In recent years, the multiple traveling salesmen problem (MTSP or multiple TSP) has received increasing research interest and one of its main applications is coordinated multirobot mission planning, such as cooperative search and rescue tasks. However, it is still challenging to solve MTSP with improved inference efficiency as well as solution quality in varying situations, e.g., different city positions, different numbers of cities, or agents. In this article, we propose an attention-based multiagent reinforcement learning (AMARL) approach, which is based on the gated transformer feature representations for min-max multiple TSPs. The state feature extraction network in our proposed approach adopts the gated transformer architecture with reordering layer normalization (LN) and a new gate mechanism. It aggregates fixed-dimensional attention-based state features irrespective of the number of agents and cities. The action space of our proposed approach is designed to decouple the interaction of agents’ simultaneous decision-making. At each time step, only one agent is assigned to a non-zero action so that the action selection strategy can be transferred across tasks with different numbers of agents and cities. Extensive experiments on min-max multiple TSPs were conducted to illustrate the effectiveness and advantages of the proposed approach. Compared with six representative algorithms, our proposed approach achieves state-of-the-art performance in solution quality and inference efficiency. In particular, the proposed approach is suitable for tasks with different numbers of agents or cities without extra learning, and experimental results demonstrate that the proposed approach realizes powerful transfer capability across tasks.

In a decentralized multi-robot exploration problem, the robots have to cooperate effectively to map a strange environment as soon as possible without a centralized controller. In the past few decades, a set of “human-designed” cooperation strategies have been proposed to address this problem, such as the well-known frontier-based approach. However, many real-world settings, especially the ones that are constantly changing, are too complex for humans to design efficient and decentralized strategies. This paper presents a novel approach, the Attention-based Communication neural network (CommAttn), to “learn” the cooperation strategies automatically in the decentralized multi-robot exploration problem. The communication neural network enables the robots to learn the cooperation strategies with explicit communication. Moreover, the attention mechanism we introduced additionally can precisely calculate whether the communication is necessary for each pair of agents by considering the relevance of each received message, which enables the robots to communicate only with the necessary partners. The empirical results on a simulated multi-robot disaster exploration scenario demonstrate that our proposal outperforms the traditional “human-designed” methods, as well as other competing “learning-based” methods in the exploration task.

Robotic technologies and applications have recently witnessed remarkable advancements. A major challenge is the shortest-path planning problem of a curvature-bounded vehicle from the known starting configuration to visit a target point and finally return to the starting configuration in an obstacle environment. Spurred by this significant issue in robotic surveillance and patrolling applications, this paper proposed the Two-trip Obstacle-environment Relaxed Dubins Problem (TORDP). In TORDP, the vehicle’s target-visiting heading is a critical variable. Analytical approaches have existed for simpler scenarios than TORDP. However, these approaches are unavailable when solving the complex TORDP simultaneously with bounded curvature, variable target heading and unified ability to tackle with- or without- obstacle cases. Hence, we develop the mixed-integer piecewise-linear program (MIPWLP) approach, making the otherwise intractable complex scenario unifiedly solved with guaranteed good quality. Extensive experiments demonstrate that the proposed approach demonstrates effective performance. Furthermore, the objective approximation error in some cases was analyzed to achieve a length near the optimal length within $h^{2}/(2\sqrt {2})$ tolerance where h is the approximation piece length. The proposed MIPWLP approach could also offer a generalizable optimization framework for broader robotic path-planning applications in constrained environments.

With the rapid development of Internet communications, there is a growing demand to support devices being connected to multiple Internet service providers simultaneously. For example, every modern smartphone already provides at least mobile broadband (UMTS, LTE) as well as Wi-Fi interfaces. This multi-homing property can be used for resilience, but there is also an increasing interest in making use of concurrent multipath transport. That is, multiple network paths can be utilised simultaneously, in order to improve the payload throughput for applications like big data or cloud computing. In this paper, we examine the performance of multi-path transport in real-world Internet setups, based on Multi-Path TCP (MPTCP) in the NORNET testbed for multi-homed systems. However, systems in such challenging setups need proper configuration. Therefore, we particularly would like to highlight the performance impact of different path management and congestion control settings in such realistic scenarios.

The lithium-ion battery plays an increasingly im-portant role in energy storage and electric vehicles with the development of renewable energy. To ensure the safe operation of the lithium-ion battery system in application, the battery management system (BMS) is often equipped to monitor the battery cells in real-time. However, due to the inaccuracy and high cost, the traditional BMS is difficult to be adapted to the large-scale lithium-ion battery system, which will be increasingly common in the future. To overcome these two shortcomings, a cloud-end collaboration BMS framework is proposed in this study, which takes advantage of cloud computing and machine learning to monitor the numerous battery cells. In addition, based on this cloud-end collaboration BMS framework, the gated recurrent unit neural network and transfer learning algorithm are used to estimate the state-of-charge of different batteries. And the accuracy and flexibility of the method have been validated through experiments.