A Novel Double Layered Hybrid Multi-Robot Framework for Guidance and Navigation of Unmanned Surface Vehicles in a Practical Maritime Environment

Formation control and cooperative motion planning are two major research areas currently being used in multi robot motion planning and coordination. The current study proposes a hybrid framework for guidance and navigation of swarm of unmanned surface vehicles (USVs) by combining the key characteristics of formation control and cooperative motion planning. In this framework, two layers of offline planning and online planning are integrated and applied on a practical marine environment. In offline planning, an optimal path is generated from a constrained A* path planning approach, which is later smoothed using a spline. This optimal trajectory is fed as an input for the online planning where virtual target (VT) based multi-agent guidance framework is used to navigate the swarm of USVs. This VT approach combined with a potential theory based swarm aggregation technique provides a robust methodology of global and local collision avoidance based on known positions of the USVs. The combined approach is evaluated with the different number of USVs to understand the effectiveness of the approach from the perspective of practicality, safety and robustness.</jats:p

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

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A survey of formation control and motion planning of multiple unmanned vehicles

The increasing deployment of multiple unmanned vehicles systems has generated large research interest in recent decades. This paper therefore provides a detailed survey to review a range of techniques related to the operation of multi-vehicle systems in different environmental domains, including land based, aerospace and marine with the specific focuses placed on formation control and cooperative motion planning. Differing from other related papers, this paper pays a special attention to the collision avoidance problem and specifically discusses and reviews those methods that adopt flexible formation shape to achieve collision avoidance for multi-vehicle systems. In the conclusions, some open research areas with suggested technologies have been proposed to facilitate the future research development

Fault Recovery in Swarm Robotics Systems using Learning Algorithms

When faults occur in swarm robotic systems they can have a detrimental effect on collective behaviours, to the point that failed individuals may jeopardise the swarm's ability to complete its task. Although fault tolerance is a desirable property of swarm robotic systems, fault recovery mechanisms have not yet been thoroughly explored. Individual robots may suffer a variety of faults, which will affect collective behaviours in different ways, therefore a recovery process is required that can cope with many different failure scenarios. In this thesis, we propose a novel approach for fault recovery in robot swarms that uses Reinforcement Learning and Self-Organising Maps to select the most appropriate recovery strategy for any given scenario. The learning process is evaluated in both centralised and distributed settings. Additionally, we experimentally evaluate the performance of this approach in comparison to random selection of fault recovery strategies, using simulated collective phototaxis, aggregation and foraging tasks as case studies. Our results show that this machine learning approach outperforms random selection, and allows swarm robotic systems to recover from faults that would otherwise prevent the swarm from completing its mission. This work builds upon existing research in fault detection and diagnosis in robot swarms, with the aim of creating a fully fault-tolerant swarm capable of long-term autonomy

Robot Soccer Strategy Based on Hierarchical Finite State Machine to Centralized Architectures

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works[EN] Coordination among the robots allows a robot soccer team to perform better through coordinated behaviors. This requires that team strategy is designed in line with the conditions of the game. This paper presents the architecture for robot soccer team coordination, involving the dynamic assignment of roles among the players. This strategy is divided into tactics, which are selected by a Hierarchical State Machine. Once a tactic has been selected, it is assigned roles to players, depending on the game conditions. Each role performs defined behaviors selected by the Hierarchical State Machine. To carry out the behaviors, robots are controlled by the lowest level of the Hierarchical State Machine. The architecture proposed is designed for robot soccer teams with a central decision-making body, with global perception. 200 games were performed against a team with constant roles, winning the 92.5% of the games, scoring more goals on average that the opponent, and showing a higher percent of ball possession. Student s t-test shows better matching with measurement uncertainty of the strategy proposed. This architecture allowed an intuitive design of the robot soccer strategy, facilitating the design of the rules for role selection and behaviors performed by the players, depending on the game conditions. Collaborative behaviors and uniformity within the players behaviors during the tactics and behaviors transitions were observedJose Guillermo Guarnizo ha sido financiado por una beca del Departamento Administrativo de Ciencia, Tecnología e Innovación COLCIENCIAS, Colombia.Guarnizo, JG.; Mellado Arteche, M. (2016). Robot Soccer Strategy Based on Hierarchical Finite State Machine to Centralized Architectures. IEEE Latin America Transactions. 14(8):3586-3596. doi:10.1109/TLA.2016.7786338S3586359614

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

Cooperative Swarm Optimisation of Unmanned Surface Vehicles

Edited version embargoed 10 07.01.2020 Full version: Access restricted permanently due to 3rd party copyright restrictions. Restriction set on 11/04/2019 by AS, Doctoral CollegeWith growing advances in technology and everyday dependence on oceans for resources, the role of unmanned surface vehicles (USVs) has increased many fold. Extensive operations of USVs having naval, civil and scientific applications are currently being undertaken in various complex marine environments and demands are being placed on them to increase their autonomy and adaptability. A key requirement for the autonomous operation of USVs is to possess a multi-vehicle framework where they can operate as a fleet of vehicles in a practical marine environment with multiple advantages such as surveying of wider areas in less time. From the literature, it is evident that a huge number of studies has been conducted in the area of single USV path planning, guidance and control whilst very few studies have been conducted to understand the implications of the multi vehicle approaches to USVs. This present PhD thesis integrates the modules of efficient optimal path planning, robust path following guidance and cooperative swarm aggregation approach towards development of a new hybrid framework for cooperative navigation of swarm of USVs to enable optimal and autonomous operation in a maritime environment. Initially, an effective and novel optimal path planning approach based on the A* algorithm has been designed taking into account the constraint of a safety distance from the obstacles to avoid the collisions in scenarios of moving obstacles and sea surface currents. This approach is then integrated with a novel virtual target path following guidance module developed for USVs where the reference trajectory from the path planner is fed into the guidance system. The novelty of the current work relies on combining the above mentioned integrated path following guidance system with decentralised swarm aggregation behaviour by means of simple potential based attraction and repulsion functions to maintain the centroid of the swarm of USVs and thereby guiding the swarm of USVs onto a reference path. Finally, an optimal and hybrid framework for cooperative navigation and guidance of fleet of USVs, implementable in practical maritime environments and effective for practical applications at sea is presented.Commonwealth Scholarship Commissio

Market_based Framework for Mobile Surveillance Systems

The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects and events in a given Area Of Interest (AOI). The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, cooperative object detection and tracking, decentralized data fusion, and interoperability and accessibility of system nodes. This thesis proposes a market-based framework that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target-tracking are studied using the proposed framework as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively

Task Allocation among Connected Devices: Requirements, Approaches and Challenges

Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-of-the-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities’ provisioning

FPGA-Based Acceleration of the Self-Organizing Map (SOM) Algorithm using High-Level Synthesis

One of the fastest growing and the most demanding areas of computer science is Machine Learning (ML). Self-Organizing Map (SOM), categorized as unsupervised ML, is a popular data-mining algorithm widely used in Artificial Neural Network (ANN) for mapping high dimensional data into low dimensional feature maps. SOM, being computationally intensive, requires high computational time and power when dealing with large datasets. Acceleration of many computationally intensive algorithms can be achieved using Field-Programmable Gate Arrays (FPGAs) but it requires extensive hardware knowledge and longer development time when employing traditional Hardware Description Language (HDL) based design methodology. Open Computing Language (OpenCL) is a standard framework for writing parallel computing programs that execute on heterogeneous computing systems. Intel FPGA Software Development Kit for OpenCL (IFSO) is a High-Level Synthesis (HLS) tool that provides a more efficient alternative to HDL-based design. This research presents an optimized OpenCL implementation of SOM algorithm on Stratix V and Arria 10 FPGAs using IFSO. Compared to recent SOM implementations on Central Processing Unit (CPU) and Graphics Processing Unit (GPU), our OpenCL implementation on FPGAs provides superior speed performance and power consumption results. Stratix V achieves speedup of 1.41x - 16.55x compared to AMD and Intel CPU and 2.18x compared to Nvidia GPU whereas Arria 10 achieves speedup of 1.63x - 19.15x compared to AMD and Intel CPU and 2.52x compared to Nvidia GPU. In terms of power consumption, Stratix V is 35.53x and 42.53x whereas Arria 10 is 15.82x and 15.93x more power efficient compared to CPU and GPU respectively