Safety-critical Receding-horizon Motion Planning and Containment Control of Autonomous Surface Vehicles via Neurodynamic Optimization

Authors: guanghao  lyu
zhouhua  peng
dan  wang
Venue: IEEE_org_ICIST_2024_Conference
Published: 2024-08-14 09:24:51

Abstract: This paper addresses the safety-certiﬁed motion planning and containment control of under-actuated autonomous
surface vehicles subject to model uncertainties, external disturbances, and input constraints in the presence of stationary and
moving obstacles. A three-level modular control architecture is proposed with a trajectory generation module at its planning
level, an adaptive guidance module at its guidance level, and a kinetic control module at its control level. Speciﬁcally, at the
planning level, a safety-certiﬁed containment trajectory generator is designed to generate safe trajectories over a rolling time
window to achieve containment formation and collision avoidance  with neighboring ASVs, stationary obstacles, and moving
obstacles via dynamic control barrier functions and two-timescale neurodynamic optimization models. At the guidance level, an
adaptive line-of-sight guidance law is developed based on a ﬁnite-time predictor to estimate unknown sideslip angles and
generate guidance commands. At the control level, an optimal control law is designed based on ﬁnite-time neural predictors
and control Lyapunov functions for the autonomous surface vehicle with input constraints to follow the desired guidance
commands. The effectiveness and characteristics of the proposed method are demonstrated via simulations and hardware-in-theloop
 experiments for cooperative exploration.

Main paper: 
 : 
 Abstract-This paper addresses the safety-certified motion planning and containment control of under-actuated autonomous surface vehicles subject to model uncertainties, external disturbances, and input constraints in the presence of stationary and moving obstacles. A three-level modular control architecture is proposed with a trajectory generation module at its planning level, an adaptive guidance module at its guidance level, and a kinetic control module at its control level. Specifically, at the planning level, a safety-certified containment trajectory generator is designed to generate safe trajectories over a rolling time window to achieve containment formation and collision avoidance with neighboring ASVs, stationary obstacles, and moving obstacles via dynamic control barrier functions and two-timescale neurodynamic optimization models. At the guidance level, an adaptive line-of-sight guidance law is developed based on a finite-time predictor to estimate unknown sideslip angles and generate guidance commands. At the control level, an optimal control law is designed based on finite-time neural predictors and control Lyapunov functions for the autonomous surface vehicle with input constraints to follow the desired guidance commands. The effectiveness and characteristics of the proposed method are demonstrated via simulations and hardware-in-theloop experiments for cooperative exploration.Index Terms-Autonomous surface vehicles, safety-certified motion planning, containment control, dynamic control barrier function, two-timescale neurodynamic optimization model. 
 
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