Distributed Safe Learning and Planning for Multi-robot Systems

This paper considers the problem of online multi-robot motion planning with general nonlinear dynamics subject to unknown external disturbances. We propose dSLAP, a distributed safe learning and planning framework that allows the robots to safely navigate through the environments by coupling online learning and motion planning. Gaussian process regression is used to online learn the disturbances with uncertainty quantification. The safe motion planning algorithm ensures collision avoidance against the learning uncertainty and utilizes set-valued analysis to achieve fast adaptation in response to the newly learned models. A model predictive control problem is then formulated and solved to return a control policy that balances between actively exploring the unknown disturbances and reaching goal regions. Sufficient conditions are established to guarantee the safety of the robots in the absence of backup policy. Monte Carlo simulations are conducted for evaluation

Autonomous navigation with constrained consistency for C-Ranger

Autonomous underwater vehicles (AUVs) have become the most widely used tools for undertaking complex exploration tasks in marine environments. Their synthetic ability to carry out localization autonomously and build an environmental map concurrently, in other words, simultaneous localization and mapping (SLAM), are considered to be pivotal requirements for AUVs to have truly autonomous navigation. However, the consistency problem of the SLAM system has been greatly ignored during the past decades. In this paper, a consistency constrained extended Kalman filter (EKF) SLAM algorithm, applying the idea of local consistency, is proposed and applied to the autonomous navigation of the C-Ranger AUV, which is developed as our experimental platform. The concept of local consistency (LC) is introduced after an explicit theoretical derivation of the EKF-SLAM system. Then, we present a locally consistency-constrained EKF-SLAM design, LC-EKF, in which the landmark estimates used for linearization are fixed at the beginning of each local time period, rather than evaluated at the latest landmark estimates. Finally, our proposed LC-EKF algorithm is experimentally verified, both in simulations and sea trials. The experimental results show that the LC-EKF performs well with regard to consistency, accuracy and computational efficiency

Antecedents of Coordination Effectiveness of Software Developer Dyads from Interacting Teams: An Empirical Investigation

Among numerous reasons for software project failure, coordination failure is considered as especially salient. Prior studies on coordination in software development are confined to team internal coordination and do not explicitly differentiate team internal and external coordination processes. This study proposes a research model to explain the antecedents of coordination effectiveness of software developer dyads from interacting teams. We explore the antecedents by integrating inter-personal coordination and technology-based coordination. Data were collected from 59 software developer dyads from different interacting teams as well as software developers’ managers. The results reveal that implicit knowledge sharing has a significant positive impact on coordination effectiveness. Social capital (mutual trust and project commitment) has a significant impact on knowledge sharing with mutual trust directly affecting both implicit and explicit knowledge sharing. Project commitment also has a direct impact on explicit knowledge sharing and mutual trust, but it does not directly affect implicit knowledge sharing

CysLT1R Antagonists Inhibit Tumor Growth in a Xenograft Model of Colon Cancer.

The expression of the inflammatory G-protein coupled receptor CysLT1R has been shown to be upregulated in colon cancer patients and associated with poor prognosis. The present study investigated the correlation between CysLT1R and colon cancer development in vivo using CysLT1R antagonists (ZM198,615 or Montelukast) and the nude mouse xenograft model. Two drug administration regimens were established. The first regimen was established to investigate the importance of CysLT1R in tumor initiation. Nude mice were inoculated with 50 µM CysLT1R antagonist-pretreated HCT-116 colon cancer cells and received continued treatment (5 mg/kg/day, intraperitoneally). The second regimen aimed to address the role of CysLT1R in tumor progression. Nude mice were inoculated with non-pretreated HCT-116 cells and did not receive CysLT1R antagonist treatment until recordable tumor appearance. Both regimens resulted in significantly reduced tumor size, attributed to changes in proliferation and apoptosis as determined by reduced Ki-67 levels and increased levels of p21(WAF/Cip1) (P<0.01), cleaved caspase 3, and the caspase-cleaved product of cytokeratin 18. Decreased levels of VEGF (P<0.01) and reduced vessel size (P<0.05) were also observed, the latter only in the ZM198,615-pretreatment group. Furthermore, we performed a series of in vitro studies using the colon cancer cell line HCT-116 and CysLT1R antagonists. In addition to significant reductions in cell proliferation, adhesion and colony formation, we observed induction of cell cycle arrest and apoptosis in a dose-dependent manner. The ability of Montelukast to inhibit growth of human colon cancer xenograft was further validated by using two additional colon cancer cell lines, SW-480 and HT-29. Our results demonstrate that CysLT1R antagonists inhibit growth of colon cancer xenografts primarily by reducing proliferation and inducing apoptosis of the tumor cells

A New Family of High Oxidation State Antiperovskite Nitrides:La₃MN₅ (M=Cr, Mn and Mo)

Three new nitrides La3MN5 (M=Cr, Mn, and Mo) have been synthesized using a high pressure azide route. These are the first examples of ternary Cs3CoCl5-type nitrides, and show that this (MN4)NLa3 antiperovskite structure type may be used to stabilise high oxidation-state transition metals in tetrahedral molecular [MN4]n− nitridometallate anions. Magnetic measurements confirm that Cr and Mo are in the M6+ state, but the M=Mn phase has an anomalously small paramagnetic moment and large cell volume. Neutron powder diffraction data are fitted using an anion-excess La3MnN5.30 model (space group I4/mcm, a=6.81587(9) Å and c=11.22664(18) Å at 200 K) in which Mn is close to the +7 state. Excess-anion incorporation into Cs3CoCl5-type materials has not been previously reported, and this or other substitution mechanisms may enable many other high oxidation state transition metal nitrides to be prepared.</p

Managing price and service rate in customer-intensive services under social interactions

This paper investigates the price and service rate decisions in a customer-intensive service in an M/M/1 queue system under the influence of social interactions, where a higher value of the service is perceived if more customers purchase the service. The customer-intensive nature of the service requires a low service speed to maintain its quality, which may increase the congestion of the system. Two cases where customers are either homogeneous or heterogeneous in terms of the customer intensity are considered. It is found that social interactions can always benefit the service provider as more expected revenue can be achieved, and potential profits would be lost if the influence of social interactions is ignored. For the case with heterogeneous customers, the optimal price and service rate decisions are solved with or without considering social interaction effect. The study finds the proportions of high and low sensitive customers and the social interaction intensity are critical to the operational decisions and the market coverage strategies. These results offer a better understanding on the interplay between the quality-speed conundrum and the influence of social interactions in customers’ purchase behaviour in managing customer-intensive services

Multi-Robot-Assisted Human Crowd Evacuation using Navigation Velocity Fields

This work studies a robot-assisted crowd evacuation problem where we control a small group of robots to guide a large human crowd to safe locations. The challenge lies in how to model human-robot interactions and design robot controls to indirectly control a human population that significantly outnumbers the robots. To address the challenge, we treat the crowd as a continuum and formulate the evacuation objective as driving the crowd density to target locations. We propose a novel mean-field model which consists of a family of microscopic equations that explicitly model how human motions are locally guided by the robots and an associated macroscopic equation that describes how the crowd density is controlled by the navigation velocity fields generated by all robots. Then, we design density feedback controllers for the robots to dynamically adjust their states such that the generated navigation velocity fields drive the crowd density to a target density. Stability guarantees of the proposed controllers are proven. Agent-based simulations are included to evaluate the proposed evacuation algorithms