Formation Navigation and Relative Localisation of Multi-Robot Systems

When proceeding from single to multiple robots, cooperative action is one of the most relevant topics. The domain of robotic security systems contains typical applications for a multi-robot system (MRS). Possible scenarios are safety and security issues on airports, harbours, large industry plants or museums. Additionally, the field of environmental supervision is an up-coming issue. Inherent to these applications is the need for an organised and coordinated navigation of the robots, and a vital prerequisite for any coordinated movements is a good localisation. This dissertation will present novel approaches to the problems of formation navigation and relative localisation with multiple ground-based mobile robots. It also looks into the question what kind of metric is applicable for multi-robot navigation problems. Thereby, the focus of this work will be on aspects of 1. coordinated navigation and movement A new potential-field-based approach to formation navigation is presented. In contradiction to classical potential-field-based formation approaches, the proposed method also uses the orientation between neighbours in the formation. Consequently, each robot has a designated position within the formation. Therefore, the new method is called directed potential field approach. Extensive experiments prove that the method is capable of generating all kinds of formation shapes, even in the presence of dense obstacles. All tests have been conducted with simulated and real robots and successfully guided the robot formation through environments with varying obstacle configurations. In comparison, the nondirected potential field approach turns out to be unstable regarding the positions of the robots within formations. The robots strive to switch their positions, e.g. when passing through narrow passages. Under such conditions the directed approach shows a preferable behaviour, called “breathing”. The formation shrinks or inflates depending on the obstacle situation while trying to maintain its shape and keep the robots at their desired positions inside the formation. For a more particular comparison of formation algorithms it is important to have measures that allow a meaningful evaluation of the experimental data. For this purpose a new formation metric is developed. If there are many obstacles, the formation error must be scaled down to be comparable to an empty environment where the error would be small. Assuming that the environment is unknown and possibly non-static, only actual sensor information can be used for these calculations. We developed a special weighting factor, which is inverse proportional to the “density” of obstacles and which turns out to model the influence of the environment adequately. 2. relative localisation A new method for relative localisation between the members of a robot group is introduced. This relative localisation approach uses mutual sensor observations to localise the robots with respect to other objects – without having an environment model. Techniques like the Extended Kalman Filter (EKF) have proven to be powerful tools in the field of single robot applications. This work presents extensions to these algorithms with respect to the use in MRS. These aspects are investigated and combined under the topic of improving and stabilising the performance of the localisation and navigation process. Most of the common localisation approaches use maps and/or landmarks with the intention of generating a globally consistent world-coordinate system for the robot group. The aim of the here presented relative localisation approach, on the other hand, is to maintain only relative positioning between the robots. The presented method enables a group of mobile robots to start at an unknown location in an unknown environment and then to incrementally estimate their own positions and the relative locations of the other robots using only sensor information. The result is a robust, fast and precise approach, which does not need any preconditions or special assumptions about the environment. To validate the approach extensive tests with both, real and simulated, robots have been conducted. For a more specific evaluation, the Mean Localisation Error (MLE) is introduced. The conducted experiments include a comparison between the proposed Extended Kalman Filter and a standard SLAM-based approach. The developed method robustly delivered an accuracy better than 2 cm and performed at least as well as the SLAM approach. The algorithm coped with scattered groups of robots while moving on arbitrarily shaped paths. In summary, this thesis presents novel approaches to the field of coordinated navigation in multi-robot systems. The results facilitate cooperative movements of robot groups as well as relative localisation among the group members. In addition, a solid foundation for a non-environment related metric for formation navigation is introduced

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

Adaptive self-management of teams of autonomous vehicles

Unmanned Autonomous Vehicles (UAVs) are increasingly deployed for missions that are deemed dangerous or impractical to perform by humans in many military and disaster scenarios. Collaborating UAVs in a team form a Self- Managed Cell (SMC) with at least one commander. UAVs in an SMC may need to operate independently or in sub- groups, out of contact with the commander and the rest of the team in order to perform specific tasks, but must still be able to eventually synchronise state information. The SMC must also cope with intermittent and permanent communication failures as well permanent UAV failures. This paper describes a failure management scheme that copes with both communication link and UAV failures, which may result in temporary disjoint sub-networks within the SMC. A communication management protocol is proposed to control UAVs performing disconnected individual operations, while maintaining the SMCs structure by trying to ensure that all members of the mission regardless of destination or task, can communicate by moving UAVs to act as relays or by allowing the UAVs to rendezvous at intermittent intervals. Copyright 2008 ACM.Accepted versio

Emergent velocity agreement in robot networks

In this paper we propose and prove correct a new self-stabilizing velocity agreement (flocking) algorithm for oblivious and asynchronous robot networks. Our algorithm allows a flock of uniform robots to follow a flock head emergent during the computation whatever its direction in plane. Robots are asynchronous, oblivious and do not share a common coordinate system. Our solution includes three modules architectured as follows: creation of a common coordinate system that also allows the emergence of a flock-head, setting up the flock pattern and moving the flock. The novelty of our approach steams in identifying the necessary conditions on the flock pattern placement and the velocity of the flock-head (rotation, translation or speed) that allow the flock to both follow the exact same head and to preserve the flock pattern. Additionally, our system is self-healing and self-stabilizing. In the event of the head leave (the leading robot disappears or is damaged and cannot be recognized by the other robots) the flock agrees on another head and follows the trajectory of the new head. Also, robots are oblivious (they do not recall the result of their previous computations) and we make no assumption on their initial position. The step complexity of our solution is O(n)

Multi-agent Communication Protocols with Emergent Behaviour

The emergent behaviour of a multiagent system depends on the component agents and how they interact. A critical part of interaction between agents is communication. This thesis presents a multi-agent system communication model for physical moving agents. The work presented in this thesis provides all the tools to create a physical multi-agent communication system. The model integrates different agent technologies at both the micro and macro level. The micro structure involves the architecture of the individual components in the system whilst the macro structure involves the interaction relationships between these individual components in the system. Regarding the micro structure of the system, the model provides the description of a novel hybrid BDI-Blackboard architectured agent that builds-in a hybrid of reactive and deliberative agent. The macro structure of the system, provided by this model, provides the operational specifications of the communication protocols. The thesis presents a theory of communication that integrates an animal intelligence technique together with a cognitive intelligence one. This results in a local co-ordination of movements, and global task coordination. Accordingly, agents are designed to communicate with other agents in order to coordinate their movements via a set of behavioural rules. These behavioural rules allow a simple directed flocking behaviour to emerge. A flocking algorithm is used because it satisfies a major objective, i.e. it has a real time response to local environmental changes and minimises the cost of path planning. A higher level communication mechanism is implemented for task distribution that is carried out via a blackboard conversation and ii negotiation process with a ground based controller. All the tasks are distributed as team tasks. A novel utilization of speech acts as communication utterances through a blackboard negotiation process is proposed. In order to implement the proposed communication model, a virtual environment is built that satisfies the realism of representing the agents, environment, and the sensors as well as representing the actions. The virtual environment used in the work is built as a semi-immersive full-scale environment and provides the visualisation tools required to test, modify, compare and evaluate different behaviours under different conditions. The visualization tools allow the user to visualize agents negotiations and interacting with them. The 3D visualisation and simulation tools allow the communication protocol to be tested and the emergent behaviour to be seen in an easy and understandable manner. The developed virtual environment can be used as a toolkit to test different communication protocols and different agent’s architecture in real time

A STUDY OF MACHINE VISION IN THE AUTOMOTIVE INDUSTRY

With the growth of industrial automation, it has become increasingly important to validate the quality of every manufactured part during production. Until now, human visual inspection aided with hard tooling or machines have been the primary means to this end, but the speed of today's production lines, the complexity of production equipment and the highest standards of quality to which parts must adhere frequently, make the traditional methods of industrial inspection and control impractical, if not impossible. Subsequently, new solutions have been developed for the monitoring and control of industrial processes, in real­time. One such technology is the area of machine vision. After many years of research and development, computerised vision systems are now leaving the laboratory and are being used successfully in the factory environment. They are both robust and competitively priced as a sensing technique which has now opened up a whole new sector for automation. Machine vision systems are becoming an important integral part of the automotive manufacturing process, with applications ranging from inspection, classification, robot guidance, assembly verification through to process monitoring and control. Although the number of systems in current use is still relatively small, there can be no doubt, given the issues at stake, that the automotive industry will once again lead the way with the implementation of machine vision just as it has done robotic technology. The thesis considered the issue of machine vision and in particular, its deployment within the automotive industry. The thesis has presented work on machine vision for the prospective end-user and not the designer of such systems. It will provide sufficient background about the subject, to separate machine vision promises from reality and permit intelligent decisions regarding machine vision applications to be made. The initial part of the dissertation focussed on the strategic issues affecting the selection of machine vision at the planning stage, such as a listing of the factors to justify investment, the capability of the technology and type of problems that are associated with this relatively new but complex science. Though it is widely accepted that no two industrial machine vision systems are identical, knowledge of the basic fundamentals which underpin the structure of the technology in its application is presented. This work covered a structured description detailing typical hardware components such as camera technology, lighting systems, etc... which form an integral part of an industrial system and discussions regarding the criteria for selection are presented. To complement this work, a further section is specifically devoted to the bewildering array of vision software analysis techniques which are currently available today. A detailed description of the various techniques that are applied to images in order to make use of and understand the data contained within them are discussed and explored. Applications for machine vision fall into two main categories namely robotic guidance and inspection. Obviously within each category there are many further sub­groups. Within this context the latter part of the thesis reviews with a well structured description of several industrial case studies derived from the automotive industry, which illustrate that machine vision is capable of providing real time solutions to manufacturing based problems. In conclusion, despite the limited availability of industrially based machine vision systems, the success of implementation is not always guaranteed, as the technology imposes both technical limitations and introduce new human engineering considerations. By understanding the application and the implications of the technical requirements on both the "staging" and the "image-processing" power required of the machine vision system. The thesis has shown that the most significant elements of a successful application are indeed the lighting, optics, component design, etc... - the "Staging". From the case studies investigated, optimised "staging" has resulted in the need for less computing power in the machine vision system. Inevitably, greater computing power not only requires more time but is generally more expensive. The experience gained from the this project, has demonstrated that machine vision technology is a realistic alternative means of capturing data in real-time. Since the current limitations of the technology are well suited to the delivery process of the quality function within the manufacturing process

Simultaneous localization and map-building using active vision

An active approach to sensing can provide the focused measurement capability over a wide field of view which allows correctly formulated Simultaneous Localization and Map-Building (SLAM) to be implemented with vision, permitting repeatable long-term localization using only naturally occurring, automatically-detected features. In this paper, we present the first example of a general system for autonomous localization using active vision, enabled here by a high-performance stereo head, addressing such issues as uncertainty-based measurement selection, automatic map-maintenance, and goal-directed steering. We present varied real-time experiments in a complex environment.Published versio

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Real-time control of industrial robots in multiple microcomputers

Imperial Users onl

Symbiotic interaction between humans and robot swarms

Comprising of a potentially large team of autonomous cooperative robots locally interacting and communicating with each other, robot swarms provide a natural diversity of parallel and distributed functionalities, high flexibility, potential for redundancy, and fault-tolerance. The use of autonomous mobile robots is expected to increase in the future and swarm robotic systems are envisioned to play important roles in tasks such as: search and rescue (SAR) missions, transportation of objects, surveillance, and reconnaissance operations. To robustly deploy robot swarms on the field with humans, this research addresses the fundamental problems in the relatively new field of human-swarm interaction (HSI). Four groups of core classes of problems have been addressed for proximal interaction between humans and robot swarms: interaction and communication; swarm-level sensing and classification; swarm coordination; swarm-level learning. The primary contribution of this research aims to develop a bidirectional human-swarm communication system for non-verbal interaction between humans and heterogeneous robot swarms. The guiding field of application are SAR missions. The core challenges and issues in HSI include: How can human operators interact and communicate with robot swarms? Which interaction modalities can be used by humans? How can human operators instruct and command robots from a swarm? Which mechanisms can be used by robot swarms to convey feedback to human operators? Which type of feedback can swarms convey to humans? In this research, to start answering these questions, hand gestures have been chosen as the interaction modality for humans, since gestures are simple to use, easily recognized, and possess spatial-addressing properties. To facilitate bidirectional interaction and communication, a dialogue-based interaction system is introduced which consists of: (i) a grammar-based gesture language with a vocabulary of non-verbal commands that allows humans to efficiently provide mission instructions to swarms, and (ii) a swarm coordinated multi-modal feedback language that enables robot swarms to robustly convey swarm-level decisions, status, and intentions to humans using multiple individual and group modalities. The gesture language allows humans to: select and address single and multiple robots from a swarm, provide commands to perform tasks, specify spatial directions and application-specific parameters, and build iconic grammar-based sentences by combining individual gesture commands. Swarms convey different types of multi-modal feedback to humans using on-board lights, sounds, and locally coordinated robot movements. The swarm-to-human feedback: conveys to humans the swarm's understanding of the recognized commands, allows swarms to assess their decisions (i.e., to correct mistakes: made by humans in providing instructions, and errors made by swarms in recognizing commands), and guides humans through the interaction process. The second contribution of this research addresses swarm-level sensing and classification: How can robot swarms collectively sense and recognize hand gestures given as visual signals by humans? Distributed sensing, cooperative recognition, and decision-making mechanisms have been developed to allow robot swarms to collectively recognize visual instructions and commands given by humans in the form of gestures. These mechanisms rely on decentralized data fusion strategies and multi-hop messaging passing algorithms to robustly build swarm-level consensus decisions. Measures have been introduced in the cooperative recognition protocol which provide a trade-off between the accuracy of swarm-level consensus decisions and the time taken to build swarm decisions. The third contribution of this research addresses swarm-level cooperation: How can humans select spatially distributed robots from a swarm and the robots understand that they have been selected? How can robot swarms be spatially deployed for proximal interaction with humans? With the introduction of spatially-addressed instructions (pointing gestures) humans can robustly address and select spatially- situated individuals and groups of robots from a swarm. A cascaded classification scheme is adopted in which, first the robot swarm identifies the selection command (e.g., individual or group selection), and then the robots coordinate with each other to identify if they have been selected. To obtain better views of gestures issued by humans, distributed mobility strategies have been introduced for the coordinated deployment of heterogeneous robot swarms (i.e., ground and flying robots) and to reshape the spatial distribution of swarms. The fourth contribution of this research addresses the notion of collective learning in robot swarms. The questions that are answered include: How can robot swarms learn about the hand gestures given by human operators? How can humans be included in the loop of swarm learning? How can robot swarms cooperatively learn as a team? Online incremental learning algorithms have been developed which allow robot swarms to learn individual gestures and grammar-based gesture sentences supervised by human instructors in real-time. Humans provide different types of feedback (i.e., full or partial feedback) to swarms for improving swarm-level learning. To speed up the learning rate of robot swarms, cooperative learning strategies have been introduced which enable individual robots in a swarm to intelligently select locally sensed information and share (exchange) selected information with other robots in the swarm. The final contribution is a systemic one, it aims on building a complete HSI system towards potential use in real-world applications, by integrating the algorithms, techniques, mechanisms, and strategies discussed in the contributions above. The effectiveness of the global HSI system is demonstrated in the context of a number of interactive scenarios using emulation tests (i.e., performing simulations using gesture images acquired by a heterogeneous robotic swarm) and by performing experiments with real robots using both ground and flying robots