Abstract

In this paper, a kinematic motion planning algorithm for cooperative spatial payload manipulation is presented. A hierarchical approach is introduced to compute realtime collision-free motion plans for a formation of mobile manipulator robots. Initially, collision-free configurations of a deformable 2-D virtual bounding box are identified, over a planning horizon, to define a convex workspace for the entire system. Then, 3-D payload configurations whose projections lie within the defined convex workspace are computed. Finally, a convex decentralized model-predictive controller is formulated to plan collision-free trajectories for the formation of mobile manipulators. This approach facilitates real-time motion planning for the system and is scalable in the number of robots. The algorithm is validated in simulated dynamic environments.

Abstract

In this work, we address traffic management of multiple payload transport systems comprising of nonholonomic robots. We consider loosely coupled rigid robot formations carrying a payload from one place to another. Each payload transport system (PTS) moves in various kinds of environments with obstacles. We ensure each PTS completes its given task by avoiding collisions with other payload systems and obstacles as well. Each PTS has one leader and multiple followers and the followers maintain a desired distance and angle with respect to the leader using a decentralized leader follower control architecture while moving in the traffic. We showcase, through simulations the time taken by each PTS to traverse its respective trajectory with and without other PTS and obstacles. We show that our strategies help manage the traffic for a large number of PTS moving from one place to another.

Abstract

This paper presents our solution for transportation of deformable payloads using a formation of non-holonomic mobile robots while avoiding static and dynamic obstacles. We assume the center of the formation to be a virtual robot which also plays the role of the leader of the formation. Rapidly exploring Random Tree star (RRT*) algorithm is used to determine a static obstacle free trajectory for the virtual leader. In addition to that, a custom path planning algorithm is incorporated for the follower robots. A decentralized leader-follower formation control is applied for the robots to traverse their respective trajectories without breaking the formation while following the constraints of the deformable payload. The virtual leader moves forward or backward on the initially generated path with variable velocity depending on the proximity of dynamic obstacles. All the other robots follow it so that the formation is able to avoid the dynamic obstacles while still remaining on the path leading towards the destination.

Abstract

Traditional row store has the disadvantage of reading irrelevant attributes into main memory, when only a few attributes are queried and it incurs a large number of cache misses. Column store, while being cache efficient, suffers from large “stitching” costs if the number of attributes queried are large. In this paper, we introduce FAST, an intuitive, hybrid combination of both these storage mechanisms that leverages the main memory to reduce the I/O time and cache misses to efficiently serve ad-hoc read-only queries in real time. Our system acts as a commercial off-the-shelf (COTS) solution on top of existing databases. We show that on average, FAST executes queries up-to an order of magnitude faster than row storage and as much as twice as fast than column storage for ad-hoc data warehouse workloads. Our results show superior performance of FAST on multiple TPC-H queries.We also present techniques to automatically adapt the main memory layout to a changing workload.

Abstract

In this work, we consider a multi-wheeled payload transport system. Each of the wheels can be selectively actuated.When they are not actuated, wheels are free moving and do not consume battery power. The payload transport system is modeled as an actuated multi-agent system, with each wheelmotor pair as an agent. Kinematic and dynamic models are developed to ensure that the payload transport system moves as desired. We design optimization formulations to decide on the number of wheels to be active and which of the wheels to be active so that the battery is conserved and the wear on the motors is reduced. Our multi-level control framework over the agents ensures that near-optimal number of agents is active for the payload transport system to function. Through simulation studies we show that our solution ensures energy efficient operation and increases the distance traveled by the payload transport system, for the same battery power. We have built the payload transport system and provide results for preliminary experimental validation.

Abstract

We are given an environment with some objects (a city block area) and mobile agents moving in the environment. An agent (hider) can hide behind an object to be not seen by other agents (seekers) through their line of sight (visibility). The aim of hiders is not to be caught for the longest time, and the aim of the seekers is catch all of them in the shortest period of time. We formulate the problem by using visibility based map abstractions. Agents plan their moves by utilizing multi-armed bandits UCB reward update model. We evaluate our abstractions and strategies by simulating the game under various different scenarios.

Abstract

In this paper, we consider a set of articles or reports by journalists or others, wherein they predict or promise something about future. The problem we approach is determining the credibility of the authors based on the predictions coming out to be true. The two specific problems we address are extracting the predictions from the articles and annotating with various prediction attributes. And then we determine the truth of these predictions, using Wikipedia as a credible source to extract relevant facts which can ascertain the validity of the predictions. We proposed and built an end to end system for automated predictions validation(APV) by extracting future speculations and predictions from news articles and social media. We considered 28 news articles and extracted 97 predictions from these articles and the range of credibility scores(Fscores) for these articles are (0.57-0.71).

Abstract

We estimate that a large number of news articles contain references to future. The reference is detected through the notion of predictive statements (phrases). Distinguishing such predictive statements from factual statements in news articles is important for most applications such as fact checking, opinion mining, future trend analysis, etc. In this paper, we approach the problem of automatically extracting futurerelated information by solving two sub-problems. The first sub-problem is labeling a sentence as predictive or factual. In addition to extracting the predictions, we address the tasks of clausal scope resolution and dis-embedding linguistic peripheral clauses with respect to the predictive clause in a sentence. To solve these problems, we extract all the clauses of a given sentence and classify each of the clauses as predictive or factual. We then use a machine learning based approach to disambiguate the clause labels by using the clausal dependency relations and label the sentence.

Abstract

Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations to extract the relationships between treatment and problem. While deep learning techniques demand a large amount of data for training, we make use of a rule-based system particularly for relationship classes with fewer samples. Our final relations are derived by jointly combining the results from deep learning and rule-based models. Our system achieved a promising performance on the relationship classes of I2b2 2010 relation extraction task.

Abstract

Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations to extract the relationships between treatment and problem. While deep learning techniques demand a large amount of data for training, we make use of a rule-based system particularly for relationship classes with fewer samples. Our final relations are derived by jointly combining the results from deep learning and rule-based models. Our system achieved a promising performance on the relationship classes of I2b2 2010 relation extraction task.