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

Connectivity Preservation in Multi-Agent Systems using Model Predictive Control

Flocking of multiagent systems is one of the basic behaviors in the field of control of multiagent systems and it is an essential element of many real-life applications. Such systems under various network structures and environment modes have been extensively studied in the past decades. Navigation of agents in a leader-follower structure while operating in environments with obstacles is particularly challenging. One of the main challenges in flocking of multiagent systems is to preserve connectivity. Gradient descent method is widely utilized to achieve this goal. But the main shortcoming of applying this method for the leader-follower structure is the need for continuous data transmission between agents and/or the preservation of a fixed connection topology. In this research, we propose an innovative model predictive controller based on a potential field that maintains the connectivity of a flock of agents in a leader-follower structure with dynamic topology. The agents navigate through an environment with obstacles that form a path leading to a certain target. Such a control technique avoids collisions of followers with each other without using any communication links while following their leader which navigates in the environment through potential functions for modelling the neighbors and obstacles. The potential field is dynamically updated by introducing weight variables in order to preserve connectivity among the followers as we assume only the leader knows the target position. The values of these weights are changed in real-time according to trajectories of the agents when the critical neighbors of each agent is determined. We compare the performance of our predictive-control based algorithm with other approaches. The results show that our algorithm causes the agents to reach the target in less time. However, our algorithm faces more deadlock cases when the agents go through relatively narrow paths. Due to the consideration of the input costs in our controller, the group of agents reaching the target faster does not necessarily result in the followers consuming more energy than the leader

Development of Path Following and Cooperative Motion Control Algorithms for Autonomous Underwater Vehicles

Research on autonomous underwater vehicle (AUV) is motivating and challenging owing to their specific applications such as defence, mine counter measure, pipeline inspections, risky missions e.g. oceanographic observations, bathymetric surveys, ocean floor analysis, military uses, and recovery of lost man-made objects. Motion control of AUVs is concerned with navigation, path following and co-operative motion control problems. A number of control complexities are encountered in AUV motion control such as nonlinearities in mass matrix, hydrodynamic terms and ocean currents. These pose challenges to develop efficient control algorithms such that the accurate path following task and effective group co-ordination can be achieved in face of parametric uncertainties and disturbances and communication constraints in acoustic medium. This thesis first proposes development of a number of path following control laws and new co-operative motion control algorithms for achieving successful motion control objectives. These algorithms are potential function based proportional derivative path following control laws, adaptive trajectory based formation control, formation control of multiple AUVs steering towards a safety region, mathematical potential function based flocking control and fuzzy potential function based flocking control. Development of a path following control algorithm aims at generating appropriate control law, such that an AUV tracks a predefined desired path. In this thesis first path following control laws are developed for an underactuated (the number of inputs are lesser than the degrees of freedom) AUV. A potential function based proportional derivative (PFPD) control law is derived to govern the motion of the AUV in an obstacle-rich environment (environment populated by obstacles). For obstacle avoidance, a mathematical potential function is exploited, which provides a repulsive force between the AUV and the solid obstacles intersecting the desired path. Simulations were carried out considering a special type of AUV i.e. Omni Directional Intelligent Navigator (ODIN) to study the efficacy of the developed PFPD controller. For achieving more accuracy in the path following performance, a new controller (potential function based augmented proportional derivative, PFAPD) has been designed by the mass matrix augmentation with PFPD control law. Simulations were made and the results obtained with PFAPD controller are compared with that of PFPD controlle

ADVANCES IN MULTI-AGENT FLOCKING: CONTINUOUS-TIME AND DISCRETE-TIME ALGORITHMS

We present multi-agent control methods that address flocking in continuous-time and discrete-time settings. The method is decentralized, that is, each agents controller relies on local sensing to determine the relative positions and velocities of nearby agents. In the continuous-time setting, each agent has double-integrator dynamics. In the discrete-time setting, each agent has the discrete-time double-integrator dynamics obtained by sampling the continuous-time double integrator and applying a zero-order hold on the control input. We demonstrate using analysis, numerical simulations, and experimental demonstrations that agents using the flocking methods converge to flocking formations and follow the centralized leader (if applicable)

Design and Development of an Integrated Mobile Robot System for Use in Simple Formations

In recent years, formation control of autonomous unmanned vehicles has become an active area of research with its many broad applications in areas such as transportation and surveillance. The work presented in this thesis involves the design and implementation of small unmanned ground vehicles to be used in leader-follower formations. This mechatronics project involves breadth in areas of mechanical, electrical, and computer engineering design. A vehicle with a unicycle-type drive mechanism is designed in 3D CAD software and manufactured using 3D printing capabilities. The vehicle is then modeled using the unicycle kinematic equations of motion and simulated in MATLAB/Simulink. Simple motion tasks are then performed onboard the vehicle utilizing the vehicle model via software, and leader-follower formations are implemented with multiple vehicles

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

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 Review of Consensus-based Multi-agent UAV Implementations

In this paper, a survey on distributed control applications for multi Unmanned Aerial Vehicles (UAVs) systems is proposed.The focus is on consensus-based control, and both rotary-wing and fixed-wing UAVs are considered. On one side, the latest experimental configurations for the implementation of formation flight are analysed and compared for multirotor UAVs. On the other hand, the control frameworks taking into account the mobility of the fixed-wing UAVs performing target tracking are considered. This approach can be helpful to assess and compare the solutions for practical applications of consensus in UAV swarms

Adaptive Formation Control of Cooperative Multi-Vehicle Systems

The literature comprises many approaches and results for the formation control of multi-vehicle systems; however, the results established for the cases where the vehicles contain parametric uncertainties are limited. Motivated by the need for explicit characterization of the effects of uncertainties on multi-vehicle formation motions, we study distributed adaptive formation control of multi-vehicle systems in this thesis, focusing on different interrelated sub-objectives. We first examine the cohesive motion control problem of minimally persistent formations of autonomous vehicles. Later, we consider parametric uncertainties in vehicle dynamics in such autonomous vehicle formations. Following an indirect adaptive control approach and exploiting the features of the certainty equivalence principle, we propose control laws to solve maneuvering problem of the formations, robust to parametric modeling uncertainties. Next, as a formation acquisition/closing ranks problem, we study the adaptive station keeping problem, which is defined as positioning an autonomous mobile vehicle $A$ inside a multi-vehicle network, having specified distances from the existing vehicles of the network. In this setting, a single-integrator model is assumed for the kinematics for the vehicle $A$, and $A$ is assumed to have access to only its own position and its continuous distance measurements to the vehicles of the network. We partition the problem into two sub-problems; localization of the existing vehicles of the network using range-only measurements and motion control of $A$ to its desired location within the network with respect to other vehicles. We design an indirect adaptive control scheme, provide formal stability and convergence analysis and numerical simulation results, demonstrating the characteristics and performance of the design. Finally, we study re-design of the proposed station keeping scheme for the more challenging case where the vehicle $A$ has non-holonomic motion dynamics and does not have access to its self-location information. Overall, the thesis comprises methods and solutions to four correlated formation control problems in the direction of achieving a unified distributed adaptive formation control framework for multi-vehicle systems