Self-sufficiency of an autonomous self-reconfigurable modular robotic organism

In recent years, getting inspiration from simple but complex biological organisms, several advances have been seen in autonomous systems to mimic different behaviors that emerge from the interactions of a large group of simple individuals with each other and with the environment. Among several open issues a significantly important issue, not addressed so far, is the self-sufficiency, or in other words, the energetic autonomy of a modular robotic organism. This feature plays a pivotal role in maintaining a robotic organism\u27s autonomy for a longer period of time. To address the challenges of self-sufficiency, a novel dynamic power management system (PMS) with fault tolerant energy sharing is proposed, realized in the form of hardware and software, and tested. The innate fault tolerant feature of the proposed PMS ensures power sharing in an organism despite docked faulty robotic modules. Due to the unavailability of sufficient number of real robotic modules a simulation framework called Replicator Power Flow Simulator is devised for the implementation of application software layer power management components. The simulation framework was especially devised because at the time of writing this work no simulation tool was available that could be used to perform power sharing and fault tolerance experiments at an organism level. The simulation experiments showed that the proposed application software layer dynamic power sharing policies in combination with the distributed fault tolerance feature in addition to self-sufficiency are expected to enhance the robustness and stability of a real modular robotic organism under varying conditions.Inspiriert von einfachen aber komplexen biologischen Organismen wurden in den letzten Jahren verschiedenste autonome Systeme entwickelt, welche die Verhaltensweisen einer großen Gruppe einfacher Individuen nachahmen. Das zentrale und bis heute ungelöste Problem dieser Organismen ist deren autonome Energieversorgung. Zur Sicherstellung der Energieversorgung eines aus mehreren Robotern zusammengesetzten Organismus wurde in dieser Arbeit ein neuartiges Power-Management-System (PMS) konzipiert, aufgebaut und an einzelnen Robotermodulen und einem Roboterorganismus getestet. Die Hardware eines bestehenden Roboters wurde um ein neues Konzept erweitert, das auch bei fehlerhaften Robotermodulen einen Energieaustausch sicherstellt und so zu einer erhöhten Robustheit des PMS führen soll. Das entwickelte PMS wurde in modulare Roboter integriert und beispielhaft anhand eines Roboterorganismus getestet. In Ermangelung einer ausreichenden Anzahl von Robotermodulen wurde eine Simulationsumgebung entwickelt und die Software des PMS im Simulationsprogramm, anstatt im Roboter, implementiert. Dieses Simulationswerkzeug ist momentan das Einzige, das unter Berücksichtigung des Bewegungsmodells des Organismus den Energietransport im Roboterorganismus visuell darstellt und das Verhalten in verschiedenen Fehlerfällen simulieren kann. Die Simulationen und Messungen zeigen, dass das entwickelte PMS geeignet ist, die Energieversorgung von Roboterorganismen auch in Fehlerfällen sicherzustellen und so die Stabilität und Robustheit zu erhöhen

Development and evaluation of vision processing algorithms in multi-robotic systems.

The trend in swarm robotics research is shifting to the design of more complicated systems in which the robots have abilities to form a robotic organism. In such systems, a single robot has very limited memory and processing resources, but the complete system is rich in these resources. As vision sensors provide rich surrounding awareness and vision algorithms also requires intensive processing. Therefore, vision processing tasks are the best candidate for distributed processing in such systems. To perform distributed vision processing, a number of scenarios are considered in swarm and the robotic organism form. In the swarm form, as the robots use low bandwidth wireless communication medium, so the exchange of simple visual features should be made between robots. This is addressed in a swarm mode scenario, where novel distance vector features are exchanged within a swarm of robots to generate a precise environmental map. The generated map facilitates the robot navigation in the environment. If features require encoding with high density information, then sharing of such features is not possible using the wireless channel with limited bandwidth. So methods were devised which process such features onboard and then share the process outcome to perform vision processing in a distributed fashion. This is shown in another swarm mode scenario in which a number of optimisation stages are followed and novel image pre-processing techniques are developed which enable the robots to perform onboard object recognition, and then share the process outcome in terms of object identity and its distance from the robot, to localise the objects. In the robotic organism, the use of reliable communication medium facilitates vision processing in distributed fashion, and this is presented in two scenarios. In the first scenario, the robotic organism detect objects in the environment in distributed fashion, but to get detailed surrounding awareness, the organism needs to learn these objects. This leads to a second scenario, which presents a modular approach to object classification and recognition. This approach provides a mechanism to learn newly detected objects and also ensure faster response to object recognition. Using the modular approach, it is also demonstrated that the collective use of 4 distributed processing resources in a robotic organism can provide 5 times the performance of an individual robot module. The overall performance was comparable to an individual less flexible robot (e.g., Pioneer-3AT) with significant higher processing capability

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

An investigation of service degradation in long-term human-robot interaction with a particular reference to recharge behaviour

Autonomous long-term operation of social robots has always been a challenge in Human robot-interaction. Social mobile robots acting as companions or assistants will need to operate over a long-term period of time (days, weeks or even months) to perform daily tasks and interact with users. Therefore they should be capable of operating with a great degree of autonomy and will require sustainable social intelligence. Social robots are fallible and have their own limitations with the service they provide. One of the most important limitations of mobile robots is power constraints and the need for frequent recharging. Social mobile robots generally draw power from batteries carried on the robot in order to operate various sensors, actuators and perform tasks. However, batteries have a limited power life and take a long time to recharge via a power source. While the recharge behaviour is active, which may impede human-robot interaction and lead to service degradation. This thesis raises some important issues related to recharge behaviour of social mobile robots which appear to have been overlooked in social robotics research. This work investigated service degradation in long-term interaction due to recharge behaviour of autonomous social mobile robots and proposes an approach to manage service degradation due to recharge. First we performed a long-term study to investigate the service degradation caused by the recharging behaviour of a social robot. Second we conducted a more focused social study which helped to understand user’s attitudes towards a mobile robot with respect to recharge activity. We explored a social strategy by modifying the robot’s verbal behaviour to manage service degradation during recharge. The results obtained from our social study indicates the use of verbal strategies (transparency, apology, politeness) made the robot more acceptable to the users during recharge. We believe that social mobile robots should behave in a socially intelligent manner while managing service degradation. We also provide some recommendations for social mobile robots to manage their recharge behaviour in this thesis

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

A general architecture for robotic swarms

Swarms are large groups of simplistic individuals that collectively solve disproportionately complex tasks. Individual swarm agents are limited in perception, mechanically simple, have no global knowledge and are cheap, disposable and fallible. They rely exclusively on local observations and local communications. A swarm has no centralised control. These features are typifed by eusocial insects such as ants and termites, who construct nests, forage and build complex societies comprised of primitive agents. This project created the basis of a general swarm architecture for the control of insect-like robots. The Swarm Architecture is inspired by threshold models of insect behaviour and attempts to capture the salient features of the hive in a closely defined computer program that is hardware agnostic, swarm size indifferent and intended to be applicable to a wide range of swarm tasks. This was achieved by exploiting the inherent limitations of swarm agents. Individual insects were modelled as a machine capable only of perception, locomotion and manipulation. This approximation reduced behaviour primitives to a fixed tractable number and abstracted sensor interpretation. Cooperation was achieved through stigmergy and decisions made via a behaviour threshold model. The Architecture represents an advance on previous robotic swarms in its generality - swarm control software has often been tied to one task and robot configuration. The Architecture's exclusive focus on swarms, sets it apart from existing general cooperative systems, which are not usually explicitly swarm orientated. The Architecture was implemented successfully on both simulated and real-world swarms

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

Interaction and Intelligent Behavior

We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems