This thesis analyzes the kinematical structure of Puma 760 arm and introduces the implementation of image based pick and place application by taking care of the obstacles in the environment. Forward and inverse kinematical solutions of PUMA 760 are carried out. A control software has been developed to calculate both the forward and inverse kinematics solution of this manipulator. The control program enables user to perform both offline programming and real time realization by transmitting the VAL commands (Variable Assembly Language) to the control computer. Using the proposed inverse kinematics solutions, an interactive application is generated on PUMA 760 arm. The picture of the workspace is taken using a fixed camera attached above the robot workspace. The captured image is then processed to find the position and the distribution of all objects in the workspace. The target is differentiated from the obstacles by analyzing some specific properties of all objects, i.e. roundness. After determining the configuration of the workspace, a clustering based search algorithm is executed to find a path to pick the target object and places it to the desired place. The trajectory points in pixel coordinates, are mapped into the robot workspace coordinates by using the camera calibration matrix obtained in the calibration procedure of the robot arm with respect to the attached camera. The required joint angles, to get the end effector of the robot arm to the desired location, are calculated using the Jacobian type inverse kinematics algorithm. The VAL commands are generated and sent to the control computer of PUMA 760 to pick the object and places it to a user defined location.

Background: In computer science, optimization can be defined as finding the most cost-effective or notable achievable performance under certain circumstances, maximizing desired factors, and minimizing undesirable results. Many problems in the real world are continuous, and it isn't easy to find global solutions. However, computer technological development increases the speed of computations [1]. The optimization method, an efficient numerical simulator, and a realistic depiction of physical operations that we intend to describe and optimize for any optimization issue are all interconnected components of the optimization process [2]. Objectives: A literature review on existing optimization algorithms is performed. Ten different benchmark functions are considered and are implemented on the existing chosen algorithms like GA (Genetic Algorithm), ACO (Ant ColonyOptimization) Method, and Plant Intelligence Behaviour optimization algorithm to measure the efficiency of these approaches based on the factors or metrics like CPU Time, Optimality, Accuracy, and Mean Best Standard Deviation. Methods: In this research work, a mixed-method approach is used. A literature review is performed based on the existing optimization algorithms. On the other hand, an experiment is conducted by using ten different benchmark functions with the current optimization algorithms like PSO algorithm, ACO algorithm, GA, and PIBO to measure their efficiency based on the four different factors like CPU Time, Optimality, Accuracy, Mean Best Standard Deviation. This tells us which optimization algorithms perform better. Results: The experiment findings are represented within this section. Using the standard functions on the suggested method and other methods, the various metrics like CPU Time, Optimality, Accuracy, and Mean Best Standard Deviation are considered, and the results are tabulated. Graphs are made using the data obtained. Analysis and Discussion: The research questions are addressed based on the experiment's results that have been conducted. Conclusion: We finally conclude the research by analyzing the existing optimization methods and the algorithms' performance. The PIBO performs much better and can be depicted from the results of the optimal metrics, best mean, standard deviation, and accuracy, and has a significant drawback of CPU Time where its time taken is much higher when compared to the PSO algorithm and almost close to GA and performs much better than ACO algorithm.