Feedrate scheduling is crucial for improving productivity and accuracy in robotic milling applications. However, due to the nonlinear relationship between the joint space and task space, how to plan a time-optimal feedrate profile with quick analytical solution while ensuring kinematic control up to the snap level remains quite a challenge. To solve these concerns, a snap-bounded and time-optimal feedrate scheduling model is first presented in this article. For accelerating the solving process of the nonlinear model, a synchronous linearization approach is also introduced to help relax the highly nonlinear constraints in both joint space and task space into linear ones. Thereby, the originally complex feedrate scheduling issue is converted to a finite-state convex optimization problem, and an analytical solution to the feedrate profile could be computed efficiently using a straightforward linear programming algorithm. Finally, comparative simulation and experiment are carried out to verify the effectiveness of the proposed method.

Nowadays, although there exists many ridesharing services and dynamic matching algorithms for passengers and drivers, there is no service or algorithm that can balance the benefit of passengers and drivers while taking their time and cost constraints into consideration. In this paper, we try to solve the dynamic ridesharing problem by considering all above factors for all the participants. To this end, we present URoad, an efficient algorithm for large-scale dynamic ridesharing service, where a new price cost model is carefully designed to make up for the shortcomings of existing algorithms, and in the meantime a corresponding efficient matching algorithm is proposed to satisfy both the time and cost constraints of passengers and drivers. Specifically, for a given passenger, URoad will find out the optimal driver who can satisfy all the constraints of the passenger and the driver with the minimum detour distance. We design a series of data structures to speed up URoad for large scale ridesharing service application, e.g., Time Index, Grid Index and Greedy Strategy. Through extensive experiments, we prove that URoad can find the optimal driver for a given passenger from more than one hundred thousand drivers within 0.5 second in average.

This paper studies dynamic pricing for cloud service where different resources are consumed by different users. The traditional cloud resource pricing models can be divided into two categories: on‐demand service and reserved service. The former only takes the using time into account and is unfair for the users with long using time and little concurrency. The latter charges the same price to all the users and does not consider the resource consumption of users. Therefore, in this paper, we propose a flexible dynamic pricing model for cloud resources, which not only takes into account the occupying time and resource consumption of different users but also considers the maximal concurrency of resource consumption. As a result, on the one hand, this dynamic pricing model can help users save the cost of cloud resources. On the other hand, the profits of service providers are guaranteed. The key of the pricing model is how to efficiently calculate the maximal concurrency of resource consumption since the cost of providers is dynamically varied based on the maximal concurrency. To support this function in real time, we propose a data structure based on the classical B+ tree and the implementation for its corresponding basic operations like insertion, deletion, split, and query. Finally, the experiment results show that we can complete the dynamic pricing query on 10 million cloud resource usage records within 0.2 seconds on average.