M.Ing. / The objective of this research is to present motion planning methods for an autonomous robot. Motion planning is one of the most important issues in robotics. The goal of motion planning is to find a path from a starting position to a goal position while avoiding obstacles in the environment. The robot's environment can be static or dynamic. Motion planning problems can be addressed using either classical approaches or obstacle-avoidance approaches. The classical approaches discussed in this work are: Voronoi, Visibility graph, Cell decomposition and Potential field. The obstacle avoidance approaches discussed in this research are: Neural network, Bug Algorithms, Dynamic Window Approach, Vector field histogram, Bubble band technique and Curvature velocity techniques. In this dissertation, simulation results and experimental results are presented. In the simulation, we address the motion planning issues using points extracted from a map. Algorithms used for simulation are: Voronoi algorithm, Hopfield neural network, Potential field and A* search algorithm. The simulation results show that the approaches used are effective and can be applied to real robots to solve motion planning problems. In the experiment, the Dynamic Window Approach (DWA) is used for obstacle-avoidance, a Pioneer robot explores the environment using an open source system, ROS (Robot Operating System). The experiment proved that DWA can be used to avoid obstacles in real time. keywords Motion planning, autonomous robot, optimal path problems, environment, search algorithm, classical approaches, obstacle avoidance approaches, exploration.

The objective of this thesis is the algorithmic enhancement and initial evaluation of an underwater acoustic positioning system which is designed to determine the position and orientation of a mobile nuclear reactor vessel inspection robot. Although a great deal of research has been done in the area of underwater acoustic positioning, this work differs from previous work in three significant ways. First, most applied acoustic positioning systems have been designed for the offshore oil drilling industry, and thus their requirements and restrictions are dictated by an oceanic environment. Second, most previous work has focused only upon acquiring the position of a point from the acoustic system. The inspection robot operation requires accurate positioning and orientation. Finally, the accuracy of acoustic positioning systems is generally dependent upon an evaluation of the speed of sound. However, this parameter is highly dependent upon water temperature. As will be discussed, the reactor vessel water temperature may not be uniform or constant, which makes the design of a precise positioning system difficult. Original methods to overcome this obstacle are discussed and evaluated. Also examined are configurations and constraints of the acoustic transceivers, the numerical solution procedures utilized, and the resulting errors associated with the developed methods. / Master of Science

Tang Wai Sum. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 68-70). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Redundant Manipulators --- p.1 / Chapter 1.2 --- Inverse Kinematics of Robotic Manipulators --- p.2 / Chapter 1.3 --- Inverse Dynamics of Robotic Manipulators --- p.4 / Chapter 1.4 --- Redundancy Resolutions of Manipulators --- p.5 / Chapter 1.5 --- Motivation of Using Neural Networks for these Applications --- p.9 / Chapter 1.6 --- Previous Work for Redundant Manipulator Inverse Kinematics and Inverse Dynamics Computation by Neural Networks --- p.9 / Chapter 1.7 --- Advantages of the Proposed Recurrent Neural Networks --- p.11 / Chapter 1.8 --- Contribution of this work --- p.11 / Chapter 1.9 --- Organization of this thesis --- p.12 / Chapter 2 --- Problem Formulations --- p.14 / Chapter 2.1 --- Constrained Optimization Problems for Inverse Kinematics Com- putation of Redundant Manipulators --- p.14 / Chapter 2.1.1 --- Primal and Dual Quadratic Programs for Bounded Joint Velocity Minimization --- p.14 / Chapter 2.1.2 --- Primal and Dual Linear Programs for Infinity-norm Joint Velocity Minimization --- p.15 / Chapter 2.2 --- Constrained Optimization Problems for Inverse Dynamics Com- putation of Redundant Manipulators --- p.17 / Chapter 2.2.1 --- Quadratic Program for Unbounded Joint Torque Mini- mization --- p.17 / Chapter 2.2.2 --- Primal and Dual Quadratic Programs for Bounded Joint Torque Minimization --- p.18 / Chapter 2.2.3 --- Primal and Dual Linear Programs for Infinity-norm Joint Torque Minimization --- p.19 / Chapter 3 --- Proposed Recurrent Neural Networks --- p.20 / Chapter 3.1 --- The Lagrangian Network --- p.21 / Chapter 3.1.1 --- Optimality Conditions for Unbounded Joint Torque Min- imization --- p.21 / Chapter 3.1.2 --- Dynamical Equations and Architecture --- p.22 / Chapter 3.2 --- The Primal-Dual Network 1 --- p.24 / Chapter 3.2.1 --- Optimality Conditions for Bounded Joint Velocity Min- imization --- p.24 / Chapter 3.2.2 --- Dynamical Equations and Architecture for Bounded Joint Velocity Minimization --- p.26 / Chapter 3.2.3 --- Optimality Conditions for Bounded Joint Torque Mini- mization --- p.27 / Chapter 3.2.4 --- Dynamical Equations and Architecture for Bounded Joint Torque Minimization --- p.28 / Chapter 3.3 --- The Primal-Dual Network 2 --- p.29 / Chapter 3.3.1 --- Energy Function for Infinity-norm Joint Velocity Mini- mization Problem --- p.29 / Chapter 3.3.2 --- Dynamical Equations for Infinity-norm Joint Velocity Minimization --- p.30 / Chapter 3.3.3 --- Energy Functions for Infinity-norm Joint Torque Mini- mization Problem --- p.32 / Chapter 3.3.4 --- Dynamical Equations for Infinity-norm Joint Torque Min- imization --- p.32 / Chapter 3.4 --- Selection of the Positive Scaling Constant --- p.33 / Chapter 4 --- Stability Analysis of Neural Networks --- p.36 / Chapter 4.1 --- The Lagrangian Network --- p.36 / Chapter 4.2 --- The Primal-Dual Network 1 --- p.38 / Chapter 4.3 --- The Primal-Dual Network 2 --- p.41 / Chapter 5 --- Simulation Results and Network Complexity --- p.45 / Chapter 5.1 --- Simulation Results of Inverse Kinematics Computation in Re- dundant Manipulators --- p.45 / Chapter 5.1.1 --- Bounded Least Squares Joint Velocities Computation Using the Primal-Dual Network 1 --- p.46 / Chapter 5.1.2 --- Minimum Infinity-norm Joint Velocities Computation Us- ing the Primal-Dual Network 2 --- p.49 / Chapter 5.2 --- Simulation Results of Inverse Dynamics Computation in Redun- dant Manipulators --- p.51 / Chapter 5.2.1 --- Minimum Unbounded Joint Torques Computation Using the Lagrangian Network --- p.54 / Chapter 5.2.2 --- Minimum Bounded Joint Torques Computation Using the Primal-Dual Network 1 --- p.57 / Chapter 5.2.3 --- Minimum Infinity-norm Joint Torques Computation Us- ing the Primal-Dual Network 2 --- p.59 / Chapter 5.3 --- Network Complexity Analysis --- p.60 / Chapter 6 --- Concluding Remarks and Future Work --- p.64 / Publications Resulted from the Study --- p.66 / Bibliography --- p.68

Recent work has shown humanoid robots with spinal columns, instead of rigid torsos, benefit from both better balance and an increased ability to absorb external impact. Similarly, snake robots have shown promise as a viable option for exploration in confined spaces with limited human access, such as during power plant maintenance. Both spines and snakes, as well as hyper-redundant manipulators, can simplify to a model of a system with multiple links. The multi-link inverted pendulum is a well known benchmark problem in control systems due to its ability to accommodate varying model complexity. Such a system is useful for testing new learning algorithms or laying the foundation for autonomous control of more complex devices such as robotic spines and multi-segmented arms which currently use traditional control methods or are operated by humans. It is often easy to view these systems as single-agent learners due to the high level of interaction among the segments. However, as the number of links in the system increases, the system becomes harder to control. This work replaces the centralized learner with a team of coevolved agents. The use of a multiagent approach allows for control of larger systems. The addition of transfer learning not only increases the learning rate, but also enables the training of larger teams which were previously infeasible due to extended training times. The results presented support these claims by examining neuro-evolutionary control of 3-, 6-, and 12-link systems with nominal conditions as well as with sensor noise, actuator noise, and the addition of more complex physics. / Graduation date: 2012

Successful development of mechatronic systems requires a combination of targeted hardware and software design. The compact rescue robot (CRR), a quadruped pneumatically-actuated walking robot that seeks to use the benefits garnered from pneumatic power, is a prime example of such a system. This thesis discusses the development and testing of a simulation that will aid in further design and development of the CRR by enabling users to examine the impacts of pneumatic actuation on a walking robot. However, development of an entirely new dynamic simulation specific to the system is not practical. Instead, the simulation combines a MATLAB/Simulink actuator simulation with a readily available C++ dynamics library. This multi-platform approach results in additional incurred challenges due to the transfer of data between the platforms. As a result, the system developed here is designed in the fashion that provides the best balance of realistic behavior, model integrity, and practicality. An analytically derived actuator model is developed using classical fluid circuit modeling together with nonlinear area and pressure curves to model the valve and a Stribeck-Tanh model to characterize the effects of friction on the cylinder. The valve model is designed in Simulink and validated on a single degree-of-freedom test rig. This actuator model is then interfaced with SrLib, a dynamics library that computes dynamics of the robot and interactions with the environment, and validated through comparisons with a CRR prototype. Conclusions are focused on the final composition of the simulation, its performance and limitations, and the benefits it offers to the system as a whole.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis proposes an automated grid-based alignment system utilizing lasers and an array of light-detecting photodiodes. The intent is to create an inexpensive and scalable alignment system for pick-and-place robotic systems. The system utilizes the transformation matrix, geometry, and trigonometry to determine the movements to align the robot with a grid-based array of photodiodes. The alignment system consists of a sending unit utilizing lasers, a receiving module consisting of photodiodes, a data acquisition unit, a computer-based control system, and the robot being aligned. The control system computes the robot movements needed to position the lasers based on the laser positions detected by the photodiodes. A transformation matrix converts movements from the coordinate system of the grid formed by the photodiodes to the coordinate system of the robot. The photodiode grid can detect a single laser spot and move it to any part of the grid, or it can detect up to four laser spots and use their relative positions to determine rotational misalignment of the robot. Testing the alignment consists of detecting the position of a single laser at individual points in a distinct pattern on the grid array of photodiodes, and running the entire alignment process multiple times starting with different misalignment cases. The first test provides a measure of the position detection accuracy of the system, while the second test demonstrates the alignment accuracy and repeatability of the system. The system detects the position of a single laser or multiple lasers by using a method similar to a center-of-gravity calculation. The intensity of each photodiode is multiplied by the X-position of that photodiode. The summed result from each photodiode intensity and position product is divided by the summed value of all of the photodiode intensities to get the X-position of the laser. The same thing is done with the Y-values to get the Y-position of the laser. Results show that with this method the system can read a single laser position value with a resolution of 0.1mm, and with a maximum X-error of 2.9mm and Y-error of 2.0mm. It takes approximately 1.5 seconds to process the reading. The alignment procedure calculates the initial misalignment between the robot and the grid of photodiodes by moving the robot to two distinct points along the robot’s X-axis so that only one laser is over the grid. Using these two detected points, a movement trajectory is generated to move that laser to the X = 0, Y = 0 position on the grid. In the process, this moves the other three lasers over the grid, allowing the system to detect the positions of four lasers and uses the positions to determine the rotational and translational offset needed to align the lasers to the grid of photodiodes. This step is run in a feedback loop to update the adjustment until it is within a permissible error value. The desired result for the complete alignment is a robot manipulator positioning within ±0.5mm along the X and Y-axes. The system shows a maximum error of 0.2mm in the X-direction and 0.5mm in the Y-direction with a run-time of approximately 4 to 5 minutes per alignment. If the permissible error value of the final alignment is tripled the alignment time goes down to 1 to 1.5 minutes and the maximum error goes up to 1.4mm in both the X and Y-directions. The run time of the alignment decreases because the system runs fewer alignment iterations.