The tasks would fail to be assigned to any robots in the task allocation phase as a consequence of the inherent communication constraints in multi-robot systems (MRS). This negative effect becomes even more serious in tasks with temporal constraints. We therefore propose the constraint-based approach (CoBA), a market-based task allocation approach to enable multi-robot coordination in domains with temporal constraints between subtasks of a complex task and network constraints between robots. We handle network constraints by having each robot maintain a dynamic acquaintance network of robots that it knows about, and allowing a robot to submit a bid on behalf of another robot during a task auction (“indirect bidding”). In order to model the complex task, we introduce the AND/OR task tree with temporal constraints. An auction-clearing routine, which supports the AND/OR task tree with temporal constraints and direct/indirect task auction, is proposed to enable effective multi-robot task allocation in spite of various constraints. The solution was validated in both simulation and physical environments by a series of experiments in disaster response domains. Specifically, we study the system performance by separately varying the number of robots, the expected rate of task issuance, the communication reliability factor, the compositions of MRS, as well as the acquaintance relationship parameter, in simulation experiments. The results suggest that our solution outperforms others, that is, robots were able to complete the tasks more promptly and effectively.

In this paper, we propose a new travel time estimation framework based on transformer and convolution neural networks (CNN) to improve the accuracy of travel time estimation. We design a traffic information fusion component, which fuses the GPS trajectory, real road network, and external attributes, to fully consider the influence of road network topological characteristics as well as the traffic temporal characteristics on travel time estimation. Moreover, we provide a multiview CNN transformer component to capture the spatial information of each trajectory point at multiple regional scales. Extensive experiments on Chengdu and Beijing datasets show that the mean absolute percent error (MAPE) of our MCT-TTE is 11.25% and 11.78%, which is competitive with the state-of-the-arts baselines.

Online friend recommendation is a fast developing topic in web mining. In this paper, we used SVD matrix factorization to model user and item feature vector and used stochastic gradient descent to amend parameter and improve accuracy. To tackle cold start problem and data sparsity, we used KNN model to influence user feature vector. At the same time, we used graph theory to partition communities with fairly low time and space complexity. What is more, matrix factorization can combine online and offline recommendation. Experiments showed that the hybrid recommendation algorithm is able to recommend online friends with good accuracy.

In recent years, both domestic and international research on quadruped robots has advanced towards high dynamics and agility, with a focus on high-speed locomotion as a representative motion in high-dynamic activities. Quadruped animals like cheetahs exhibit high-speed running capabilities, attributed to the indispensable role played by their flexible spines during the flight phase motion. This paper establishes dynamic models of flexible spinal quadruped robots with different degrees of simplification, providing a parameterized description of the flight phase motion for both rigid-trunk and flexible-spine quadruped robots. By setting different initial values for the spine joint and calculating the flight phase results for both types of robots at various initial velocities, the study compares and analyzes the impact of a flexible spine on the flight phase motion of quadruped robots. Through comparative experiments, the research aims to validate the influence of a flexible spine during the flight phase motion, providing insights into how spine flexibility affects the flight phase motion of quadruped robots.

In large-scale location-based service, an ideal situation is that self-adapting routing strategies use future traffic data as input to generate a topology which could adapt to the changing traffic well. In the paper, we propose a traffic prediction model for the broker in publish/subscribe system, which can predict the traffic of the link in future by neural network. We first introduced our traffic prediction model and then described the model integration. Finally, the experimental results show that our traffic prediction model could predict the traffic of link well.

In recent years, both domestic and international research on quadruped robots has advanced towards high dynamics and agility, with a focus on high-speed locomotion as a representative motion in high-dynamic activities. Quadruped animals like cheetahs exhibit high-speed running capabilities, attributed to the indispensable role played by their flexible spines during the flight phase motion. This paper establishes dynamic models of quadruped robots with different degrees of spine flexibility, providing a parameterized description of the flight phase motion for both rigid trunks and flexible spine quadruped robots. By setting different initial values for the spine joint and calculating the flight phase results for both types of robots at various initial velocities, the study compares and analyzes the impact of a flexible spine on the flight phase motion of quadruped robots. Through comparative experiments, the research aims to validate the influence of a flexible spine during the flight phase motion, providing insights into how spine flexibility affects the flight phase motion of quadruped robots.