An Efficient Multi-solution Solver for the Inverse Kinematics of 3-Section Constant-Curvature Robots

Piecewise constant curvature is a popular kinematics framework for continuum robots. Computing the model parameters from the desired end pose, known as the inverse kinematics problem, is fundamental in manipulation, tracking and planning tasks. In this paper, we propose an efficient multi-solution solver to address the inverse kinematics problem of 3-section constant-curvature robots by bridging both the theoretical reduction and numerical correction. We derive analytical conditions to simplify the original problem into a one-dimensional problem. Further, the equivalence of the two problems is formalised. In addition, we introduce an approximation with bounded error so that the one dimension becomes traversable while the remaining parameters analytically solvable. With the theoretical results, the global search and numerical correction are employed to implement the solver. The experiments validate the better efficiency and higher success rate of our solver than the numerical methods when one solution is required, and demonstrate the ability of obtaining multiple solutions with optimal path planning in a space with obstacles.Comment: Robotics: Science and Systems 202

A Robotic System for Learning Visually-Driven Grasp Planning (Dissertation Proposal)

We use findings in machine learning, developmental psychology, and neurophysiology to guide a robotic learning system\u27s level of representation both for actions and for percepts. Visually-driven grasping is chosen as the experimental task since it has general applicability and it has been extensively researched from several perspectives. An implementation of a robotic system with a gripper, compliant instrumented wrist, arm and vision is used to test these ideas. Several sensorimotor primitives (vision segmentation and manipulatory reflexes) are implemented in this system and may be thought of as the innate perceptual and motor abilities of the system. Applying empirical learning techniques to real situations brings up such important issues as observation sparsity in high-dimensional spaces, arbitrary underlying functional forms of the reinforcement distribution and robustness to noise in exemplars. The well-established technique of non-parametric projection pursuit regression (PPR) is used to accomplish reinforcement learning by searching for projections of high-dimensional data sets that capture task invariants. We also pursue the following problem: how can we use human expertise and insight into grasping to train a system to select both appropriate hand preshapes and approaches for a wide variety of objects, and then have it verify and refine its skills through trial and error. To accomplish this learning we propose a new class of Density Adaptive reinforcement learning algorithms. These algorithms use statistical tests to identify possibly interesting regions of the attribute space in which the dynamics of the task change. They automatically concentrate the building of high resolution descriptions of the reinforcement in those areas, and build low resolution representations in regions that are either not populated in the given task or are highly uniform in outcome. Additionally, the use of any learning process generally implies failures along the way. Therefore, the mechanics of the untrained robotic system must be able to tolerate mistakes during learning and not damage itself. We address this by the use of an instrumented, compliant robot wrist that controls impact forces

Fabric-based eversion type soft actuators for robotic grasping applications

Humans have managed to simplify their lives by using robots to automate dull and repetitive tasks. Traditional robots have been very helpful in this respect, but in certain applications, the complexity of manufacturing and the requisite control strategies have rendered these systems inadequate. The concept of robots made of soft materials has increasingly been explored and a new avenue of research has opened up within the robotics community. In terms of construction, robots made of soft and flexible materials have several advantages over their rigid-bodied counterparts, among them simple design, simple control mechanisms, inexpensive constituent materials and the fact that they can be easily integrated into existing systems. Soft grippers in particular have been the subject of extensive research and we have witnessed significant development in terms of attributes like grasping, payload and sensing methodologies. Progress has been enhanced by the development of new materials used in the construction of actuators or end effectors of the grippers. The use of lightweight, non-stretch fabrics is a relatively new concept but initial studies have demonstrated their effectiveness in grasping applications. This thesis sets out a comparative study of popular gripping systems, focusing on the advantages of using fabrics in the construction of soft grippers. Multiple designs for fabric based finger like actuators, each addressing the drawbacks of the preceding design, are discussed along with the experimental evaluation of each design. A novel gripping mechanism in which the fingers of the gripper grow lengthwise from the tip (evert) to access and grasp the object is also presented. Large-scale fabric based eversion robots have been constructed to access environments with restricted access and for monitoring purposes. An experimental evaluation of the eversion capable finger is also presented, outlining important attributes such as payload, bending and force capability of the designed finger. An optical fibre based sensing methodology is also presented, capable of measuring the bending behaviour in soft actuators. The proposed sensor can be configured to sense bending angles, as well as the contact forces along different points along the length of the actuators