Article
Automation & Control Systems
Sasa V. Rakovic, Sixing Zhang, Li Dai, Yanye Hao, Yuanqing Xia
Summary: This study presents a collision avoidance model predictive control approach that ensures system stability and efficiency by introducing the notion of safe distance sets and a strategic-tactical structure, enabling real-time implementation and practical application.
IET CONTROL THEORY AND APPLICATIONS
(2021)
Article
Engineering, Aerospace
Qingyu Qu, Kexin Liu, Wei Wang, Jinhu Lu
Summary: This article focuses on the relative position tracking problem of autonomous spacecraft rendezvous with collision avoidance requirement. It proposes an exploration-adaptive deep deterministic policy gradient (DDPG) algorithm for training a definite control strategy. By introducing adaptive noise and a metalearning-based idea, it reduces energy consumption and adapts to other similar scenarios effectively.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2022)
Article
Automation & Control Systems
Yang Lyu, Jinwen Hu, Ben M. Chen, Chunhui Zhao, Quan Pan
Summary: The article studies a distributed flocking control strategy for a network of autonomous vehicles with limited communication range, focusing on collision avoidance as a necessary condition. Sufficient conditions for system feasibility and stability are provided by the proposed strategy.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Automation & Control Systems
Zhigang Xiong, Yasong Luo, Zhong Liu, Jianqiang Zhang, Zhikun Liu
Summary: This paper explores obstacle and collision avoidance under kinematical constraints in unknown environments when communication and simultaneous localization and mapping (SLAM) are not available. It proposes a strategy based on mixed-integer programming, establishing velocity constraints using a modified Barrier function for completely detected obstacles. For incompletely detected obstacles, a feasible set is created for velocity programming based on convex theory, and the contradictory constraints are addressed using the logic metric method. Additionally, the paper avoids actuator saturation by converting kinematical constraints into restrictions on magnitude, direction, and negative correlations between velocity components.
IET CONTROL THEORY AND APPLICATIONS
(2023)
Article
Nanoscience & Nanotechnology
Qiangwei Pang, Yongyong Zhu, Ye Chen, Deshi Wang, Wenkai Suo
Summary: This paper proposes a distributed Kalman model predictive control algorithm to address the perturbation of formation of multiple unmanned aerial vehicles (UAVs) subject to external disturbances and improve the accuracy of maintaining a formation in flight. The algorithm builds a UAV two-order discrete-time system model and devises a Kalman prediction model based on the standard prediction model. It determines the reference state of UAVs through desired formation configuration and neighbor Kalman optimal state estimation. A logarithmic barrier function is introduced to ensure flight safety considering the formation tracking error and input stability. Information is exchanged with neighbors using a directed and time-invariant communication topological structure. Sufficient conditions for the asymptotic stability of the formation system are defined using the Lyapunov stability theorem. Simulation results show that the algorithm effectively suppresses perturbations in the formation of UAVs caused by external disturbances, enabling the formation to handle conflicts between individual UAVs.
Article
Robotics
Cosmin Ginerica, Mihai Zaha, Florin Gogianu, Lucian Busoniu, Bogdan Trasnea, Sorin Grigorescu
Summary: The paper introduces ObserveNet Control as a vision-dynamics approach to the predictive control problem of autonomous vehicles, using a deep neural network and planner to predict future sensory data and compute safe vehicle trajectories. The method aims to learn the dynamics of the observed driving environment in a self-supervised manner, without manually specifying training labels.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Engineering, Marine
Guanghao Lv, Zhouhua Peng, Haoliang Wang, Lu Liu, Dan Wang, Tieshan Li
Summary: A distributed formation tracking control method for under-actuated unmanned surface vehicles is proposed in this paper, utilizing an extended-state-observer-based model predictive control approach. The vehicles' dynamics are transformed and uncertainties are estimated to design position and angular motion controllers, solving the problem as a constrained quadratic programming. Simulation results validate the effectiveness of the proposed method for multiple vehicles.
Article
Engineering, Multidisciplinary
YongXue Chen, Wei Dong, Ye Ding
Summary: This paper presents an efficient approach to plan smooth trajectories for an autonomous quadrotor passing through desired way-points using SCP, satisfying motion constraints by approximating the quadrotor and obstacles as geometrical shapes, solving a nonconvex optimization problem, and verifying feasibility through simulations.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2021)
Article
Automation & Control Systems
Boyang Zhang, Henri P. Gavin
Summary: This article presents a solution to the problem of multiagent navigation by extending Gauss's principle of least constraint. The solution utilizes active inequality constraints for collision avoidance and behavior control, and stabilizes them using Baumgarte's approach. Specialized solutions are proposed to address the issue of linearly dependent constraints in dense configurations.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Nan Gu, Dan Wang, Zhouhua Peng, Lu Liu
Summary: This article addresses the distributed path maneuvering of underactuated unmanned surface vehicles (USVs) with collision avoidance and connectivity preservation. It proposes an observer-based finite-time control method incorporating artificial potential field for collision avoidance and connectivity preservation, along with antidisturbance kinetic control laws. The effectiveness of the proposed method is verified through simulation results for multiple USVs with position-yaw measurements.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Yaohua Guo, Gang Chen
Summary: This article presents a robust coordination solution for nonlinear uncertain second-order multiagent networks with motion constraints, such as velocity saturation and collision avoidance. The approach utilizes a single-critic neural network-based approximate dynamic programming and exact estimation of unknown dynamics for online learning of the optimal value function and controller. By incorporating avoidance penalties into tracking variable, constructing a novel value function, and designing suitable learning algorithms, both multiagent coordination and collision avoidance are achieved simultaneously. The developed feedback-based coordination strategy ensures uniformly ultimately bounded convergence of the closed-loop dynamical stability, while strictly obeying all underlying motion constraints. The effectiveness of the proposed collision-free coordination law is demonstrated through numerical simulations.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Engineering, Aerospace
Lixiang Wang, Dong Ye, Yan Xiao, Xianren Kong
Summary: This paper proposes trajectory planning algorithms for large-scale satellite clusters reconfiguration based on sequential convex programming, considering fuel consumption and collision avoidance. The problem is formulated as a nonconvex optimal control problem with nonlinear dynamics and nonconvex path constraints. The original problem is transformed into a discrete convex optimization subproblem through linearization and discretization, considering collision and obstacle avoidance. The proposed methods achieve significant improvements in computational efficiency compared to the pseudo-spectral method as the number of satellites increases.
AEROSPACE SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Aerospace
Zengfu Wang, Jiarui Tian, Jing Fu
Summary: We aim to minimize or balance fuel consumption in the satellite formation reconfiguration process within a fixed orbit transfer time. Regardless of the initial state, the formation will eventually result in an in-plane formation. We consider constraints such as relative motion equation, initial and terminal states, collision avoidance, and maximum thrust. By introducing an unknown offset in the terminal state and using a convex-programming-based iterative approach, we successfully optimize both fuel usage and the offset of the formations, outperforming benchmarks in all numerically tested situations.
ADVANCES IN SPACE RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Xu Shang, Azim Eskandarian
Summary: Although research on emergency collision avoidance has been extensive for highways with straight or curved roads, a general method applicable to all road environments has not been thoroughly investigated. Furthermore, most current algorithms do not consider collision mitigation in emergency situations, which is crucial due to the potential lack of feasible solutions. In this study, we propose a safe controller using model predictive control and artificial potential function to address these issues. By considering the shape of objects and utilizing an inspired artificial potential function, we achieve improved success rates and reduced collision rates compared to existing methods.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Automation & Control Systems
Hongjiu Yang, Qing Li, Zhiqiang Zuo, Hai Zhao
Summary: This article examines event-triggered model predictive control for simultaneous tracking and formation of a multi-vehicle system with collision and obstacle avoidance. By establishing an event-triggered mechanism and a compatibility constraint, the safety and convergence of the system are guaranteed.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
