Safety-Control of Mobile Robots Under Time-Delay Using Barrier Certificates and a Two-Layer Predictor

Performing swift and agile maneuvers is essential for the safe operation of autonomous mobile robots. Moreover, the presence of time-delay restricts the response time of the system and hinders the safety performance. Thus, this paper proposes a modular and scalable safety-control design that utilizes the Smith predictor and barrier certificates to safely and consistently avoid obstacles with different footprints. The proposed solution includes a two-layer predictor to compensate for the time-delay in the servo-system and angle control loops. The proposed predictor configuration dramatically improves the transient performance and reduces response time. Barrier certificates are used to determine the safe range of the robot’s heading angle to avoid collisions. The proposed obstacle avoidance technique conveniently integrates with various trajectory tracking algorithms, which enhances design flexibility. The angle condition is adaptively calculated and corrects the robot’s heading angle and angular velocity. Also, the proposed method accommodates multiple obstacles and decouples the control structure from the obstacles’ shape, count, and distribution. The control structure has only eight tunable parameters facilitating control calibration and tuning in large systems of mobile robots. Extensive experimental results verify the effectiveness of the proposed safety-control.

[1]  Namyun Kim,et al.  Obstacle Magnification for 2-D Collision and Occlusion Avoidance of Autonomous Multirotor Aerial Vehicles , 2020, IEEE/ASME Transactions on Mechatronics.

[2]  Magnus Egerstedt,et al.  Hybrid Nonsmooth Barrier Functions With Applications to Provably Safe and Composable Collision Avoidance for Robotic Systems , 2019, IEEE Robotics and Automation Letters.

[3]  Xiao Liang,et al.  Swarm control with collision avoidance for multiple underactuated surface vehicles , 2019, Ocean Engineering.

[4]  M. Michaek,et al.  Vector-Field-Orientation Feedback Control Method for a Differentially Driven Vehicle , 2010, IEEE Transactions on Control Systems Technology.

[5]  Azad Ghaffari Modular Safety Control for Mobile Robots Using Barrier Certificates and Modified Feedback , 2021, 2021 American Control Conference (ACC).

[6]  Gökhan M. Atinç,et al.  Trajectory Tracking Control of Unicycle Robots with Collision Avoidance and Connectivity Maintenance , 2019, J. Intell. Robotic Syst..

[7]  Bayu Jayawardhana,et al.  Stabilization with guaranteed safety using Control Lyapunov-Barrier Function , 2016, Autom..

[8]  Hui Kong,et al.  Exponential-Condition-Based Barrier Certificate Generation for Safety Verification of Hybrid Systems , 2013, CAV.

[9]  Kun Yang,et al.  Multi-Robot Obstacle Avoidance Based on the Improved Artificial Potential Field and PID Adaptive Tracking Control Algorithm , 2019, Robotica.

[10]  Souma Chowdhury,et al.  Learning reciprocal actions for cooperative collision avoidance in quadrotor unmanned aerial vehicles , 2019, Robotics Auton. Syst..

[11]  Zhiwei Guan,et al.  A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles , 2020, Expert Syst. Appl..

[12]  Paulo Tabuada,et al.  Control Barrier Function Based Quadratic Programs for Safety Critical Systems , 2016, IEEE Transactions on Automatic Control.

[13]  Mohammad Habibur Rahman,et al.  Investigating Reduced Path Planning Strategy for Differential Wheeled Mobile Robot , 2019, Robotica.

[14]  Indra Narayan Kar,et al.  Adaptive sliding mode control of a class of nonlinear systems with artificial delay , 2017, J. Frankl. Inst..

[15]  H. Nijmeijer,et al.  Predictor-based tracking control of a mobile robot with time-delays , 2010 .

[16]  The Smith predictor, the modified Smith predictor, and the finite spectrum assignment: A comparative study , 2019, Stability, Control and Application of Time-delay Systems.

[17]  Ehud Rivlin,et al.  Predictive Driving in an Unstructured Scenario Using the Bundle Adjustment Algorithm , 2021, IEEE Transactions on Control Systems Technology.

[18]  Xiaojing Zhang,et al.  Optimization-Based Collision Avoidance , 2017, IEEE Transactions on Control Systems Technology.

[19]  Dongkyoung Chwa,et al.  Obstacle Avoidance Method for Wheeled Mobile Robots Using Interval Type-2 Fuzzy Neural Network , 2015, IEEE Transactions on Fuzzy Systems.

[20]  Miroslav Krstic,et al.  Safety Verification Using Barrier Certificates with Application to Double Integrator with Input Saturation and Zero-Order Hold , 2018, 2018 Annual American Control Conference (ACC).

[21]  Kil To Chong,et al.  Receding horizon tracking control for wheeled mobile robots with time-delay , 2008 .

[22]  Zhihua Qu,et al.  A new analytical solution to mobile robot trajectory generation in the presence of moving obstacles , 2004, IEEE Transactions on Robotics.

[23]  I-Ming Chen,et al.  Real-Time Avoidance Strategy of Dynamic Obstacles via Half Model-Free Detection and Tracking With 2D Lidar for Mobile Robots , 2021, IEEE/ASME Transactions on Mechatronics.

[24]  Roland Siegwart,et al.  Cooperative Collision Avoidance for Nonholonomic Robots , 2018, IEEE Transactions on Robotics.

[25]  Myoungho Sunwoo,et al.  Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles , 2012, IEEE Transactions on Intelligent Transportation Systems.

[26]  Azad Ghaffari Analytical Design and Experimental Verification of Geofencing Control for Aerial Applications , 2020 .

[27]  Sung Jin Yoo,et al.  A low-complexity tracker design for uncertain nonholonomic wheeled mobile robots with time-varying input delay at nonlinear dynamic level , 2017 .

[28]  Henk Nijmeijer,et al.  Smith Predictor Compensating for Vehicle Actuator Delays in Cooperative ACC Systems , 2019, IEEE Transactions on Vehicular Technology.

[29]  Tore Hägglund,et al.  Robust tuning procedures of dead-time conpensating controllers , 2001 .