Orienting Deformable Polygonal Parts without Sensors

Parts orienting is an important part of automated manufacturing. Sensorless manipulation has proven to be a useful paradigm in addressing parts orienting, and the manipulation of deformable objects is a growing area of interest. Until now, these areas have remained separate because existing orienting approaches utilize forces that if applied to deformable parts violate the assumptions used by existing algorithms, and could potentially break the part. We introduce a new algorithm and manipulator actions that, when provided with the geometric description and a deformation model of choice for the part, exploits the deformation and generates a Plan that consists of the shortest sequence of manipulator actions guaranteed to orient the part up to symmetry from any unknown initial orientation and pose. Additionally, the algorithm estimates whether a given manipulator is sufficiently precise to perform the actions which guarantee the final orientation. This is dictated by the particular part geometry, deformation model, and the manipulator action path planner which contains simple end-effector constraints and any standard motion planner. We illustrate the success of the algorithm with multiple parts through 192 trials of experiments that were performed with low-precision robot manipulators and six parts made of four types of materials. The experimental trials resulted in 154 successes, which show the feasibility of deformable parts orienting. The analysis of the failures showed that for success the assumptions of zero friction are essential for this work, increased manipulator precision would be beneficial but not necessary, and a simple deformation model can be sufficient. Finally, we note that the algorithm has applications to truly sensorless manipulation of non-deformable parts

Algorithmic Robot Design: Label Maps, Procrustean Graphs, and the Boundary of Non-Destructiveness

This dissertation is focused on the problem of algorithmic robot design. The process of designing a robot or a team of robots that can reliably accomplish a task in an environment requires several key elements. How the problem is formulated can play a big role in the design process. The ability of the model to correctly reflect the environment, the events, and different pieces of the problem is crucial. Another key element is the ability of the model to show the relationship between different designs of a single system. These two elements can enable design algorithms to navigate through the space of all possible designs, and find a set of solutions. In this dissertation, we introduce procrustean graphs, a model for encoding the robot-environment interactions. We also provide a model for navigating through the space of all possible designs, called label maps. Using these models, we focus on answering the following questions: What degradations to the set of sensors or actuators of a robotic system can be tolerated? How different degradations affect the cost of doing a given task? What sets of resources — that is, sensors and actuators — are minimal for accomplishing a specific given job? And how to find such a set? To this end, our general approach is to sample, using a variety of sampling methods, over the space of all maps for a given problem, and use different techniques for answering these questions. We use decision tree classifiers to determine the crucial sensors and actuators required for a robotic system to accomplish its job. We present an algorithm based on space bisection to find the boundary between the feasible and infeasible subspaces of possible designs. We present an algorithm to measure the cost of doing a given task, and another algorithm to find the relationship between different degradation of a robotic system and the cost of doing the task. In all these solutions, we use a variety of techniques to scale up each approach to enable it to solve real world problems. Our experiments show the efficiency of the presented approach

Algorithms for Robot Coverage Under Movement and Sensing Constraints

This thesis explores the problem of generating coverage paths—that is, paths that pass within some sensor footprint of every point in an environment—for mobile robots. It both considers models for which navigation is a solved problem but motions are constrained, as well for models in which navigation must be considered along with coverage planning because of the robot’s unreliable sensing and movements. The motion constraint we adopt for the former is a common constraint, that of a Dubins vehicle. We extend previous work that solves this coverage problem as a traveling salesman problem (TSP) by introducing a practical heuristic algorithm to reduce runtime while maintaining near-optimal path length. Furthermore, we show that generating an optimal coverage path is NP-hard by reducing from the Exact Cover problem, which provides justification for our algorithm’s conversion of Dubins coverage instances to TSP instances. Extensive experiments demonstrate that the algorithm does indeed produce path lengths comparable to optimal in significantly less time. In the second model, we consider the problem of coverage planning for a particular type of very simple mobile robot. The robot must be able to translate in a commanded direction (specified in a global reference frame), with bounded error on the motion direction, until reaching the environment boundary. The objective, for a given environment map, is to generate a sequence of motions that is guaranteed to cover as large a portion of that environment as possible, in spite of the severe limits on the robot’s sensing and actuation abilities. We show how to model the knowledge available to this kind of robot about its own position within the environment, show how to compute the region whose coverage can be guaranteed for a given plan, and characterize regions whose coverage cannot be guaranteed by any plan. We also describe an algorithm that generates coverage plans for this robot, based on a search across a specially-constructed graph. Simulation results demonstrate the effectiveness of the approach