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

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Formation control of nonholonomic wheeled mobile robots using adaptive distributed fractional order fast terminal sliding mode control

In this paper, an adaptive distributed formation controller for wheeled nonholonomic mobile robots is developed. The dynamical model of the robots is first derived by employing the Euler-Lagrange equation while taking into consideration the presence of disturbances and uncertainties in practical applications. Then, by incorporating fractional calculus in conjunction with fast terminal sliding mode control and consensus protocol, a robust distributed formation controller is designed to assure a fast and finite-time convergence of the robots towards the required formation pattern. Additionally, an adaptive mechanism is integrated to effectively counteract the effects of disturbances and uncertain dynamics. Moreover, the suggested control scheme's stability is theoretically proven through the Lyapunov theorem. Finally, simulation outcomes are given in order to show the enhanced performance and efficiency of the suggested control technique

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

Leader-Follower Formation Control for Underwater Transportation using Multiple Autonomous Underwater Vehicles

The successful ability to conduct underwater transportation using multiple autonomous underwater vehicles (AUVs) is important for the commercial sector to undertake precise underwater installations on large modules, whilst for the military sector it has the added advantage of improved secrecy for clandestine operations. The technical requirements are the stability of the payload and internal collision avoidance while keeping track of the desired trajectory considering the underwater effects. Here, a leader-follower formation control strategy was developed and implemented on the transportation system of AUVs. PID controllers were used for the vehicles and a linear feedback controller for maintaining the formation. A Kalman Filter (KF) was designed to estimate the full state of the leader under disturbance, noise and limited sensor readings. The results demonstrate that though the technical requirements are met, the thrust oscillations under disturbance and noise produce the undesired heading angles

Coordinated multi-robot formation control

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

Nonlinear control of multiple mobile manipulator robots transporting a rigid object in coordination

This doctoral thesis proposes and validates experimentally nonlinear control strategies for a group of mobile manipulator robots transporting a rigid object in coordination. This developed approach ensures trajectory tracking in Cartesian space in the presence of parameter uncertainty and undesirable disturbances. The objective of the creation of robots in the early sixties was to relieve man of certain hard jobs such as: handling a heavy object, and repetitive tasks which are often tiring or even sometimes infeasible manually. Following this situation, several types of manipulator robots were created. Naturally, the need for robots having both locomotion and manipulation capabilities has led to the creation of the mobile manipulators. Typical examples of mobile manipulators, more or less automated, are the cranes mounted on trucks , the satellite arms, the deep-sea exploration submarines, or extra-planetary exploration vehicles. Some operations requiring the handling of a heavy object are difficult to achieve by a single mobile manipulator. These operations require a coordination of several mobile manipulators to move or transport a heavy object in common. However, this complicates the robotic system as its control design complexity increases greatly. The problem of controlling the mechanical system forming a closed kinematic chain mechanism lies in the fact that it imposes a set of kinematic constraints on the coordination of the position and velocity of the mobile manipulator. Therefore, there is a reduction in the degrees of freedom for the entire system. Further, the internal forces of the object produced by all mobile manipulators should be controlled. This thesis work was focused on developing a consistent control technique for a group of mobile manipulator robots executing a task in coordination. Different nonlinear controllers were simulated and experimentally applied to multiple mobile manipulator system transporting a rigid object in coordination. To achieve all objectives of this thesis, as a first step, an experimental platform was developed and mounted in the laboratory of GREPCI-ETS to implement and validate the different designed control laws. In the second step, several adaptive coordinated motion/force tracking control laws were applied, ensuring that the desired trajectory can excellently tracked under uncertainties parameters and disturbances

Adaptive and Optimal Motion Control of Multi-UAV Systems

This thesis studies trajectory tracking and coordination control problems for single and multi unmanned aerial vehicle (UAV) systems. These control problems are addressed for both quadrotor and fixed-wing UAV cases. Despite the fact that the literature has some approaches for both problems, most of the previous studies have implementation challenges on real-time systems. In this thesis, we use a hierarchical modular approach where the high-level coordination and formation control tasks are separated from low-level individual UAV motion control tasks. This separation helps efficient and systematic optimal control synthesis robust to effects of nonlinearities, uncertainties and external disturbances at both levels, independently. The modular two-level control structure is convenient in extending single-UAV motion control design to coordination control of multi-UAV systems. Therefore, we examine single quadrotor UAV trajectory tracking problems to develop advanced controllers compensating effects of nonlinearities and uncertainties, and improving robustness and optimality for tracking performance. At fi rst, a novel adaptive linear quadratic tracking (ALQT) scheme is developed for stabilization and optimal attitude control of the quadrotor UAV system. In the implementation, the proposed scheme is integrated with Kalman based reliable attitude estimators, which compensate measurement noises. Next, in order to guarantee prescribed transient and steady-state tracking performances, we have designed a novel backstepping based adaptive controller that is robust to effects of underactuated dynamics, nonlinearities and model uncertainties, e.g., inertial and rotational drag uncertainties. The tracking performance is guaranteed to utilize a prescribed performance bound (PPB) based error transformation. In the coordination control of multi-UAV systems, following the two-level control structure, at high-level, we design a distributed hierarchical (leader-follower) 3D formation control scheme. Then, the low-level control design is based on the optimal and adaptive control designs performed for each quadrotor UAV separately. As particular approaches, we design an adaptive mixing controller (AMC) to improve robustness to varying parametric uncertainties and an adaptive linear quadratic controller (ALQC). Lastly, for planar motion, especially for constant altitude flight of fixed-wing UAVs, in 2D, a distributed hierarchical (leader-follower) formation control scheme at the high-level and a linear quadratic tracking (LQT) scheme at the low-level are developed for tracking and formation control problems of the fixed-wing UAV systems to examine the non-holonomic motion case. The proposed control methods are tested via simulations and experiments on a multi-quadrotor UAV system testbed

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Adaptive consensus based formation control of unmanned vehicles

Over the past decade, the control research community has given significant attention to formation control of multiple unmanned vehicles due to a variety of commercial and defense applications. Consensus-based formation control is considered to be more robust and reliable when compared to other formation control methods due to scalability and inherent properties that enable the formation to continue even if one of the vehicles experiences a failure. In contrast to existing methods on formation control where the dynamics of the vehicles are neglected, this dissertation in the form of four papers presents consensus-based formation control of unmanned vehicles-both ground and aerial, by incorporating the vehicle dynamics. First, neural networks (NN)-based optimal adaptive consensus-based formation control over finite horizon is presented for networked mobile robots or agents in the presence of uncertain robot/agent dynamics and communication. In the second paper, a hybrid automaton is proposed to control the nonholonomic mobile robots in two discrete modes: a regulation mode and a formation keeping mode in order to overcome well-known stabilization problem. The third paper presents the design of a distributed consensus-based event-triggered formation control of networked mobile robots using NN in the presence of uncertain robot dynamics to minimize communication. All these papers assume state availability. Finally, the fourth paper extends the consensus effort by introducing the development of a novel nonlinear output feedback NN-based controller for a group of quadrotor UAVs --Abstract, page iv