Traditionally, motion planning involved navigating one robot from source to goal for accomplishing a task. Now, tasks mostly require movement of a team of robots to the goal site, requiring a chain of robots to reach the desired goal. While numerous efforts are made in the literature for solving the problems of motion planning of a single robot and collective robot navigation in isolation, this paper fuses the two paradigms to let a chain of robot navigate. Further, this paper uses SLAM to first make a static map using a high-end robot, over which the physical low-sensing robots run. Deliberative Planning uses A* algorithm to plan the path. Reactive planning uses the Potential Field Approach to avoid obstacles and stay as close to the initial path planned as possible. These two algorithms are then merged to provide an algorithm that allows the robot to reach its goal via the shortest path possible while avoiding obstacles. The algorithm is further extended to multiple robots so that one robot is followed by the next robot and so on, thus forming a chain. In order to maintain the robots in a chain form, the Elastic Strip model is used. The algorithm proposed successfully executes the above stated when tested on Amigobot robots in an office environment using a map made by the Pioneer LX robot. The proposed algorithm works well for moving a group of robots in a chain in a mapped environment.

It is a universally approved fact that when the driver is drowsy fatal road accident can occur. Therefore the invention of counter measure device is very crucial in order to avoid accidents which occur due to sleep deprivation of driver. Aim of this paper is to explore permanent solution to collisions caused by drowsy driving. Digital image processing (DIP) is an effective application which playsss a decisive role in road accident caused by drowsy driving. DIP is an efficient research exploration which can be used in many fields. No sooner driver begin to drowsy than this application detect his state of sleepiness and attempts to warn driver. Main objective of this paper is an attempt to exhibit the right design to develop IoT based hardware which is highly advanced device. The proposed device will track down inert state of driver and sends warning signal in order to bring back to alert state. System uses Eye Aspect Ratio (EAR) as input to detect the drowsy state of driver. Whereas the entire system is executed in Raspberry Pi3 it uses web camera to detect eye blink and drowsiness. The next phase is the traffic collision detection system. Here the devices which we use are of two types that is, ultrasonic sensors and touch sensors. The obstacles which may lead to an accident are of many such as, another vehicle with uncontrolled speed or maybe a human obstacle or even it could be a natural obstacle. Here in this system the ultrasonic sensors will continuously observe the distance between vehicles and if any obstacles are found as to cause an accident then the ultrasonic sensor will sense and alarm message will be send by using Raspberry Pi. And hence the upcoming accident could be avoided. But in case, in spite of all these efforts if an accident occurs then the touch sensors will detect the crash and will immediately send message to the emergency number by using GSM and also by using GPS the location of the crash site along with accident message will be send to the nearby medical facility. So that the driver will get all necessary treatment.