Perceiving guaranteed collision-free robot trajectories in unknown and unpredictable environments

The dissertation introduces novel approaches for solving a fundamental problem: detecting a collision-free robot trajectory based on sensing in real-world environments that are mostly unknown and unpredictable, i.e., obstacle geometries and their motions are unknown. Such a collision-free trajectory must provide a guarantee of safe robot motion by accounting for robot motion uncertainty and obstacle motion uncertainty. Further, as simultaneous planning and execution of robot motion is required to navigate in such environments, the collision-free trajectory must be detected in real-time. Two novel concepts: (a) dynamic envelopes and (b) atomic obstacles, are introduced to perceive if a robot at a configuration q, at a future time t, i.e., at a point ? = (q, t) in the robot's configuration-time space (CT space), will be collision-free or not, based on sensor data generated at each sensing moment t, in real-time. A dynamic envelope detects a collision-free region in the CT space in spite of unknown motions of obstacles. Atomic obstacles are used to represent perceived unknown obstacles in the environment at each sensing moment. The robot motion uncertainty is modeled by considering that a robot actually moves in a certain tunnel of a desired trajectory in its CT space. An approach based on dynamic envelopes is presented for detecting if a continuous tunnel of trajectories are guaranteed collision-free in an unpredictable environment, where obstacle motions are unknown. An efficient collision-checker is also developed that can perform fast real-time collision detection between a dynamic envelope and a large number of atomic obstacles in an unknown environment. The effectiveness of these methods is tested for different robots using both simulations and real-world experiments

Machine Learning Meets Advanced Robotic Manipulation

Automated industries lead to high quality production, lower manufacturing cost and better utilization of human resources. Robotic manipulator arms have major role in the automation process. However, for complex manipulation tasks, hard coding efficient and safe trajectories is challenging and time consuming. Machine learning methods have the potential to learn such controllers based on expert demonstrations. Despite promising advances, better approaches must be developed to improve safety, reliability, and efficiency of ML methods in both training and deployment phases. This survey aims to review cutting edge technologies and recent trends on ML methods applied to real-world manipulation tasks. After reviewing the related background on ML, the rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue. The paper is closed with important research directions for future works

Soft Robot-Assisted Minimally Invasive Surgery and Interventions: Advances and Outlook

Since the emergence of soft robotics around two decades ago, research interest in the field has escalated at a pace. It is fuelled by the industry's appreciation of the wide range of soft materials available that can be used to create highly dexterous robots with adaptability characteristics far beyond that which can be achieved with rigid component devices. The ability, inherent in soft robots, to compliantly adapt to the environment, has significantly sparked interest from the surgical robotics community. This article provides an in-depth overview of recent progress and outlines the remaining challenges in the development of soft robotics for minimally invasive surgery

Design of a Variable Stiffness Passive Layer Jamming Structure for Anthropomorphic Robotic Finger Applications

Soft robots can effectively mimic human hand interface characteristics and facilitate collaborative operations with humans in a safe manner. This dissertation research concerns the design and fabrication of a low cost variable stiffness structure for applications in compliant robotic fingers. A conceptual design of a compact multi-layer structure is proposed for realizing variable stiffness, when applied to underactuated fingers of an anthropomorphic robotic hand. The proposed design comprises thin material layers with clearance that permits a progressive hardening feature while grasping and added design flexibility and tuning of the fingers’ compliance. The design permits stiffness variations in a passive manner in the soft contact regions. The design is realized to ensure ease of scalability and cost-effective fabrication by the ’Additive Manufacturing (AM)’/3D-printing technology. Both the multi-layer structures and the fingers could be fabricated as a single entity, and from a single base material with relatively low elastic modulus. The proposed design also exhibits finite degrees-of-freedom representative of the human finger - The feasibility of the design and its manufacturability are verified through prototype fabrication using a readily available 3D-printing material, namely; 'Thermoplastic PolyUrethane (TPU)' with Young’s Modulus of 25MPa. The chosen material permitted low stiffness of the multi-layer structure in the contact interface under relatively small deformations, while ensuring sufficient rigidity on the non-contact regions of the finger. A finite element (FE) model is formulated considering 3D tetrahedral elements and a nodal-normal contact detection method together with the augmented Lagrange formulation. The model is analyzed to determine the force-displacement characteristics of the structure subject to linearly increasing compressive load, under the assumption of low interface friction. A simplified analytical model of the multi-layer structure is also formulated considering essential boundary and support conditions for each individual layer. The model revealed progressive hardening characteristics of the multilayer structure during compression due to sequential jamming of individual layers. The force-displacement characteristics of the design could thus be varied by varying the multi-layer structure parameters, such as number of layers, thickness of individual layers, material properties, and clearance between the successive layers. It is shown that the simplified analytical model could provide reasonably good estimate of the force-deflection properties of the structure in a computationally efficient manner. The analytical model is subsequently used to investigate the influences of variations in the multilayer structure parameters in a computationally efficient manner. It is shown that the proposed design offers superior tuning flexibility to realize desired force-displacement characteristics of the structure for developing scalable anthropomorphic robotic fingers of a compliant robotic hand, in addition to the cost-effective manufacturability

Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models

Soft robots have the potential to significantly change the way that robots interact with the environment and with humans. However, accurately modeling soft robot and soft actuator dynamics in order to perform model-based control can be extremely difficult. Deep neural networks are a powerful tool for modeling systems with complex dynamics such as the pneumatic, continuum joint, six degree-of-freedom robot shown in this paper. Unfortunately it is also difficult to apply standard model-based control techniques using a neural net. In this work, we show that the gradients used within a neural net to relate system states and inputs to outputs can be used to formulate a linearized discrete state space representation of the system. Using the state space representation, model predictive control (MPC) was developed with a six degree of freedom pneumatic robot with compliant plastic joints and rigid links. Using this neural net model, we were able to achieve an average steady state error across all joints of approximately 1 and 2° with and without integral control respectively. We also implemented a first-principles based model for MPC and the learned model performed better in terms of steady state error, rise time, and overshoot. Overall, our results show the potential of combining empirical modeling approaches with model-based control for soft robots and soft actuators

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research