A model predictive controller for robots to follow a virtual leader

SUMMARYIn this paper, we develop a model predictive control (MPC) scheme for robots to follow a virtual leader. The stability of this control scheme is guaranteed by adding a terminal state penalty to the cost function and a terminal state region to the optimization constraints. The terminal state region is found by analyzing the stability. Also a terminal state controller is defined for this control scheme. The terminal state controller is a virtual controller and is never used in the control process. Two virtual leader-following formation models are studied. Simulations on different formation patterns are provided to verify the proposed control strategy.</jats:p

Coordinated multi-robot formation control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

A Distributed Epigenetic Shape Formation and Regeneration Algorithm for a Swarm of Robots

Living cells exhibit both growth and regeneration of body tissues. Epigenetic Tracking (ET), models this growth and regenerative qualities of living cells and has been used to generate complex 2D and 3D shapes. In this paper, we present an ET based algorithm that aids a swarm of identically-programmed robots to form arbitrary shapes and regenerate them when cut. The algorithm works in a distributed manner using only local interactions and computations without any central control and aids the robots to form the shape in a triangular lattice structure. In case of damage or splitting of the shape, it helps each set of the remaining robots to regenerate and position themselves to build scaled down versions of the original shape. The paper presents the shapes formed and regenerated by the algorithm using the Kilombo simulator.Comment: 8 pages, 9 figures, GECCO-18 conferenc

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments

Decentralized High Level Controller for Formation Flight Control of UAVs

International audienceThe main contribution of this paper is the design of a decentralized and tuning-less high level controller able to maintain without tracking errors a Leader-Follower (LF) configuration in case of lack or degraded communications (latencies, loss…) between the leader and followers UAVs. The high level controller only requires simple tunings and rests on a predictive filtering algorithm and a first order dynamic model to recover an estimation of the leader UAV velocities and avoid the tracking errors

ROS-based Controller for a Two-Wheeled Self-Balancing Robot

In this article, a controller based on a Robot Operating System (ROS) for a two-wheeled self-balancing robot is designed. The proposed ROS architecture is open, allowing the integration of different sensors, actuators, and processing units. The low-cost robot was designed for educational purposes. It used an ESP32 microcontroller as the central unit, an MPU6050 Inertial Measurement Unit sensor, DC motors with encoders, and an L298N integrated circuit as a power stage. The mathematical model is analyzed through Newton-Euler and linearized around an equilibrium point. The control objective is to self-balance the robot to the vertical axis in the presence of disturbances. The proposed control is based on a bounded saturation, which is lightweight and easy to implement in embedded systems with low computational resources. Experimental results are performed in real-time under regulation, conditions far from the equilibrium point, and rejection of external disturbances. The results show a good performance, thus validating the mechanical design, the embedded system, and the control scheme. The proposed ROS architecture allows the incorporation of different modules, such as mapping, autonomous navigation, and manipulation, which contribute to studying robotics, control, and embedded systems

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Implementation consensus algorithm and leader-follower of multi-robot system formation

Robot technology has recently been applied to many applications to help human activities. Mobile Robot is one of the most flexible robot technology. This research uses a mobile robot designed using an omnidirectional wheel for the movement mechanism. Coordination and control of multi-robots can be assigned to perform any task from a different kind of field. Therefore, this paper aims to develop a multi-robot system to form a formation to do the task. The multi-robot system consists of three units Mobile Robot. The formation system will be built based on a coordinate point determined by a consensus point. The leader-follower topology is used to determine the orientation of the robot. ROS (Robot Operating System) is used as middleware to create a multi-robot system. The Open Base package in Gazebo Simulator is also used to simulate the movement of the multi-robot. From three test scenarios, this research results show that all the robots can do and follow the tasks simulated in the Gazebo with an average accuracy of 88.14%. Furthermore, no feedback from the robot to the Gazebo Simulator affects the robot's accuracy average below 90%.

Implementation consensus algorithm and leader-follower of multi-robot system formation

Robot technology has recently been applied to many applications to help human activities. Mobile Robot is one of the most flexible robot technology. This research uses a mobile robot designed using an omnidirectional wheel for the movement mechanism. Coordination and control of multi-robots can be assigned to perform any task from a different kind of field. Therefore, this paper aims to develop a multi-robot system to form a formation to do the task. The multi-robot system consists of three units Mobile Robot. The formation system will be built based on a coordinate point determined by a consensus point. The leader-follower topology is used to determine the orientation of the robot. ROS (Robot Operating System) is used as middleware to create a multi-robot system. The Open Base package in Gazebo Simulator is also used to simulate the movement of the multi-robot. From three test scenarios, this research results show that all the robots can do and follow the tasks simulated in the Gazebo with an average accuracy of 88.14%. Furthermore, no feedback from the robot to the Gazebo Simulator affects the robot's accuracy average below 90%.