Automated chemical synthesis carries great promises of safety, efficiency and reproducibility for both research and industry laboratories. Current approaches are based on specifically-designed automation systems, which present two major drawbacks: (i) existing apparatus must be modified to be integrated into the automation systems; (ii) such systems are not flexible and would require substantial re-design to handle new reactions or procedures. In this paper, we propose a system based on a robot arm which, by mimicking the motions of human chemists, is able to perform complex chemical reactions without any modifications to the existing setup used by humans. The system is capable of precise liquid handling, mixing, filtering, and is flexible: new skills and procedures could be added with minimum effort. We show that the robot is able to perform a Michael reaction, reaching a yield of 34 , which is comparable to that obtained by a junior chemist (undergraduate student in Chemistry).
To automate the experiments in these papers @cite_2 , @cite_7 , @cite_4 and @cite_12 , modifications such as sensors and tubing were made to most apparatus to facilitate liquid transfer and all components of the automation unit were dependent on each other. Hence, it will not be convenient to dismantle the unit to access any of these components individually due to the amount of tubing used. This accessibility may be desired by research chemists who share a common pool of equipment as by isolating equipment in the automation unit, it can result in a smaller amount of resources available. In addition, based on these studies, it is evident that most research involves pure automation and there were not many implementations of robotics. Robot arms were also mostly limited to pick-and-place tasks. However, robot arms may pose several advantages due to their flexibility and may be able to eliminate messy tubing to transfer liquids or apparatus. Hence, robot arms may allow the integration of independent equipment to automate chemical synthesis, such that the accessibility of these individual equipment can be retained.
@cite_4 @cite_12 @cite_7 @cite_2
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This paper addresses an engineering attempt of utilizing multiple robot arms in the assembly of deformable parts. The robot system developed in this research simulates a practically existing assembly process in an automobile plant where wrapped cables (wire harness) have to be fixed on the body of a car. This operation is performed by skilled workers and is considered to be difficult for automation. In this paper, a synthetic robotized solution is proposed for this process. For the first time, it realizes robotized assembly of a complex deformable object (wire harness) under a condition similar to an actual plant. Although some of the problems are simplified in the implementation, those critical to assemblies involving deformable objects are investigated and solved with experimental validation. Therefore, this research is expected to contribute to exploring the feasibility and potential of robotized assembly using deformable parts in actual plants. To make production lines more flexible, dual-arm robots are good candidates to be deployed in autonomous assembly units. In this paper, we propose a sparse kinematic control strategy, that minimizes the number of joints actuated for a coordinated task between two arms. The control strategy is based on a hierarchical sparse QP architecture. We present experimental results that highlight the capability of this architecture to produce sparser motions (for an assembly task) than those obtained with standard controllers. Robotic assistants have the potential to greatly improve our quality of life by supporting us in our daily activities. A service robot acting autonomously in an indoor environment is faced with very complex tasks. Consider the problem of pouring a liquid into a cup, the robot should first determine if the cup is empty or partially filled. RGB-D cameras provide noisy depth measurements which depend on the opaqueness and refraction index of the liquid. In this paper, we present a novel probabilistic approach for estimating the fill-level of a liquid in a cup using an RGB-D camera. Our approach does not make any assumptions about the properties of the liquid like its opaqueness or its refraction index. We develop a probabilistic model using features extracted from RGB and depth data. Our experiments demonstrate the robustness of our method and an improvement over the state of the art. Advanced modern humanoid robots often have complex manipulators with a large number of degrees of freedom. Thus, motion planning for such manipulators is a very computationally challenging problem. However, often robotic manipulators allow the wrist degrees of freedom to be controlled independently from the configuration of the rest of the arm. In this paper we show how to split the high dimensional planning problem into two lower-dimensional sub-problems—planning for the main arm joints and planning for the wrist joints, without losing guarantees on completeness. This approach is an extension of our previously developed framework for planning with adaptive dimensionality. Experimentally, we show that this approach is very effective in speeding up planning for robotic arms on Willow Garage’s PR2 platform. We compare our algorithm with several popular alternative approaches for performing motion planning for robotic arms. The results we observe illustrate that our algorithm provides a good balance between planning time, planning success rate, path consistency, and path quality.