Jaydev P. Desai

Modeling and control of formations of nonholonomic mobile robots

This paper addresses the control of a team of nonholonomic mobile robots navigating in a terrain with obstacles while maintaining a desired formation and changing formations when required, using graph theory. We model the team as a triple, (g, r, H), consisting of a group element g that describes the gross position of the lead robot, a set of shape variables r that describe the relative positions of robots, and a control graph H that describes the behaviors of the robots in the formation. Our framework enables the representation and enumeration of possible control graphs and the coordination of transitions between any two formations.

Robotic surgery: a current perspective.

Objective:To review the history, development, and current applications of robotics in surgery. Background:Surgical robotics is a new technology that holds significant promise. Robotic surgery is often heralded as the new revolution, and it is one of the most talked about subjects in surgery today. Up to this point in time, however, the drive to develop and obtain robotic devices has been largely driven by the market. There is no doubt that they will become an important tool in the surgical armamentarium, but the extent of their use is still evolving. Methods:A review of the literature was undertaken using Medline. Articles describing the history and development of surgical robots were identified as were articles reporting data on applications. Results:Several centers are currently using surgical robots and publishing data. Most of these early studies report that robotic surgery is feasible. There is, however, a paucity of data regarding costs and benefits of robotics versus conventional techniques. Conclusions:Robotic surgery is still in its infancy and its niche has not yet been well defined. Its current practical uses are mostly confined to smaller surgical procedures.

Controlling formations of multiple mobile robots

We investigate feedback laws used to control multiple robots moving together in a formation. We propose a method for controlling formations that uses only local sensor-based information, in a leader-follower motion. We use methods of feedback linearization to exponentially stabilize the relative distance and orientation of the follower, and show that the zero dynamics of the system are also (asymptotically) stable. We demonstrate in simulation the use of these algorithms to control six robots moving around an obstacle. These types of control laws can be used to control arbitrarily large numbers of robots moving in very general types of formations.

Force Feedback Plays a Significant Role in Minimally Invasive Surgery Results and Analysis

Objective:To evaluate the role of force feedback with applications to minimally invasive surgery (MIS). Two research hypotheses were tested using our automated laparoscopic grasper. Summary Background Data:Conventional laparoscopic tools do not have the ability of providing force feedback to a surgeon when in use with or without robotic surgical systems. Loss of haptic (force and tactile) feedback in MIS procedures is a disadvantage to surgeons since they are conventionally used to palpating tissues to diagnose tissues as normal or abnormal. Therefore, the need exists to incorporate force feedback into laparoscopic tools. Methods:We have developed an automated laparoscopic grasper with force feedback capability to help surgeons differentiate tissue stiffness through a haptic interface device. We tested our system with 20 human subjects (10 surgeons and 10 nonsurgeons) using our grasper to evaluate the role of force feedback to characterize tissues and answer 2 research hypotheses. Results:Our experiments confirmed 1 of our 2 research hypotheses, namely, providing both vision and force feedback leads to better tissue characterization than only vision feedback or only force feedback. Conclusions:We have validated 1 of our 2 research hypotheses regarding incorporating force feedback with vision feedback to characterize tissues of varying stiffness.

A Graph Theoretic Approach for Modeling Mobile Robot Team Formations

This paper addresses a new approach for modeling and control of multiple teams of mobile robots navigating in a terrain with obstacles, while maintaining a desired formation and changing formations when required. We model each team as a triple, (g,r, ℋ ), consisting of a group element, g∈SE(2), that describes the gross position of the lead robot, a set of shape variables, r, that describe the relative positions of robots, and a control graph, ℋ, that describes the behaviors of the robots in the formation. We assume that all the robots are equipped with the appropriate sensors to detect and avoid other robots and obstacles in the environment. Our framework enables the representation and enumeration of possible control graphs, and the coordination of transitions between any two control graphs. Further, we describe an algorithm that allows each team of robots to move between any two formations, while avoiding obstacles. As the number of robots increases, the number of possible control graphs increases. However, because the control computations are decentralized, the algorithms scale with the number of robots. We present examples to illustrate the control graphs and the algorithm for transitioning between them in the presence and absence of sensor noise.

Control of changes in formation for a team of mobile robots

Addresses the control of a team of robots navigating in a terrain with obstacles while maintaining a desired formation and changing formations when required. We model the team as a triple consisting of a group element that describes the gross motion of the team, a set of shape variables that describe the relative positions of robots, and a control graph that describes the behaviors of the robots in the formation. We assume that a lead robot is equipped with the appropriate sensors and plans the gross motion (path) for the team. This path is derived from optimal control theory. All other robots are coordinated by continuous controllers that are prescribed by the control graph. Our framework allows us to enumerate the number of control graphs and the possible transitions between them. Further, we describe an algorithm that allows the team of robots to move between any two formations, while avoiding obstacles. We illustrate the methodology with examples involving teams of 5 and 6 robots in the presence of obstacles.

Evaluating the Effect of Force Feedback in Cell Injection

Conventional methods of manipulating individual biological cells have been prevalent in the field of molecular biology. These methods do not have the ability to provide force feedback to an operator. Poor control of cell injection force is one of the primary reasons for low success rates in cell injection and transgenesis in particular. Therefore, there exists a need to incorporate force feedback into a cell injection system. We have developed a force feedback interface, which has the capability of measuring forces in the range of and provide a haptic display of the cell injection forces in real time. Using this force feedback interface, we performed several human factors studies to evaluate the effect of force feedback on cell injection outcomes. We tested our system with 40 human subjects and our experimental results indicate that the subjects were able to feel the cell injection force and confirmed our research hypothesis that the use of combined vision and force feedback leads to a higher success rate in cell injection task compared to using vision feedback alone.

Optimal Gait Selection for Nonholonomic Locomotion Systems

This paper addresses the optimal control and selection of gaits in a class of nonholonomic locomotion systems that exhibit group symmetries. We study optimal gaits for the snakeboard, a representative example of this class of systems. We employ Lagrangian reduction techniques to simplify the optimal control problem and describe a general framework and an algorithm to obtain numerical solutions to this problem. This work employs optimal control techniques to study the optimality of gaits and issues involving gait transitions. The general framework provided in this paper can easily be applied to other examples of biological and robotic locomotion.

Constitutive modeling of liver tissue: experiment and theory.

Realistic surgical simulation requires incorporation of the mechanical properties of soft tissue in mathematical models. In actual deformation of soft-tissue during surgical intervention, the tissue is subject to tension, compression, and shear. Therefore, characterization and modeling of soft-tissue in all these three deformation modes are necessary. In this paper we applied two types of pure shear test, unconfined compression and uniaxial tension test to characterize porcine liver tissue. Digital image correlation technique was used to accurately measure the tissue deformation field. Due to gravity and its effect on the soft tissue, a maximum stretching band was observed from the relative strain field on sample undergoing tension and pure shear test. The zero strain state was identified according to the position of this maximum stretching band. Two new constitutive models based on combined exponential/logarithmic and Ogden strain energy were proposed. The models are capable to represent the observed non-linear stress–strain relation of liver tissue for full range of tension and compression and also the general response of pure shear.

Modeling and control of the Mitsubishi PA-10 robot arm harmonic drive system

The purpose of this paper is to present our results in developing a dynamic model of the Mitsubishi PA-10 robot arm for the purpose of low-velocity trajectory tracking using low-feedback gains. The PA-10 is ideal for precise manipulation tasks because of the backdrivability, precise positioning capabilities, and zero backlash afforded by its harmonic drive transmission (HDT). However, the compliance and oscillations inherent in harmonic drive systems, and the lack of any technical information on the internal dynamics of the transmission, make the development of an accurate dynamic model of the robot extremely challenging. The novelty of this research is therefore the development of a systematic algorithm to extract the model parameters of a harmonic drive transmission in the robot arm to facilitate model-based control. We have modeled all seven joints of the Mitsubishi PA-10, and we have done several experiments to identify the various parameters of the harmonic drive system. We conclude with a sample trajectory-tracking task that demonstrates our model-based controller for the Mitsubishi PA-10 robot arm.