Safety-critical model predictive control with control barrier function for dynamic obstacle avoidance

In this paper, a safety critical control scheme for a nonholonomic robot is developed to generate control signals that result in optimal obstacle-free paths through dynamic environments. A barrier function is used to obtain a safety envelope for the robot. We formulate the control synthesis problem as an optimal control problem that enforces control barrier function (CBF) constraints to achieve obstacle avoidance. A nonlinear model predictive control (NMPC) with CBF is studied to guarantee system safety and accomplish optimal performance at a short prediction horizon, which reduces computational burden in real-time NMPC implementation. An obstacle avoidance constraint under the Euclidean norm is also incorporated into NMPC to emphasize the effectiveness of CBF in both point stabilization and trajectory tracking problem of the robot. The performance of the proposed controller achieving both static and dynamic obstacle avoidance is verified using several simulation scenarios.Comment: 6 pages, 6 figures, IFAC World Congress 202

Reinforcement Learning-Enhanced Control Barrier Functions for Robot Manipulators

In this paper we present the implementation of a Control Barrier Function (CBF) using a quadratic program (QP) formulation that provides obstacle avoidance for a robotic manipulator arm system. CBF is a control technique that has emerged and developed over the past decade and has been extensively explored in the literature on its mathematical foundations, proof of set invariance and potential applications for a variety of safety-critical control systems. In this work we will look at the design of CBF for the robotic manipulator obstacle avoidance, discuss the selection of the CBF parameters and present a Reinforcement Learning (RL) scheme to assist with finding parameters values that provide the most efficient trajectory to successfully avoid different sized obstacles. We then create a data-set across a range of scenarios used to train a Neural-Network (NN) model that can be used within the control scheme to allow the system to efficiently adapt to different obstacle scenarios. Computer simulations (based on Matlab/Simulink) demonstrate the effectiveness of the proposed algorithm

Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety

PID-fixed time sliding mode control for trajectory tracking of AUVs under disturbance

A novel approach is proposed for the trajectory tracking control of Autonomous Underwater Vehicles (AUVs). Firstly, previous implementations of Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) are discussed and their disadvantages are highlighted in terms of fixed time convergence and the chattering phenomenon. Secondly, to improve the stability of the tracking performance and convergence of the system, a controller combining PID and Fixed Time SMC (FTSMC) is proposed for AUVs. The proposed controller is then applied to simulate a 6 Degrees-of-Freedom (6DOF) BlueRov2 underwater robot and the results are analytically discussed. The simulation results show that the proposed PID-FTSMC controller can accurately control the BlueRov2 in trajectory tracking operations with faster convergence and no oscillations around the set reference, even under disturbance.<br/

Adaptive fixed-time control for uncertain surface vessels with output constraints using barrier Lyapunov function

This paper proposes a fixed-time control for trajectory tracking of a marine surface vessel with model uncertainties and prescribed position tracking constraints. The vessel tracking is achieved with fixed-time backstepping using a universal barrier function to constrain the position tracking error. We develop and analyse the controller for a general single-input single-output (SISO) system before extending to the multiple-input multiple-output marine surface vehicle (MIMO MSV) control. Numerical simulations based on a physical test system are provided to demonstrate the efficacy of the solution. We show the proposed controller can provide lower error tracking for the position states and also show that the barrier Lyapunov function (BLF) type control provides to some degree capability to maintain tracking in the presence of unknown disturbance.<br/

Enhancing mobile robot navigation safety and efficiency through NMPC with relaxed CBF in dynamic environments

In this paper, a safety-critical control strategy for a nonholonomic robot is developed to generate control signals that result in optimal, obstacle-free paths throughdynamic environments. A barrier function is employed to define a safety envelope for the robot. We formulate the control synthesis problem as an optimal control problem thatenforces Control Lyapunov Function (CLF) constraints for system stability as well as safety-critical constraints using Control Barrier Function (CBF) with a relaxing technique.We investigate an approach that integrates Nonlinear Model Predictive Control (NMPC) with CLF and CBF to ensure system safety and facilitate optimal performance within ashort prediction horizon, thereby reducing the computational burden in real-time NMPC implementation. Additionally, we incorporate an obstacle avoidance constraint based on the Euclidean norm into the NMPC framework, showcasing the CBF approach’s superiority in addressing mobile robotic systems’ point stabilisation and trajectory tracking challenges. Through extensive simulations, the proposed controller demonstrates proficiency in static and dynamic obstacle avoidance under various scenarios. Experimental validations conducted using the Husky A200 robot align with simulation results, reinforcing the applicability of our proposed approach in real-world scenarios, notably improving the computational efficiency and safety in practical mobile robot applications