Ashis Gopal Banerjee

A survey of CAD model simplification techniques for physics-based simulation applications

Automated CAD model simplification plays an important role in effectively utilizing physics-based simulation during the product realization process. Currently a rich body of literature exists that describe many successful techniques for fully-automatic or semi-automatic simplification of CAD models for a wide variety of applications. The purpose of this paper is to compile a list of the techniques that are relevant for physics-based simulations problems and to characterize them based on their attributes. We have classified them into the following four categories: techniques based on surface entity based operators, volume entity based operators, explicit feature based operators, and dimension reduction operators. This paper also presents the necessary background information in the CAD model representation to assist the new readers. We conclude the paper by outlining open research directions in this field.

Developing a Stochastic Dynamic Programming Framework for Optical Tweezer-Based Automated Particle Transport Operations

Automated particle transport using optical tweezers requires the use of motion planning to move the particle while avoiding collisions with randomly moving obstacles. This paper describes a stochastic dynamic programming based motion planning framework developed by modifying the discrete version of an infinite-horizon partially observable Markov decision process algorithm. Sample trajectories generated by this algorithm are presented to highlight effectiveness in crowded scenes and flexibility. The algorithm is tested using silica beads in a holographic tweezer set-up and data obtained from the physical experiments are reported to validate various aspects of the planning simulation framework. This framework is then used to evaluate the performance of the algorithm under a variety of operating conditions.

Approaching the Symbol Grounding Problem with Probabilistic Graphical Models

n order for robots to engage in dialog with human teammates, they must have the ability to map between words in the language and aspects of the external world. A solution to this symbol grounding problem (Harnad, 1990) would enable a robot to interpret commands such as “Drive over to receiving and pick up the tire pallet.” In this article we describe several of our results that use probabilistic inference to address the symbol grounding problem. Our specific approach is to develop models that factor according to the linguistic structure of a command. We first describe an early result, a generative model that factors according to the sequential structure of language, and then discuss our new framework, generalized grounding graphs (G3). The G3 framework dynamically instantiates a probabilistic graphical model for a natural language input, enabling a mapping between words in language and concrete objects, places, paths and events in the external world. We report on corpus-based experiments where the robot is able to learn and use word meanings in three real-world tasks: indoor navigation, spatial language video retrieval, and mobile manipulation.

Real-Time Path Planning for Coordinated Transport of Multiple Particles Using Optical Tweezers

Automated transport of multiple particles using optical tweezers requires real-time path planning to move them in coordination by avoiding collisions among themselves and with randomly moving obstacles. This paper develops a decoupled and prioritized path planning approach by sequentially applying a partially observable Markov decision process algorithm on every particle that needs to be transported. We use an iterative version of a maximum bipartite graph matching algorithm to assign given goal locations to such particles. We then employ a three-step method consisting of clustering, classification, and branch and bound optimization to determine the final collision-free paths. We demonstrate the effectiveness of the developed approach via experiments using silica beads in a holographic tweezers setup. We also discuss the applicability of our approach and challenges in manipulating biological cells indirectly by using the transported particles as grippers.

Survey on indirect optical manipulation of cells, nucleic acids, and motor proteins

Optical tweezers have emerged as a promising technique for manipulating biological objects. Instead of direct laser exposure, more often than not, optically-trapped beads are attached to the ends or boundaries of the objects for translation, rotation, and stretching. This is referred to as indirect optical manipulation. In this paper, we utilize the concept of robotic gripping to explain the different experimental setups which are commonly used for indirect manipulation of cells, nucleic acids, and motor proteins. We also give an overview of the kind of biological insights provided by this technique. We conclude by highlighting the trends across the experimental studies, and discuss challenges and promising directions in this domain of active current research.

Research in Automated Planning and Control for Micromanipulation

Manipulation of microscopic objects, especially biological objects and microelectromechanical systems (MEMS) components, has become an important area of robotics research over the past several years. Automation is necessary as it is challenging to manually control the microobjects due to the scaling effect of the surface forces, stochastic motion of objects in fluid media, and uncertainty associated with object state estimation. Automation requires real-time control of the position, orientation, and force applied by each of the operational manipulators, as governed by the system-level objectives of optimizing resource, time, and effort, by planning suitable actions for the manipulated objects. In this paper, we provide a survey of the research in planning and control of such automated micromanipulation operations. We present a broad taxonomy based on the underlying approach, and discuss the salient features and experimental success of each research effort. We also identify the major limitations and common trends across all the approaches, discuss the effectiveness of an approach depending on the operation characteristics, and outline promising future research directions.

Content-based assembly search: A step towards assembly reuse

The increased use of CAD systems by product development organizations has resulted in the creation of large databases of assemblies. This explosion of assembly data is likely to continue in the future. In many situations, a text-based search alone may not be sufficient to search for assemblies and it may be desirable to search for assemblies based on the content of the assembly models. The ability to perform content-based searches on these databases is expected to help the designers in the following two ways. First, it can facilitate the reuse of existing assembly designs, thereby reducing the design time. Second, a lot of useful designs for manufacturing, and assembly knowledge are implicitly embedded in existing assemblies. Therefore a capability to locate existing assemblies and examine them can be used as a learning tool by designers to learn from the existing assembly designs. This paper describes a system for performing content-based searches on assembly databases. We identify templates for comprehensive search definitions and describe algorithms to perform content-based searches for mechanical assemblies. We also illustrate the capabilities of our system through several examples.

Generating Simplified Trapping Probability Models From Simulation of Optical Tweezers System

This paper presents a radial basis function based approach to generate simplified models to estimate the trapping probability in optical trapping experiments using offline simulations. The difference form of Langevins equation is used to perform physically accurate simulations of a particle under the influence of a trapping potential and is used to estimate trapping probabilities at discrete points in the parameter space. Gaussian radial basis functions combined with kd-tree based partitioning of the parameter space are then used to generate simplified models of trapping probability. We show that the proposed approach is computationally efficient in estimating the trapping probability and that the estimated probability using the simplified models is sufficiently close to the probability estimates from offline simulation data.

Comparing the Performance of Expert User Heuristics and an Integer Linear Program in Aircraft Carrier Deck Operations

Planning operations across a number of domains can be considered as resource allocation problems with timing constraints. An unexplored instance of such a problem domain is the aircraft carrier flight deck, where, in current operations, replanning is done without the aid of any computerized decision support. Rather, veteran operators employ a set of experience-based heuristics to quickly generate new operating schedules. These expert user heuristics are neither codified nor evaluated by the United States Navy; they have grown solely from the convergent experiences of supervisory staff. As unmanned aerial vehicles (UAVs) are introduced in the aircraft carrier domain, these heuristics may require alterations due to differing capabilities. The inclusion of UAVs also allows for new opportunities for on-line planning and control, providing an alternative to the current heuristic-based replanning methodology. To investigate these issues formally, we have developed a decision support system for flight deck operations that utilizes a conventional integer linear program-based planning algorithm. In this system, a human operator sets both the goals and constraints for the algorithm, which then returns a proposed schedule for operator approval. As a part of validating this system, the performance of this collaborative human-automation planner was compared with that of the expert user heuristics over a set of test scenarios. The resulting analysis shows that human heuristics often outperform the plans produced by an optimization algorithm, but are also often more conservative.

Optical Tweezers: Autonomous Robots for the Manipulation of Biological Cells

Optical tweezers (OTs) are a popular tool for manipulating biological objects, especially cells [1], [2]. Using a tightly focused laser beam, they exert sufficient forces to tweeze, i.e., hold (trap) and move, freely diffusing cells in the vicinity of the beam focus. The beam can be focused at any point in the workspace, which is typically a liquid-filled glass slide. The trapped cell can, thus, be translated and rotated (transported) in three dimensions by changing the beam focus position. OTs provide certain advantages over other cell-manipulation techniques. They are able to manipulate cells with a greater degree of precision as compared with microfluidic flow. Significant contact forces are not exerted on the cells, unlike in mechanical manipulation, thereby avoiding damages due to contact friction or surface chemistry. The cells are also easily released at the end of the manipulation by simply switching off the laser beam. Hence, OTs have been extensively used for mechanical characterization of cells by measuring their viscoelastic properties to distinguish between normal and diseased cells [3]. They have also been used for separating cells of different types [4] and investigating the response of cells to external stimuli [5]. However, manual or teleoperated control of the laser beam has limited their applicability for multicellular studies.