Publications

Fully distributed prescribed-time consensus control of multiagent systems under fixed and switching topologies

Published in This paper is accepted by information science, 2023

In this paper, we provide a Fully distributed prescribed-time consensus control of multiagent systems under fixed and switching topologies.

Recommended citation: Lv J, Wang C, Liu B, et al. Fully distributed prescribed-time consensus control of multiagent systems under fixed and switching topologies[J]. Information Sciences, 2023, 648: 119538. 10.1016/j.ins.2023.119538

Robust Depth-Aided Visual-Inertial-Wheel Odometry for Mobile Robots

Published in This paper is accepted by IEEE Transactions on Industrial Electronics, 2023

This article introduces visual-depth-inertial-wheel odometry (VDIWO), a robust approach for real-time localization of mobile robots in indoor and outdoor scenarios. Notably, VDIWO achieves accurate localization without relying on prior information.

Recommended citation: Zhao X, Li Q, Wang C, Liu B. Robust Depth-Aided Visual-Inertial-Wheel Odometry for Mobile Robots[J]. IEEE Transactions on Industrial Electronics, 2023. https://ieeexplore.ieee.org/abstract/document/10297569

Task Assignment of UAV Swarms Based on Deep Reinforcement Learning

Published in This paper is accepted by Drones, 2023

In this paper, we propose the Extensible Multi-Agent Deep Deterministic Policy Gradient (Ex-MADDPG) algorithm, which builds on the MADDPG framework. The Ex-MADDPG algorithm improves the robustness and scalability of the assignment algorithm by incorporating local communication, mean simulation observation, a synchronous parameter-training mechanism, and a scalable multiple-decision mechanism.

Recommended citation: Liu B, Wang S, Li Q, et al. Task Assignment of UAV Swarms Based on Deep Reinforcement Learning[J]. Drones, 2023, 7(5): 297. https://www.mdpi.com/2504-446X/7/5/297

Empathy structure in multi-agent system with the mechanism of self-other separation: Design and analysis from a random walk view

Published in This paper is accepted by COGNITIVE SYSTEMS RESEARCH, 2023

In a socialized multi-agent system, the preferences of individuals will be inevitably influenced by others. This paper introduces an extended empathy structure to characterize the coupling process of preferences under specific relations and make it cover scenarios including human society, human-machine system, and even abiotic engineering applications. In this model, empathy is abstracted as a stochastic experience process in the form of Markov chain, and the coupled empathy utility is defined as the expectation of obtaining preferences under the corresponding probability distribution. The self-other separation is the core concept with which our structure can exhibit social attributes, including attraction of implicit states, inhibition of excessive empathy, attention of empathetic targets, and anisotropy of the utility distribution. Compared with the previous empirical models, our model has a better performance on the data set and can provide a new perspective for designing and analyzing the cognitive layer of the human-machine network, as well as the information fusion and semi-supervised clustering methods in engineering.

Recommended citation: Chen J, Liu B, Qu Z, et al. Empathy structure in multi-agent system with the mechanism of self-other separation: Design and analysis from a random walk view[J]. Cognitive Systems Research, 2023, 79: 175-189. https://www.sciencedirect.com/science/article/abs/pii/S1389041723000189

Robust Depth-Aided RGBD-Inertial Odometry for Indoor Localization

Published in This paper is accepted by Measurement, 2023

RGB-D cameras such as RealSense and Structure Sensors have been widely used in most robotics systems. This paper presents a system for estimating the trajectory of an RGB-D camera and IMU in indoor environments. The system uses a novel relative pose estimation method that utilizes depth measurements and epipolar constraints for initialization. An adaptive depth estimation method is also proposed, which fuses a depth uncertainty model and multi-view triangulation. In the backend, a sliding window framework is used to optimize the system state by minimizing the residuals of pre-integrated IMU, 3D features re-projection, and 2D features epipolar constraint. The effectiveness of the system is evaluated using publicly available datasets with ground truth trajectories.

Recommended citation: Zhao X, Li Q, Wang C, Liu B. Robust Depth-Aided RGBD-Inertial Odometry for Indoor Localization[J]. Measurement, 2023, 209: 112487. https://www.sciencedirect.com/science/article/abs/pii/S0263224123000519

Social decision-making in a large-scale MultiAgent system considering the influence of empathy

Published in This paper is accepted by Applied Intelligence, 2022

Empathy is the ability to spontaneously or purposefully place oneself in another’s situation. Under the continuous effect of empathy, an individual’s preference for things will inevitably be affected by the local and non-local social environment. Inspired by neuropsychology, this paper constructs an extended empathy model to compensate for the shortcomings of previous models in describing the global preference (utility) coupling between individuals, and analyzes how to make efficient decisions based on this model in a large-scale multiagent system.

Recommended citation: Chen J, Liu B, Zhang D, et al. Social decision-making in a large-scale MultiAgent system considering the influence of empathy[J]. Applied Intelligence, 2023, 53(9): 10068-10095. https://link.springer.com/article/10.1007/s10489-022-03933-2

Continuous-time Gaussian Process Trajectory Generation for Multi-robot Formation via Probabilistic Inference

Published in This paper is accepted to IROS2021, 2021

In this paper, we extend a famous motion planning approach GPMP2 to multi-robot cases. Our proposed method can generate trajectories efficiently for a team of robots which have to get through a width-varying area by adaptive formation change.

Recommended citation: S. Guo, B. Liu, S. Zhang, J. Guo and C. Wang, "Continuous-time Gaussian Process Trajectory Generation for Multi-robot Formation via Probabilistic Inference," 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 9247-9253, doi: 10.1109/IROS51168.2021.9636573. https://ieeexplore.ieee.org/document/9636573

An Improved Dyna-Q Algorithm for Mobile Robot Path Planning in Unknown Dynamic Environment

Published in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021

This article deals with the problem of mobile robot path planning in an unknown environment that contains both static and dynamic obstacles, utilizing a reinforcement learning approach. We propose an improved Dyna-Q algorithm, which incorporates heuristic search strategies, simulated annealing mechanism, and reactive navigation principle into Q-learning based on the Dyna architecture. A novel action-selection strategy combining ϵ-greedy policy with the cooling schedule control is presented, which, together with the heuristic reward function and heuristic actions, can tackle the exploration-exploitation dilemma and enhance the performance of global searching, convergence property, and learning efficiency for path planning. The proposed method is superior to the classical Q-learning and Dyna-Q algorithms in an unknown static environment, and it is successfully applied to an uncertain environment with multiple dynamic obstacles in simulations. Further, practical experiments are conducted by integrating MATLAB and robot operating system (ROS) on a physical robot platform, and the mobile robot manages to find a collision-free path, thus fulfilling autonomous navigation tasks in the real world.

Recommended citation: M. Pei, H. An, B. Liu and C. Wang, "An Improved Dyna-Q Algorithm for Mobile Robot Path Planning in Unknown Dynamic Environment," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi: 10.1109/TSMC.2021.3096935. https://ieeexplore.ieee.org/document/9495840

刘博

Publications