In this thesis, a strategy to accomplish pick-and-place operations using a six degree-of-freedom (DOF) robotic arm attached to a wheeled mobile robot is presented. This research work is part of a bigger project in developing a robotic-assisted nursing to be used in medical settings. The significance of this project relies on the increasing demand for elderly and disabled skilled care assistance which nowadays has become insufficient. Strong efforts have been made to incorporate technology to fulfill these needs. Several methods were implemented to make a 6-DOF manipulator capable of performing pick-and-place operations. Some of these methods were used to achieve specific tasks such as: solving the inverse kinematics problem, or planning a collision-free path. Other methods, such as forward kinematics description, workspace evaluation, and dexterity analysis, were used to describe the manipulator and its capabilities. The manipulator was accurately described by obtaining the link transformation matrices from each joint using the Denavit-Hartenberg (DH) notations. An Iterative Inverse Kinematics method (IIK) was used to find multiple configurations for the manipulator along a given path. The IIK method was based on the specific geometric characteristic of the manipulator, in which several joints share a common plane. To find admissible solutions along the path, the workspace of the manipulator was considered. Algebraic formulations to obtain the specific workspace of the 6-DOF manipulator on the Cartesian coordinate space were derived from the singular configurations of the manipulator. Local dexterity analysis was also required to identify possible orientations of the end-effector for specific Cartesian coordinate positions. The closed-form expressions for the range of such orientations were derived by adapting an existing dexterity method. Two methods were implemented to plan the free-collision path needed to move an object from one place to another without colliding with an obstacle. Via-points were added to avoid the robot mobile platform and the zones in which the manipulator presented motion difficulties. Finally, the segments located between initial, final, and via-points positions, were connected using straight lines forming a global path. To form the collision-free path, the straight-line were modified to avoid the obstacles that intersected the path. The effectiveness of the proposed analysis was verified by comparing simulation and experimental results. Three predefined paths were used to evaluate the IIK method. Ten different scenarios with different number and pattern of obstacles were used to verify the efficiency of the entire path planning algorithm. Overall results confirmed the efficiency of the implemented methods for performing pick-and-place operations with a 6-DOF manipulator.

Industrial works in the sea, such as renewable energy or oil industries are dependent on underwater repairs which on great depths needs to be unmanned. Since the ability to sense and control applied forces is imperative to minimize damage on company property, the goal for this master thesis was toinvestigate and develop different force feedback control algorithms compatible with the Saab Seaeye eM1-7 electric manipulator, utilizing a 6-DOF load cell. This thesis addressed the conditions for the force feedback control and the behaviour of the control strategies in MATLAB and Gazebo. Tests with the manipulator concluded that force feedback control is possible with the suggested strategies but that the kinematic control needs further development in order to conduct simulations of assembly tasks which requires collaboration between the force feedback and kinematic control objectives.

This project investigated the implementation and evaluation of various collision-free path planning algorithms for the Saab Seaeye eM1-7 6-DOF Electric Manipulator (eManip). The primary goal was to enhance the autonomous performance of the eManip by integrating efficient path planning methodologies, ultimately ensuring the avoidance of collisions and manipulator singularities during underwater operations. Key algorithms examined included the Rapidly-exploring Random Trees (RRT) algorithm and its enhanced variants. Through simulation tests in MATLAB and Gazebo, metrics such as planning time, path length, and the number of explored nodes were evaluated. The results highlighted the robustness of Goal-biased and Bidirectional RRT* (Gb-Bd-RRT*), which consistently performed well across various environments. The research also highlighted the correlation between algorithm effectiveness and specific task attributes, emphasizing their adaptability to complex environments. This research contributes valuable insights into the effectiveness of path planning algorithms, informing the selection and integration of viable strategies for 6-DOF robotic manipulators.