Robots are utilized in activities such as mine detection, search and rescue, etc. Especially where the environment is dangerous or search time can be optimized, etc. As the cost of robots reduced and application of robotic systems grew, researchers started working on distributed robotic systems. However, creating a robust, flexible and scalable system of collaborating mobile robots that can efficiently perform the search task has been a challenging issue for roboticists. Biologists observed that animal interactions in their societies create a collective behavior that helps them in foraging, group defending and other activities. They show that each animal in its society has a simple role but the collection of these behaviors enables them to accomplish a complex task. Moreover, swarm behavior has interesting properties such as robustness, flexibility and scalability. Based on these studies, researchers have introduced new optimization methods. Roboticists utilize these optimization methods to provide important properties of swarm behavior. PSO is one of the successful optimization methods in this area. Initially, PSO performs well in exploring different search regions, however, in some cases the method doesn't exploit well in promising regions which increases the search time. In this study, mobile robots are deployed to find a target in the search space. Robots interact with each other and move around the search space using PSO algorithm. In addition, the MPSO algorithm is introduced to create an efficient balance between exploration and exploitation of the PSO algorithm. Results show that MPSO reduces search time and minimizes region revisiting.

Solving open-ended problems with paper and pencil is an important part of education and constitutes a large share of course workload, especially in science and engineering programs. However, grading this kind of work can be prohibitively expensive in large classes. Because of the complexity of handwritten free-form work, there are no existing computational methods capable of interpreting it. In this dissertation, we aim to work toward complete interpretation and semantic analysis of handwritten free-form solutions to homework and exam problems of the sort assigned in undergraduate engineering and science courses. In our study, students wrote their homework and exam solutions on dot patterned paper with Livescribe smartpens. Our work comprises three innovations. First, we developed methods to locate final answers. Our methods locate answers by identifying the boxes drawn around them. Experiments demonstrated that our methods are both accurate and efficient at recognizing answer boxes. Second, we developed a novel CNN-BLSTM-CRF network for semantic labeling of students' handwritten assignments. Semantic labeling is the task of classifying pen strokes according to the type of content they represent. Our method distinguishes between cross-out strokes, equation strokes, and free body diagram strokes. The input to our network is a set of pen strokes, which are sorted in chronological order, and the output is a sequence of labels for the strokes. Labeling strokes in this manner is an important step in enabling the semantic analysis of the writing. Our labeling approach outperforms existing methods. Finally, we developed a novel GRU-CRF network to locate complete mathematical equations. The network exploits the temporal context of consecutive strokes and simultaneously finds and groups equation strokes. Once our method has located the equations, they can be interpreted using existing techniques. Together, this work provides a significant step toward the automated grading of handwritten free-form course work.