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

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

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 novel coordination framework for multi-robot systems

Having made great progress tackling the basic problems concerning single-robot systems, many researchers shifted their focus towards the study of multi-robot systems (MRS). MRS were shortly found to be a perfect t for tasks considered to be hard, complex or even impossible for a single robot to perform, e.g. spatially separate tasks. One core research problem of MRS is robots' coordinated motion planning and control. Arti cial potential elds (APFs) and virtual spring-damper bonds are among the most commonly used models to attack the trajectory planning problem of MRS coordination. However, although mathematically sound, these approaches fail to guarantee inter-robot collision-free path generation. This is particularly the case when robots' dynamics, nonholonomic constraints and complex geometry are taken into account. In this thesis, a novel bio-inspired collision avoidance framework via virtual shells is proposed and augmented into the high-level trajectory planner. Safe trajectories can hence be generated for the low-level controllers to track. Motion control is handled by the design of hierarchical controllers which utilize virtual inputs. Several distinct coordinated task scenarios for 2D and 3D environments are presented as a proof of concept. Simulations are conducted with groups of three, four, ve and ten nonholonomic mobile robots as well as groups of three and ve quadrotor UAVs. The performance of the overall improved coordination structure is veri ed with very promising result

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

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Safe navigation and motion coordination control strategies for unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. Therefore, more research is still needed towards developing advanced reliable control strategies for UAVs to enable safe navigation in unknown and dynamic environments. This problem is even more challenging for multi-UAV systems where it is more efficient to utilize information shared among the networked vehicles. Therefore, the work presented in this thesis contributes towards the state-of-the-art in UAV control for safe autonomous navigation and motion coordination of multi-UAV systems. The first part of this thesis deals with single-UAV systems. Initially, a hybrid navigation framework is developed for autonomous mobile robots using a general 2D nonholonomic unicycle model that can be applied to different types of UAVs, ground vehicles and underwater vehicles considering only lateral motion. Then, the more complex problem of three-dimensional (3D) collision-free navigation in unknown/dynamic environments is addressed. To that end, advanced 3D reactive control strategies are developed adopting the sense-and-avoid paradigm to produce quick reactions around obstacles. A special case of navigation in 3D unknown confined environments (i.e. tunnel-like) is also addressed. General 3D kinematic models are considered in the design which makes these methods applicable to different UAV types in addition to underwater vehicles. Moreover, different implementation methods for these strategies with quadrotor-type UAVs are also investigated considering UAV dynamics in the control design. Practical experiments and simulations were carried out to analyze the performance of the developed methods. The second part of this thesis addresses safe navigation for multi-UAV systems. Distributed motion coordination methods of multi-UAV systems for flocking and 3D area coverage are developed. These methods offer good computational cost for large-scale systems. Simulations were performed to verify the performance of these methods considering systems with different sizes

Search and restore: a study of cooperative multi-robot systems

Swarm intelligence is the study of natural biological systems with the ability to transform simple local interactions into complex global behaviours. Swarm robotics takes these principles and applies them to multi-robot systems with the aim of achieving the same level of complex behaviour which can result in more robust, scalable and flexible robotic solutions than singular robot systems. This research concerns how cooperative multi-robot systems can be utilised to solve real world challenges and outperform existing techniques. The majority of this research is focused around an emergency ship hull repair scenario where a ship has taken damage and sea water is flowing into the hull, decreasing the stability of the ship. A bespoke team of simulated robots using novel algorithms enable the robots to perform a coordinated ship hull inspection, allowing the robots to locate the damage faster than a similarly sized uncoordinated team of robots. Following this investigation, a method is presented by which the same team of robots can use self-assembly to form a structure, using their own bodies as material, to cover and repair the hole in the ship hull, halting the ingress of sea water. The results from a collaborative nature-inspired scenario are also presented in which a swarm of simple robots are tasked with foraging within an initially unexplored bounded arena. Many of the behaviours implemented in swarm robotics are inspired by biological swarms including their goals such as optimal distribution within environments. In this scenario, there are multiple items of varying quality which can be collected from different sources in the area to be returned to a central depot. The aim of this study is to imbue the robot swarm with a behaviour that will allow them to achieve the most optimal foraging strategy similar to those observed in more complex biological systems such as ants. The author’s main contribution to this study is the implementation of an obstacle avoidance behaviour which allows the swarm of robots to behave more similarly to systems of higher complexity