In this paper, we address the problem of autonomous exploration in unknown environments for ground mobile robots with deep reinforcement learning (DRL). To effectively explore unknown environments, we construct an exploration graph considering historical trajectories, frontier waypoints, landmarks, and obstacles. Meanwhile, to take full advantage of the spatiotemporal feature and historical information in the autonomous exploration task, we propose a novel network called Spatiotemporal Neural Network on Graph (Graph-STNN). Specifically, the proposed Graph-STNN extracts the spatial feature using graph convolutional network (GCN) and the temporal feature using temporal convolutional network (TCN). Then, gated recurrent unit (GRU) is performed to synthesize the spatial feature, the temporal feature, and the historical state information into the current state feature. Combined with DRL, our Graph-STNN helps estimation of the optimal target point through extracted hybrid features. The simulation experiment shows that our approach is more effective than the GCN-based approach and the information entropy-based approach. Moreover, Graph-STNN also performs better generalization ability than GCN-based, information entropy-based, and random methods. Finally, we validate our approach on the simulation platform Stage with the actual robot model.

Solid-state LiDAR offers multiple advantages over mechanism mechanical LiDAR, including higher durability, improved coverage ratio, and lower prices. However, solid-state LiDARs typically possess a narrow field of view, making them less suitable for odometry and mapping systems, especially for mobile autonomous systems. To address this issue, we propose a novel rotating solid-state LiDAR system that incorporates a servo motor to continuously rotate the solid-state LiDAR, expanding the horizontal field of view to 360°. Additionally, we propose a multi-sensor fusion odometry and mapping algorithm for our developed sensory system that integrates an IMU, wheel encoder, motor encoder and the LiDAR into an iterated Kalman filter to obtain a robust odometry estimation. Through comprehensive experiments, we demonstrate the effectiveness of our proposed approach in both outdoor open environments and narrow indoor environments.

With multi-sensors embedded, flexible unmanned aerial vehicles (UAVs) can collect sensory data and provide various services for all walks of life. However, limited computing capability and battery energy put a great burden on UAVs to handle emerging compute-intensive applications, necessitating them to resort to innovative computation offloading technique to guarantee quality of service. Existing research mainly focuses on solving the offloading problem under known global information, or applying centralized offloading frameworks when facing dynamic environments. Yet, the maneuverability of today’s UAVs, their large-scale clustering, and their increasing operation in the environment with unrevealed information pose huge challenges to previous work. In this paper, in order to enhance the long-term offloading performance and scalability for multi-UAVs, we develop a decentralized offloading scheme named DELOFF with the support of mobile edge computing (MEC). DELOFF considers the information uncertainty caused by the dynamic environment, uses UAV-to-everything (U2X)-assisted heterogeneous networks to extend network resources and offloading flexibility, and tackles the joint strategy making related to computation mode, network selection, and offloading allocation for multi-UAVs. Specifically, the optimization problem of multi-UAVs is addressed by the proposed offloading algorithm based on a multi-arm bandit learning model, where each UAV itself can adaptively assess the offloading link quality through the fuzzy logic-based pre-screening mechanism designed. The convergence and effectiveness of the DELOFF proposed are also demonstrated in simulations. And, the results confirm that DELOFF is superior to the four benchmarks in many respects, such as reduced consumed energy and delay in the task completion of UAVs.

This paper focuses on generating distributed flocking strategies via imitation learning. The primary motivation is to improve the swarm robustness and achieve better consistency while respecting the communication constraints. This paper first proposes a quantitative metric of swarm robustness based on entropy evaluation. Then, the graph importance consistency is also proposed, which is one of the critical goals of the flocking task. Moreover, the importance-correlated directed graph convolutional networks (IDGCNs) are constructed for multidimensional feature extraction and structure-related aggregation of graph data. Next, by employing IDGCNs-based imitation learning, a distributed and scalable flocking strategy is obtained, and its performance is very close to the centralized strategy template while considering communication constraints. To speed up and simplify the training process, we train the flocking strategy with a small number of agents and set restrictions on communication. Finally, various simulation experiments are executed to verify the advantages of the obtained strategy in terms of realizing the swarm consistency and improving the swarm robustness. The results also show that the performance is well maintained while the scale of agents expands (tested with 20, 30, 40 robots).

This paper presents a consensus-based payload algorithm (CBPA) to deal with the condition of robots' capability decrease for multi-robot task allocation. During the execution of complex tasks, robots' capabilities could decrease with the consumption of payloads, which causes a problem that the robot coalition would not meet the tasks' requirements in real time. The proposed CBPA is an enhanced version of the consensus-based bundle algorithm (CBBA) and comprises two primary core phases: the payload bundle construction and consensus phases. In the payload bundle construction phase, CBPA introduces a payload assignment matrix to track the payloads carried by the robots and the demands of multi-robot tasks in real time. Then, robots share their respective payload assignment matrix in the consensus phase. These two phases are iterated to dynamically adjust the number of robots performing multi-robot tasks and the number of tasks each robot performs and obtain conflict-free results to ensure that the robot coalition meets the demand and completes all tasks as quickly as possible. Physical experiment shows that CBPA is appropriate in complex and dynamic scenarios where robots need to collaborate and task requirements are tightly coupled to the robots' payloads. Numerical experiments show that CBPA has higher total task gains than CBBA.