Reinforcement learning control for coordinated manipulation of multi-robots

In this paper, coordination control is investigated for multi-robots to manipulate an object with a common desired trajectory. Both trajectory tracking and control input minimization are considered for each individual robot manipulator, such that possible disagreement between different manipulators can be handled. Reinforcement learning is employed to cope with the problem of unknown dynamics of both robots and the manipulated object. It is rigorously proven that the proposed method guarantees the coordination control of the multi-robots system under study. The validity of the proposed method is verified through simulation studies

The study area in Shenzhen, China, and travel flows extracted from the mobile phone positioning data at the base tower level.

There are 5,929 cell phone towers, and the polygons were approximated by Voronoi tessellation of the towers representing the corresponding service areas. This dataset contains the positioning data of 9.7 million phone users (approximately 57.5% of the total population) during a workday in March 2012. The thicker lines indicate that more travel flows occurred between the two Voronoi polygons. The figure was created with an open source visualization toolkit: Processing (https://processing.org/). The administrative division of a shapefile sourced from the Bureau of Planning and Natural Resources of Shenzhen (http://pnr.sz.gov.cn/ywzy/chgl/bzdtfw/).</p

The probability distributions of <i>D</i>_<i>ave</i> and parameters at the node level.

(a) and (c) for the LN set and (b) and (d) for the AN set. The different colors represent corresponding LN values or AN values, as shown on the top legend. As the group with LN = 1 represents those individuals who only have visited one location in one day, their parameters are always expressed as zero. The dashed horizontal line in (a) and (b) indicates the parameter values for the ensemble distribution, as referred to in Fig 5. The solid lines in (c) and (d) represent the power law with an exponential cut-off fit for each group of the LN set and AN set.</p

The probability distributions of <i>D</i>_<i>ave</i> and parameters at the motif level.

(a) and (c) for the LBM set and (b) and (d) for the ABM set. The different colors represent corresponding LN values or AN values, as shown on the top legend. Because certain groups have no Dave data, their parameters are always expressed as zero. The dashed horizontal lines in (a) and (b) indicate the parameter values for the LN set and AN set, respectively, as shown in Fig 7, and the blue solid line represents the parameter values for the ensemble distribution. The solid lines in (c) and (d) represent the curves of the best-fitted distributions for each group of LBM and ABM, some of which are the power law with an exponential cut-off, and some are the pure power law. For instance, LBM 31 is fitted with a pure power law with α = 2.46.</p

The probability distribution of <i>D</i>_<i>ave</i> for the overall travels in the data.

The solid blue line represents the power law with an exponential cut-off fit, of which the functional form is shown as well, while the red points refer to the log-transformed data. The vertical green line indicates the cut-off value κ. It should be noted that the log-transformation is only for visualize data but not for fit data.</p

Fitted parameters (<i>β</i> and <i>γ</i>) of <i>DGBD</i>, sample size <i>N</i> (number of <i>LBMs</i>, <i>ABMs</i> and, <i>JMs</i>), and <i>p-values</i> for chi-square test.

Fitted parameters (β and γ) of DGBD, sample size N (number of LBMs, ABMs and, JMs), and p-values for chi-square test.</p

Characterizing preferred motif choices and distance impacts - Fig 5

(a)-(b) Rank-frequency distributions separated by different location nodes and activity nodes, respectively. The different points in colors indicate the number of nodes, and the red lines represent the DGBD fit. (c)-(d) Density maps of correlations of F(r) and ⟨k⟩ for the location-based and activity-based motifs, respectively.</p

The illustration of location-based and activity-based motifs constructed from stay sequences and activity chains.

The illustration of location-based and activity-based motifs constructed from stay sequences and activity chains.</p

The fitting results of linear functions in two datasets.

<p>(a) The fitting solutions for RTs in dataset 1. (b) The fitting solutions for RTs in dataset 2. Dots represent the mean RTs averaged over all observers or RTs of five different observers. Green dots represent the RTs or mean RTs of targets with no separation (<i>D</i>_<i>s</i> = 0). Purple dots represent the RTs or mean RTs of targets with separation (<i>D</i>_<i>s</i> > 0). Curve represents the fitting curve of the linear function.</p

DataSheet1_Relationship Between Summer Compound Hot and dry Extremes in China and the Snow Cover Pattern in the Preceding Winter.docx

With global warming, the probability of summer compound hot and dry extreme (CHDE) days, which are higher risk compared with single-factor extreme events, increases in some regions. However, there have been few studies on the winter precursor signals of such events. In this study, we found that summer CHDEs have generally increased in the last 20 years, with the increases in the middle and lower reaches of the Yangtze River region and Southwest China being more than double those in other regions of China. The dominant mode of summer CHDEs in China is characterized by more hot–dry days in the Yangtze–Huaihe River Basin (YHRB). Importantly, we found that there is an obvious cross-seasonal relationship between the first mode of winter snow cover in the Northern Hemisphere (NH) and summer CHDEs in China. When the mode of winter snow cover in the NH is in a positive phase with a negative-phase Arctic Oscillation (AO), i.e., more snow cover in Europe, Northeast China, and the northern United States, and less snow cover in central Asia and the midlatitudes in winter, more CHDEs in China in the following summer. Compared with the signals from the AO, these signals from winter snow can be better stored and transmitted into summer through the snow, soil and ocean, inducing a northward shift of the upper-level westerly jet and strengthening of South Asia high. Through the strong dynamic forcing of negative vorticity advection with the change of westerly jet, the subsidence movement in the western Pacific subtropical high (WPSH) region is strengthened, resulting in the stable maintenance of the WPSH in the YHRB. Under the synergy of a remote mid- and high-latitude wave train in summer, which also relates closely to winter snow cover, more CHDEs ultimately occur in the YHRB of China.</p