Perceiving,learning, and exploiting object affordances for autonomous pile manipulation

Autonomous manipulation in unstructured environments will enable a large variety of exciting and important applications. Despite its promise, autonomous manipulation remains largely unsolved. Even the most rudimentary manipulation task—such as removing objects from a pile—remains challenging for robots. We identify three major challenges that must be addressed to enable autonomous manipulation: object segmentation, action selection, and motion generation. These challenges become more pronounced when unknown man-made or natural objects are cluttered together in a pile. We present a system capable of manipulating unknown objects in such an environment. Our robot is tasked with clearing a table by removing objects from a pile and placing them into a bin. To that end, we address the three aforementioned challenges. Our robot perceives the environment with an RGB-D sensor, segmenting the pile into object hypotheses using non-parametric surface models. Our system then computes the affordances of each object, and selects the best affordance and its associated action to execute. Finally, our robot instantiates the proper compliant motion primitive to safely execute the desired action. For efficient and reliable action selection, we developed a framework for supervised learning of manipulation expertise. To verify the performance of our system, we conducted dozens of trials and report on several hours of experiments involving more than 1,500 interactions. The results show that our learning-based approach for pile manipulation outperforms a common sense heuristic as well as a random strategy, and is on par with human action selection.     

Investigation of effective dimensionless numbers on initiation of instability in combustion of moisty organic dust简

In this work, the effect of various effective dimensionless numbers and moisture contents on initiation of instability in combustion of moisty organic dust is calculated. To have reliable model, effect of thermal radiation is taken into account. One- dimensional flame structure is divided into three zones： preheat zone, reaction zone and post-flame zone. To investigate pulsating characteristics of flame, governing equations are rewritten in dimensionless space-time （（, r/, ~） coordinates. By solving these newly achieved governing equations and combining them, which is completely discussed in body of article, a new expression is obtained. By solving this equation, it is possible to predict initiation of instability in organic dust flame. According to the obtained results by increasing Lewis number, threshold of instability happens sooner. On the other hand, pulsating is postponed by increasing Damk6hler number, pyrolysis temperature or moisture content. Also, by considering thermal radiation effect, burning velocity predicted by our model is closer to experimental results.     

Fracture mechanics‐based progressive damage modelling of adhesively bonded fibre‐reinforced polymer joints

A quasi‐static progressive damage model for prediction of the fracture behaviour and strength of adhesively bonded fibre‐reinforced polymer joints is introduced in this paper. The model is based on the development of a mixed‐mode failure criterion as a function of a master R‐curve derived from the experimental results obtained from standard fracture mechanics joints. Consequently, the developed failure criterion is crack‐length and mode‐mixity dependent, and it takes into account the contribution of the fibre‐bridging effect. Energy release rate values for adhesively bonded double‐lap joints are obtained by using the virtual crack closure technique method in a finite element model, and the numerically obtained strain energy release rate is compared to the critical strain energy release rate given by the mixed‐mode failure criterion. The entire procedure is implemented in a numerical algorithm, which was successfully used for predicting the strength and R‐curve response of adhesively bonded double‐lap structural joints made of pultruded glass fibre‐reinforced polymers and epoxy adhesives.     

A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.     

A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers

This paper presents a robust, efficient and parameter-setting-free evolutionary approach for the optimal design of compact heat exchangers. A learning automata based particle swarm optimization (LAPSO) is developed for optimization task. Seven design parameters, including discreet and continuous ones, are considered as optimization variables. To make the constraint handling straightforward, a self-adaptive penalty function method is employed. The efficiency and the accuracy of the proposed method are demonstrated through two illustrative examples that include three objectives, namely minimum total annual cost, minimum weight and minimum number of entropy generation units. Numerical results indicate that the presented approach generates the optimum configuration with higher accuracy and a higher success rate when compared with genetic algorithms (GAs) and particle swarm optimization (PSO).     

A Novel 2.5–3.1 GHz Wide-Band Low-Noise Amplifier in 0.18 \upmu \hbox {m} CMOS

Aiming for the simultaneous realization of constant gain, accurate input and output impedance matching and minimum noise figure (NF) over a wide frequency range, the circuit topology and detailed design of wide broadband low noise amplifier (LNA) are presented in this paper. A novel 2.5–3.1 GHz wide-band LNA with unique characteristics has been presented. Its design and layout are done by TSMC 0.18  \(\upmu \hbox {m}\) technology. Common gate stage has been used to improve input matching. In order to enhance output matching and reduce the noise as well, a buffer stage is utilized. Mid-stages which tend to improve the gain and reverse isolation are exploited. The proposed LNA achieves a power gain of 15.9 dB, a NF of 3.5 dB with an input return loss less than \(-\) 11.6, output return loss of \(-\) 19.2 to \(-\) 19 and reverse isolation of \(-\) 38 dB. The LNA consumes 54.6 mW under a supply voltage of 2 V while having some acceptable characteristics.     

Synthesis of polyamides from p-Xylylene glycol and dinitriles

A series of polyamides were prepared by direct polyamidation of the p-Xylylene glycol with various commercially available dinitriles via Ritter reaction. All the synthesized polyamides showed good solubility in amide type solvents such as N-methyl-2-pyrrolidone, N,N-dimethyl acetamide, N,N-dimethyl formamide, and dimethyl sulfoxide. They exhibited inherent viscosities in the range of 0.25–0.62 dl/g. Elemental analysis, differential scanning calorimetry, thermogravimetric analysis, infrared and H¹-NMR spectroscopies were used to characterize the polymers. According to the DSC analysis, the glass transition temperatures of the polyamides were found to be 95–174°C. Thermogravimetric analysis indicated that the polymers show the 10% weight loss temperatures in the range of 250–350°C.     

Simulation of Propane Dehydrogenation to Propylene in a Radial‐Flow Reactor over Pt‐Sn/Al2O3 as the Catalyst

Catalytic paraffin dehydrogenation for manufacturing olefins is considered to be one of the most significant production routes in the petrochemical industries. A reactor kinetic model for the dehydrogenation of propane to propylene in a radial‐flow reactor over Pt‐Sn/Al₂O₃ as the catalyst was investigated here. The model showed that the catalyst activity was highly time dependent. In addition, the component concentrations and the temperature varied along the reactor radius owing to the occurring endothermic reaction. Moreover, a similar trend was noticed for the propane conversion as for the propylene selectivity, with both of them decreasing over the time period studied. Furthermore, a reversal of this trend was also revealed when the feed temperature was enhanced or when argon was added into the feed as an inert gas.