be removed. Antigens are defective coding on the cell surface that appears soon after the infection of a cell by an infectious agent, but before replication has begun. Epitopes, which are patterns, present on the surface of the antigen are used by the antibody to detect if they constitute a potential threat to the body. When the Paratope of an antibody matches the Epitope of the antigen, a reaction to suppress the antigen is initiated. In case the match is not exact, the antibody undergoes a process called somatic hypermutation [6], a controlled version of mutation, to set it right. The immune system is unique, robust, autonomous and multi-layered. It is augmented with a distributed learning mechanism having lasting memory [7]. This shows the overall functioning of the immune system. The immune system recognizes the antigens and the antigenic patterns are identified. On identification of an antigenic pattern, the B cells communicate the information in parallel to each other by means of paratopes and idiotopes in the network [3]. 

## 3. **Artificial Immune System** The Immune System Is Highly Complicated And Appears To Be

precisely tuned to the problem of detecting and eliminating infections. It is believed that it also provides a compelling example of a distributed information-processing system, one which we can study for the purpose of designing better artificial adaptive systems [3], [4]. AIS uses the concepts of natural Immune System to improve the computational techniques and in this paper an attempt is made to provide mechanisms to prevent failure in a multi-robot environment. 

Every autonomous robot works on limited power and it is drained as the robot works on its course. Using the AIS 
concept the loss of energy or power from the robot is viewed as an antigen and immune concepts are incorporated to ensure that such antigen do not disrupt the normal functioning of any of the robots. 

When more then one bot work autonomously in an environment, it is made sure that none of the bot is allowed to lose its power completely making it non functional. If any one of the bot has weak battery strength that is below the threshold value and is waiting in a long queue to recharge itself, then it may stop functioning completely by it is allowed to be recharged. In order to avoid such eventualities and making one of the robot invalid this simulation allows such robots with critical threshold values to jump the queue to charge immediately ignoring the normal rules of the queue. If such critical cases are not encountered the robots follows the normal characteristics of a queue, thereby ensuring that no robots are allowed to be invalid and completes the assigned task. This suggested technique makes a multi-robot or multiagent scenario more robust and consistent ensuring the completion of a desired goal. 

## 4. **Environment Description**

Every bot traverses its environment to do a set job. Considering an Environment(E) being divided into n sub environments g(En) where n is the no of the environment (n = 
1, 2, 3, 4….n).The work done by each of the bot is w(En).So by summing up the work done by a bot on a sub environment gives the work done on an environment. 

W(E) =w(E1)g(E1) + w(E2)g(E2) + w(E3)g(E3) + w(E4)g(E4) + 
… 
 + w(En)g(En) 

$+\mathrm{w}(\mathrm{E}_{\mathrm{n}})\mathrm{g}(\mathrm{E}_{\mathrm{n}})$. 

## 4.1 **Working Description**

The algorithm followed is normal FCFS. When there are lot of variables waiting in a queue then an algorithm is followed in which the first variable to have entered the waiting queue will be the first one to come out of the queue. Whenever the battery is running low then the bot stores its present position and moves towards its docking point. On reaching its docking point the bot check s for the charger. If the charger is empty it moves to the charger otherwise it enters in the queue while waiting for its turn to avail the charger .in this case it follows FCFS. In case the bot is running very low and is towards the rear of the queue then it is made to jump to the top of the queue, hence being the first to avail the charger in the queue. This way the bots are saved from complete failure due to the lack of battery charge. After the charging is complete the bot moves back the position where it had encountered its low power problem. It then resumes its previous work that it was doing. 

4.1.1 *Simulation Workspace* There are four Autonomous Bot working in different sub environment. Each bot starts from any random position & 
battery strength. Once the bot reaches the red mark it moves back to the green mark in a straight line. There is only one charger shared by all the four bot. Each bot has a unique docking (waiting) station in case the charger is in use. 

The following are the battery strength indicator ![0_image_0.png](0_image_0.png)

The environment considering (n=4) where n is the no of bots and which is also the no of sub environments is described from Fig 1 through Fig 11. 