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The development of wall climbing robots is relatively recent, beginning with some large scale robots in the early 1990s. Wall climbing robots can be used to gain access to or inspect space that is not easily accessible or dangerous for human operators. The range of applicable fields encompasses industrial processes and inspection, exploration, rescue and monitoring. The smaller robots can be used for surveillance purposes due to their stealthy nature. Larger crawling robots may be used to carry out specific tasks such as sand blasting of ship hulls and blasting and spray painting of large containers such as cylindrical storage tanks used by the chemical, petroleum and nuclear industries. Their flexibility and mobility mean that they can accomplish tasks that would be impossible for more conventional robots. The flexibility of mobility that such robots gain from their ability to move on all surfaces rather than only horizontal ones creates some unique challenges. Broadly, they can be split into three categories: robot design, robot control and motion planning, and environmental mapping and localisation. This thesis examines the first two of these problems. A prototype bipedal robot has been built and a second designed in order to capitalise on the experience gained with the first. An in-depth examination of the motion planning problem has been made and new techniques to tackle this problem have been developed. Such techniques are not limited to applications with wall climbing robots as there is commonality with more traditional fixed manipulators. Finally, the planning techniques were combined with the robot design in a test scenario that validated both the design and the motion planning techniques developed throughout the dissertation.

"When treating highly-infectious diseases such as Ebola, health workers are at high risk of infection during the doffing of Personal Protective Equipment (PPE). This is due to factors such as fatigue, hastiness, and inconsistency in training. The introduction of a semi-autonomous robot doffing assistant has the potential to increase the safety of the doffing procedure by assisting the human during high-risk sub-tasks. The addition of a robot into the procedure introduces the need to transform a purely human task into a sequence of safe and effective human-robot collaborative actions. We take advantage of the fact that the human can do the more intricate motions during the procedure. Since diseases like Ebola can spread through the mucous membranes of the eyes, ears, nose, and mouth our goal is to keep the human’s hands away from his or her face as much as possible. Thus our framework focuses on using the robot to help avoid such human risky motion. As secondary goals, we seek to also minimize the human’s effort and make the robot’s motion intuitive for the human. To address different versions and variants of PPE, we propose a way of segmenting the doffing procedure into a sequence of human and robot actions such that the robot only assists when necessary. Our framework then synthesizes assistive motions for the robot that perform parts of the tasks according to the metrics above. Our experiments on five doffing tasks suggest that the introduction of a robot assistant improves the safety of the procedure in three out of four of the high-risk doffing tasks while reducing effort in all five tasks."

In this work we describe the design and implementation of a general framework for visualizing and editing motion planning environments, problem instances, and their solutions. The motion planning problem consists of finding a valid path between a start and a goal configuration for a movable object. The workspace is, in traditional robotics and animation applications, composed of one or more objects (called obstacles) that cannot overlap with the robot. As even the simplest motion planning problems have been shown to be in- tractable, most practical approaches to motion planning use randomization and/or compute approximate solutions. While the tool we present allows the manipulation and evaluation of planner solutions and the animation of any path found by any plan- ner, it is specialized for a class of randomized planners called probabilistic roadmap methods (PRMs). PRMs are roadmap-based methods that generate a graph or roadmap where the nodes represent collision-free configurations and the edges represent feasible paths between those configurations. PRMs typically consist of two phases: roadmap con- struction, where a roadmap is built, and query, where the start and goal configura- tions are connected to the roadmap and then a path is extracted using graph search techniques.

Search and rescue mobile robots have shown great promise and have been under development by the robotics researchers for many years. They are many locomotion methods for different robotic platforms, including legged, wheeled, flying and hybrid. In general, the environment that these robots would operate in is very hazardous and complicated, where wheeled robots will have difficulty physically traversing and where legged robots would need to spend too much time planning their foot placement. Drawing inspiration from biology, we have noticed that the snake is an animal well-suited to complicated, rubble filled environments. A snake’s body has a very simple structure that nevertheless allows the snake to traverse very complex environments smoothly and flexibly using different locomotion modes. Many researchers have developed different kinds of snake robots, but there is still a big discrepancy between the capabilities of current snake robots and natural snakes. Two aspects of this discrepancy are the rigidity of current snake robots, which limit their physical flexibility, and the current techniques for control and motion planning, which are too complicated to apply to these snake robots without a tremendous amount of computation time and expensive hardware. In order to bridge the gap in flexibility, pneumatic soft robotics is a potential good solution. A soft body can absorb the impact forces during the collisions with obstacles, making soft snake robots suitable for unpredictable environments. However, the incorporation of autonomous control in soft mobile robotics has not been achieved yet. One reason for this is the lack of the embeddable flexible soft body sensor technology and portable power sources that would allow soft robotic systems to meet the essential hardware prerequisites of autonomous systems. The infinite degree of freedom and fluid-dynamic effects inherent of soft pneumatics make these systems difficult in terms of modeling, control, and motion planning: techniques generally required for autonomous systems. This dissertation addresses fundamental challenges of soft robotics modeling, control, and motion planning, as well as the challenge of making an effective soft pneumatic snake platform. In my 5 years of PhD work, I have developed four generations of pressure operated WPI soft robotics snakes (SRS), the fastest of which can travel about 220 mm/s, which is around one body per second. In order to make these soft robots autonomous, I first proposed a mathematical dynamical model for the WPI SRS and verified its accuracy through experimentation. Then I designed and fabricated a curvature sensor to be embedded inside each soft actuator to measure their bending angles. The latest WPI SRS is a modularized system which can be scaled up or down depending on the requirements of the task. I also developed and implemented an algorithm which allows this version of the WPI SRS to correct its own locomotion using iterative learning control. Finally, I developed and tested a motion planning and trajectory following algorithm, which allowed the latest WPI SRS to traverse an obstacle filled environment. Future research will focus on motion planning and control of the WPI SRS in outdoor environments utilizing the camera instead of the tracking system. In addition, it is important to investigate optimal control and motion planning strategies for mobile manipulation tasks where the SRS needs to move and manipulate its environment.. Finally, the future work will include the design, control, and motion planning for a soft snake robot where each segment has two degrees-of-freedom, allowing it to lift itself off the ground and traverse complex-real-world environments.

Limitations imposed on control functions can significantly affect the performance of a linear controller. When applied to the real physical system, such limitations convert a linear function to a nonlinear input signal that alters the convergence or stability of the solution. The main focus of this study is to identify, classify and propose appropriate techniques to overcome such problems. In this regard, we propose an exact definition for a conditionally controllable problem and investigate control function formulations for such problems under the lenses of planning-based and optimization-based methods.

Motion-planning is an essential component of autonomous aerial and terrestrial vehicles. The canonical Motion-planning problem, which is widely studied in the literature, is of planning point-to-point motion while avoiding obstacles. However, the desired degree of vehicular autonomy has steadily risen, and has consequently led to motion-planning problems where a vehicle is required to accomplish a high-level intelligent task, rather than simply move between two points. One way of specifying such intelligent tasks is via linear temporal logic (LTL) formulae. LTL is a formal logic system that includes temporal operators such as always, eventually, and until besides the usual logical operators. For autonomous vehicles, LTL formulae can concisely express tasks such as persistent surveillance, safety requirements, and temporal orders of visits to multiple locations. Recent control theoretic literature has discussed the generation of reference trajectories and/or the synthesis of feedback control laws to enable a vehicle to move in manners that satisfy LTL specifications. A crucial step in such synthesis is the generation of a so-called discrete abstraction of a vehicle kinematic/dynamic model. Typical techniques of generating a discrete abstraction require strong assumptions on controllability and/or linearity. This dissertation discusses fast motion-planning and control techniques to satisfy LTL specifications for vehicle models with nonholonomic kinematic constraints, which do not satisfy the aforesaid assumptions. The main contributions of this dissertation are as follows. First, we present a new technique for constructing discrete abstractions of a Dubins vehicle model (namely, a vehicle that moves forward at a constant speed with a minimum turning radius). This technique relies on the so-called method of lifted graphs and precomputed reachable set calculations. Using this technique, we provide an algorithm to generate vehicle reference trajectories satisfying LTL specifications without requiring complete controllability in the presence of workspace constraints, and without requiring linearity or linearization of the vehicle model. Second, we present a technique for centralized motion-planning for a team of vehicles to collaboratively satisfy a common LTL specification. This technique is also based on the method of lifted graphs. Third, we present an incremental version of the proposed motion-planning techniques, which has an “anytime" property. This property means that a feasible solution is computed quickly, and the iterative updates are made to this solution with a guarantee of convergence to an optimal solution. This version is suited for real-time implementation, where a hard bound on the computation time is imposed. Finally, we present a randomized sampling-based technique for generating reference trajectories that satisfy given LTL specifications. This technique is an alternative to the aforesaid technique based on lifted graphs. We illustrate the proposed techniques using numerical simulation examples. We demonstrate the superiority of the proposed techniques in comparison to the existing literature in terms of computational time and memory requirements.

The purpose of the thesis was to explore the possibilities of using a Level-Set method to design a time-optimal path planar of a subject to direction-dependent maximum velocities. A promising application for such a planning approach lies in sailboat navigation planning, because of the dynamic ocean waves, current, wind and the characteristics of a sailboat. In the thesis, we developed an IOS application to simulate such scenario as environment properties with wind, static obstacles and the sailboat mapped into direction-dependent velocities in different locations of the environment.  Considering the wind is the main power for the sailboat, a wind speed generation function was created, based on different locations. The Level-Set method is widely used in image processing because of its various advantages, for instance, the ability to deal with topology change and stability. It also can be applied in path planning, in which the process of the Level-Set method can be considered as a continuous wave front propagating with a speed from the start location. A grid-based map was used to represent the environment. While the wave front was crossing the cell on the grid, a time was recorded for every cell, following the negative gradient direction of such crossing time, and then an optimal path could be found. In addition, we used the Narrow Band method to speed up the calculation of processing the level set equation. Finally, this report gives the results of the experiments of static obstacle avoidance, wind effects and smooth path planning.

Online computation is a model for formulating decision making under uncertainty. In an online problem, the algorithm does not know the entire input from the beginning; the input is revealed in a sequence of steps. At each step, the algorithm should make its decisions based on the past and without any knowledge about the future. Many important real-life problems such as robot navigation are intrinsically online and thus the design and analysis of online algorithms is one of the main research areas in theoretical computer science. Competitive analysis is the standard measure for analysis of online algorithms. It has been applied to many online problems in diverse areas ranging from robot navigation, to network routing, to scheduling, to online graph coloring. In this thesis, we first survey three classic online problems, namely the cow-path problem, the Processor-Allocation problem and the Robots-Search-Rays problem and highlight connections between them. Second, the main result is for the One-Robot-Searches-Two-Rays problem for which we consider the weighted scenario, in which the robot is located on a ray with a preferential probability p. We term the One-Robot-Searches-Two-Rays-And-Weighted problem as 1-STRAW (and in general k-STRAW for k searchers). In the 1-STRAW problem, we propose a search strategy which is optimal among weighted geometric states. In addition, we prove a tight lower bound of the worst case competitive ratio and conjecture a lower bound of the average case competitive ratio for the 1-STRAW problem. Additionally, we compare our search strategy and its performance with the doubling strategy and the SmartCow algorithm.

Online computation is a model for formulating decision making under uncertainty. In an online problem, the algorithm does not know the entire input from the beginning; the input is revealed in a sequence of steps. At each step, the algorithm should make its decisions based on the past and without any knowledge about the future. Many important real-life problems such as robot navigation are intrinsically online and thus the design and analysis of online algorithms is one of the main research areas in theoretical computer science. Competitive analysis is the standard measure for analysis of online algorithms. It has been applied to many online problems in diverse areas ranging from robot navigation, to network routing, to scheduling, to online graph coloring. In this thesis, we first survey three classic online problems, namely the cow-path problem, the Processor-Allocation problem and the Robots-Search-Rays problem and highlight connections between them. Second, the main result is for the One-Robot-Searches-Two-Rays problem for which we consider the weighted scenario, in which the robot is located on a ray with a preferential probability p. We term the One-Robot-Searches-Two-Rays-And-Weighted problem as 1-STRAW (and in general k-STRAW for k searchers). In the 1-STRAW problem, we propose a search strategy which is optimal among weighted geometric states. In addition, we prove a tight lower bound of the worst case competitive ratio and conjecture a lower bound of the average case competitive ratio for the 1-STRAW problem. Additionally, we compare our search strategy and its performance with the doubling strategy and the SmartCow algorithm.