in the step 2 is calculated by the following formula (5)₂：

In the formula: c. C_i(t) and y_h(t) represents the concentration of the ith basic action taken by the kth UUV and the concentration of the ith enemy mobile intrusion object at the moment t respectively, i is 1,2,3, h is 1,2, …, and D_h，D_dIndicating UUV detection range R_dSet of antigen numbers within, V_ikAnd the interaction coefficient between the ith basic behavior action of the kth UUV and the ith enemy target is represented.

6. The behavior-based multi-UUV self-organizing coordination control method according to claim 3, wherein the random error interference level C caused by the environmental error model in the step 2 is calculated by establishing a fractional Brownian motion model₃The method specifically comprises the following steps:

in the above formula, t is more than 0, p is more than 1, c_iSubject to a normal distribution with mean 0 and variance 1d_iD is more than or equal to 0_iLess than or equal to 2 pi and subject to uniform distributionH ∈ (0,1) is roughness index.

7. The method of claim 3, wherein the basic behavior A for the kth UUV is action A_iNatural mortality rate C results from the absence of incentives and inhibition of basic behavioral actions with other UUVs₄：

In the above formula, λ represents the natural mortality, and τ represents the compression constant.

Technical Field

The invention relates to the field of modeling and simulation of an underwater unmanned system, in particular to a behavior-based multi-UUV self-organizing coordination control method.

Background

The multi-UUV system is an underwater network which is composed of various isomorphic or heterogeneous UUV unmanned systems and has distributed space, time, events and functions, compared with a single UUV, the multi-UUV system has higher operation efficiency and wider application field, can play various roles of preposition detection, communication nodes, attack weapons, launching platforms and the like in the underwater network, and has become a hotspot for research in civil and military fields in deep open sea strategies in China.

In the military application field, a multi-UUV system is used as a weapon system, an underwater environment and a complex underwater unmanned combat system with mutual coupling and mutual influence of operation missions, and all group decisions such as task allocation, path planning, obstacle avoidance, collaborative search and the like are realized on the basis of high-efficiency coordination control. Therefore, how to design an efficient coordination control model directly influences the quality and effect of completion of underwater combat missions of the multi-UUV system, particularly in the process of capture application of enemy targets.

At present, the coordination control model of the UUVs can be divided into two major categories according to the system structure: centralized and distributed. The centralized UUV coordinated control model must perform unified planning and coordination on the operation tasks through a coordinated control center, and if the sensing information is complete and the computing capability of the coordinated center is ensured, the global optimal solution of the whole planning system can be obtained; although the model is simple and convenient to calculate, the application scenarios are often limited, and the dynamics and the robustness are poor. The distributed coordination control model adopts a bottom-up modeling mechanism, so that the multi-constraint condition can be met, the minimum cost of a coordination process can be ensured, and the distributed coordination control model becomes the key point of the coordination control research of the multi-UUV system at present; the model mainly comprises a behavior-based method, a market theory-based method and a self-organization theory-based method, wherein the self-organization theory-based method is inspired by the structure and the principle of a neural network, the operation environment and information are perceptively mapped into the behavior model, and the path planning of the multi-UUV system is realized.

The immune network is an idiotypic network theory combining the evolutionary processes of immune recognition, immune memory, immune tolerance and the like, and the immune cells in the immune system are considered to be not in an independent state from each other, but form a dynamic balance distributed network structure through mutual excitation and inhibition of antibodies. At present, the immune network has become a source for the development and innovation of the self-organizing theory. Therefore, a behavior-based self-organization coordination control model with remarkable cooperative modeling precision is hopefully developed by means of an immune network theory, and self-organization of a multi-UUV system is realized to complete capture of enemy targets.

Disclosure of Invention

The method aims to solve the problems of the existing coordination control model in the application of a multi-UUV system for capturing enemy targets; the invention provides a behavior-based multi-UUV self-organizing coordination control method.

The technical problem to be solved by the invention is realized by the following technical scheme:

a behavior-based multi-UUV self-organizing coordination control method comprises the following steps:

step 1: constructing a mapping relation between the immune network and the multi-UUV system, and defining: the multi-UUV system comprises N UUV x_k∈[x₁，x₂，…，x_N]UUV basic behavior as A_i∈[A₁，A₂，A₃]The enemy target comprises M enemy invading moving targets y_h∈[y₁，y₂，…，y_M]；

Step 2: according to a dynamic differential mathematical model of a Farmer unique network, constructing and defining an interaction intensity level C as follows:

in the formula (I), the compound is shown in the specification,indicating that the ith basic behavioral action A is taken_iUUV x of kth_kInteraction intensity level at time t, C₁Representing the excitation and inhibition level of basic behavior actions among UUV in the multi-UUV system; c₂Indicating a stimulus interaction intensity level between the enemy target and the UUV; c₃Representing the level of random error interference C caused by the error model of the ring environment₃；C₄Representing the natural mortality rate caused by the fact that basic behavior actions do not have interaction;

and step 3: by calculation C, C₁、C₂、C₃、C₄The discrete state equation realizes the self-organizing coordination control of the multiple UUV based on the immune network, and the specific discrete state equation is as follows:

wherein t represents time, k₂Is a constant used to control the magnitude of the natural mortality probability.

Further, the specific corresponding manner for constructing the mapping relationship between the immune network and the multi-UUV system in step 1 is as follows:

the immune network corresponds to the multi-UUV system, the multi-UUV system is analogized into the immune network which is composed of a plurality of UUV intelligent agent models to achieve the aim of capturing enemies;

b lymphocyte corresponds to UUV, and the UUV is analogized into B lymphocyte which is an intelligent agent model with immune calculation function;

the antigen corresponds to an enemy target, and the enemy target is similar to the antigen and is opposite to the enemy target except the UUV system;

the antibody corresponds to the UUV basic behavior action, the UUV is similar to the antibody aiming at the basic behavior action of the enemy target, and the capture of the enemy target is realized through the UUV basic behavior action;

the antigen stimulation corresponds to enemy target invasion stimulation, and the enemy target invasion stimulation is similar to the antigen stimulation and is used as an external incentive for causing the coordinated control of the multi-UUV system;

the antibody concentration corresponds to the interaction strength of basic behavior actions, and the magnitude of the behavior action demand is described;

the antibody excitation corresponds to the enhancement of the interaction strength of the basic behavior action, and indicates that more UUV are needed for coordination and cooperation to complete the trapping task;

the antibody inhibition corresponds to the weakening of the interaction strength of the basic behavior action, which indicates that the UUV has enough capacity to complete the trapping task and inhibit other UUV from coming for assistance, otherwise, resource redundancy is caused;

elimination of the antigen corresponds to a target of the surrounding enemy.

Further, the UUV basic behavior in step 1 is action a_iThe method comprises the following steps: navigation patrol A₁Sensing and detecting A₂And trend toward target A₃Wherein the voyage patrol A₁The specific behavioral actions of (a) are: UUV sailing in distributed architecturePatrolling the underwater enemy target by the patrolling behavior action;

the perception detection A₂The specific behavioral actions of (a) are: in a detection range, the UUV executes a sensing detection behavior action to discover an enemy target, and sends a detection result to the UUV of an adjacent node in a broadcast mode;

the trend target A₃The specific behavioral actions of (a) are: in the communication range, after receiving the information, the adjacent UUV executes the action of tending to an enemy target to form a capture situation, and the other UUV continues to execute the action of navigation patrol behaviors.

Further, the excitation and inhibition level C of the basic behavior action between UUVs in the multi-UUV system in step 2 is calculated by the following formula (4)₁：

In the formula, c_i(t) and c_j(t) respectively indicating that the kth UUV at the time t takes the ith basic behavior action A_iConcentration and other UUV take the jth basic behavior action A_jI, j ═ 1,2,3, C_dIndicating UUV communication range R_cSet cr of number of UUV in C_d，U_ijAnd the interaction coefficient between the basic behavior action of the kth UUV and the basic behavior actions of other UUV in the communication range is represented.

Further, the stimulation interaction intensity level C between the enemy target and the UUV in the step 2 is calculated by the following formula (5)₂：

In the formula: c. C_i(t) and y_h(t) represents the concentration of the ith basic action taken by the kth UUV and the concentration of the ith enemy mobile intrusion object at the moment t respectively, i is 1,2,3, h is 1,2, …, and D_h，D_dIndicating UUV detection range R_dSet of antigen numbers within, V_ikIndicating the ith basic behavior action of the kth UUVAnd (4) interaction coefficient with the h enemy target.

Further, calculating the random error interference level C caused by the environmental error model in the step 2 by establishing a fractional Brownian motion model₃The method specifically comprises the following steps:

in the above formula, t is more than 0, p is more than 1, c_iSubject to a normal distribution with mean 0 and variance 1d_iD is more than or equal to 0_iLess than or equal to 2 pi and subject to uniform distributionH ∈ (0,1) is roughness index.

Further, the basic behavior for the kth UUV is action A_iNatural mortality rate C results from the absence of incentives and inhibition of basic behavioral actions with other UUVs₄：

In the above formula, λ represents the natural mortality, and τ represents the compression constant.

Compared with the prior art, the invention has the beneficial effects that:

the invention is inspired by immune network, constructs the mapping relation between the immune network and the multi-UUV system, designs a coordination control dynamic model based on basic behavior and action, can meet the cooperative control of the multi-UUV system under a distributed structure, realizes the trapping operation on enemy targets, shows strong self-organization, fault tolerance and real-time performance, and is convenient for the engineering realization of an underwater unmanned system.

Drawings

FIG. 1 is a schematic diagram illustrating a principle of a multi-UUV coordinated control method based on self-organization theory according to the present invention;

FIG. 2 is a schematic diagram of a multi-UUV system navigation patrol when time t is 50 in the embodiment;

fig. 3 is a schematic diagram of a multi-UUV system detecting and sensing an intrusion target when time t is 100 in the embodiment;

fig. 4 is a schematic diagram of a multi-UUV system capturing an enemy intrusion target when time t is 150 in the embodiment;

Detailed Description

The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.

As shown in fig. 1, in order to overcome the problem that the existing coordination control model is used in capturing an enemy target by a multi-UUV system, which is inspired by an immune network, the embodiment provides a behavior-based multi-UUV self-organizing coordination control method, so as to achieve capturing of the enemy target by basic behavior actions of the multi-UUV system under a distributed structure. Firstly, constructing a corresponding relation between an immune system and a multi-UUV system, and designing basic behavior actions (antibodies) such as navigation patrol, perception detection, tendency target and the like; secondly, a coordination control dynamics model based on basic behavior and action is provided, and fractional Brownian motion modeling of operation environment errors is realized; finally, the self-organization coordination control of the multi-UUV based on the immune network in the discrete state is provided, the cooperative control of the multi-UUV system under the distributed structure is met, the capture operation of an enemy target is realized, the strong self-organization, fault tolerance and real-time performance are shown, and the engineering realization of an underwater unmanned system is facilitated.

Before describing the method of the present embodiment in detail, the design concept of the method is described as follows:

in the kinetic differential mathematical model of the Farmer idiotypic immune network, the immune system is assumed to have N antibodies [ x [ ]₁，x₂，…，x_N]M antigens [ y ]₁，y₂，…，y_M]Then antibody x_iExcitation level S of_iAnd the change in concentration s (x)_i) Respectively expressed as:

in the formula (1), the first term in square brackets represents the antibody x_iAnd x_jIn between, corresponding to x_iComponents idiotope and x_jConnection between parts, abbreviated Stimulus1, where M_ijRepresenting the excitation coefficient between the antibodies; the second term denotes antibody x_iAnd x_jInhibition of antibodies in between, corresponding to x_iParts paratope and x_jConnection between components, referred to as application, where M_jiRepresents the inhibition coefficient between antibodies; the third term represents antibody x_jWith antigen y_kIn between, corresponding to x_iParts paratope and y_kThe connection between the components epitope is abbreviated to Stimulus2, wherein W_ikThe excitation coefficient between the antibody and the antigen; in the fourth term λ_iDenotes antibody x_iNatural mortality due to the absence of excitation and inhibition; wherein k is₁And k₂Is a constant and is used primarily to balance the excitation and inhibition between antibodies. The antibody concentration in equation (2) is normalized by the sigmoid compression function.

Based on the design concept, a multi-UUV coordination control method based on a self-organization theory is provided, and the method comprises the following steps:

step 1: constructing a mapping relation between an immune network and a multi-UUV system, and defining the multi-UUV system to comprise N UUV x_k∈[x₁，x₂，…，x_N]UUV basic behavior as A_i∈[A₁，A₂，A₃]The target comprises M enemy invading moving targets y_h∈[y₁，y₂，…，y_M]；

And constructing a corresponding relation between the immune network and the multi-UUV system, as shown in Table 1.

TABLE 1 mapping relationship between immune networks and multi UUV systems

The specific correspondence in table 1: the immune network corresponds to the multi-UUV system, the multi-UUV system is analogized into the immune network which is composed of a plurality of UUV intelligent agent models to achieve the aim of capturing enemies; b lymphocyte corresponds to UUV, and the UUV is analogized into B lymphocyte which is an intelligent agent model with immune calculation function; the antigen corresponds to an enemy target, and the enemy target is similar to the antigen and is opposite to the enemy target except the UUV system; the antibody corresponds to the UUV basic behavior action, the UUV is similar to the antibody aiming at the basic behavior action of the enemy target, and the capture of the enemy target is realized through the UUV basic behavior action; the antigen stimulation corresponds to enemy target invasion stimulation, and the enemy target invasion stimulation is similar to the antigen stimulation and is used as an external incentive for causing the coordinated control of the multi-UUV system; the antibody concentration corresponds to the interaction strength of basic behavior actions, and the magnitude of the behavior action demand is described; the antibody stimulation corresponds to the enhancement of the interaction strength of the basic behavior action, and indicates that more UUV are needed for coordination and cooperation to complete the trapping task; the antibody inhibition corresponds to the weakening of the interaction strength of the basic behavior action, which indicates that the UUV has enough capacity to complete the trapping task and inhibit other UUV from coming for assistance, otherwise, resource redundancy is caused; elimination of the antigen corresponds to a target of the surrounding enemy.

Basic behavior action A_iThe (antibody) definitions and descriptions are shown in table 2 and mainly include: navigation patrol A₁Sensing and detecting A₂And trend toward target A₃。

TABLE 2 basic behavior actions for coordinated control

Step 2: according to a dynamic differential mathematical model of the Farmer unique network, the interaction intensity level C constructed and defined is:

in the formula (I), the compound is shown in the specification,indicating that the ith basic behavioral action A is taken_iThe k thInteraction intensity level at time t, C₁Representing the excitation and inhibition level of basic behavior actions among UUV in the multi-UUV system, and combining Stimulus1 and application into a comprehensive item relative to formula (1); c₂The stimulation interaction strength level between the enemy intrusion object and the UUV is expressed and is equivalent to Stimuus 2 of formula (1); c₃Representing an environmental error model; c₄The same as the formula (2), the natural mortality caused by the fact that the basic behavior action has no interaction relation is shown;

calculating the excitation and inhibition level C of the basic behavior action among the UUV in the multi-UUV system in the step 2 by the following formula (4)₁：

In the formula, c_i(t) and c_j(t) respectively indicating that the kth UUV at the time t takes the ith basic behavior action A_iConcentration and other UUV take the jth basic behavior action A_jI, j ═ 1,2,3, C_dIndicating UUV communication range R_cSet cr of number of UUV in C_d，U_ijAnd the interaction coefficient between the basic behavior action of the kth UUV and the basic behavior actions of other UUV in the communication range is represented.

Calculating the stimulation interaction intensity level C between the enemy target and the UUV in the step 2 through the following formula (5)₂：

In the formula: c. C_i(t) andy_h(t) represents the concentration of the ith basic action taken by the kth UUV and the concentration of the ith enemy mobile intrusion object at the moment t respectively, i is 1,2,3, h is 1,2, …, and D_h，D_dIndicating UUV detection range R_dSet of antigen numbers within, V_ikI basic behavior action A representing the k UUV_iAnd (4) interaction coefficient with the h enemy target.

The environmental error model of this embodiment uses fractional Brownian motion fbm (fractional Brownian motion) to describe the randomness of the error. FBM brownian motion error model proposed by mantelbrot and Wallis in the 60 s of rainfall modeling, which has the following mathematical properties for fractional brownian motion of the randomization function y (t): (1) positive growth: the increments Y (t + σ) -Y (t) follow a normal distribution with a mean of 0 and a standard deviation or variance of σ^1/2(ii) a (2) Independent increments: if t is₁＜t₂＜t₃＜t₄Then increment Y (t)₂-t₁) And an increment of Y (t)₄-t₃) Are independent of each other; (3) time independence: for all σ > 0, the delta Y (t + σ) -Y (t) is independent of t; (4) self-radioactivity: for any u and s, there is P (Y (t + σ) -Y (t) < u) ═ P (Y (s × (t + σ)) -Y (s × t) < s^σu), wherein t > 0. Just because the FBM model has the four characteristics, the FBM randomized Weierstrass function is used for describing the random error interference level C caused by the environment error model₃Calculating the random error interference level C caused by the environmental error model in the step 2 by establishing a fractional Brownian motion model₃Specifically defined as:

in the above formula, t is more than 0, p is more than 1, c_iSubject to a normal distribution with mean 0 and variance 1d_iD is more than or equal to 0_iLess than or equal to 2 pi and subject to uniform distributionH belongs to (0,1) as roughness index, and H belongs to (0,1) for ensuring the reasonability of random error and eliminating large-amplitude fluctuation of variable.

Basic behavior for the kth UUV action A_iNatural mortality rate C results from the absence of incentives and inhibition of basic behavioral actions with other UUVs₄：

In the above formula, λ represents the natural mortality, and τ represents the compression constant.

And step 3: by calculation C, C₁、C₂、C₃、C₄The discrete state equation realizes the self-organizing coordination control of the multiple UUV based on the immune network, and the specific discrete state equation is as follows:

wherein t represents time, k₂Is a constant used to control the magnitude of the natural mortality probability.

One specific implementation example of the method is described in detail as follows:

suppose the simulation is to decide: in the sea area of 10000 × 10000 unit range, taking basic action A by 20 UUV_i＝[A₁，A₂，A₃]The method realizes the enclosure operation on 1 Enemy Target, and the simulation result is shown in fig. 2, fig. 3 and fig. 4, wherein a small circle Agent marks a UUV node, and a large circle marks an Enemy intrusion Target (entity Target).

In fig. 2, when time t is 50, the multi-UUV system navigates the patrol intrusion target, and each UUV continues to execute the basic behavior a₁(ii) a In fig. 2, Agent8 in the UUV system senses an intrusion target at a position (2000, 5000) when time t is 100, and the basic behavior of the intrusion target immediately becomes a₂(ii) a In fig. 2, the situation that the multi-UUV system cooperatively traps enemy targets when the time t is 150 is formed, and all communication is carried outThe UUV performing basic behavior in range immediately becomes A₃The target is captured. In general, the behavior-based multi-UUV self-organizing coordination control method provided by the invention can complete coordination control on enemy target capture through a behavior self-organizing network, and shows strong self-organization, fault tolerance and instantaneity.

The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.