Recent years have witnessed the prosperity of robots and in order to support consensus and cooperation for multi- robot system, wireless communications and networking among robots and the infrastructure have become indispensable. In this technical note, we first provide an overview of the research contributions on communication-aware motion planning (CAMP) in designing wireless-connected robotic networks (WCRNs), where the degree-of-freedom (DoF) provided by motion and communication capabilities embraced by the robots have not been fully exploited. Therefore, we propose the framework of joint communication-motion planning (JCMP) as well as the architecture for incorporating JCMP in WCRNs. The proposed architecture is motivated by the observe-orient-decision-action (OODA) model commonly adopted in robotic motion control and cognitive radio. Then, we provide an overview of the orient module that quantify the connectivity assessment. Afterwards, we highlight the JCMP module and compare it with the conventional communication-planning, where the necessity of the JCMP is validated via both theoretical analysis and simulation results of an illustrative example. Finally, a series of open problems are discussed, which picture the gap between the state-of-the-art and a practical WCRN.
Recent years have witnessed the evolution of robotics and great industrial academic efforts. As the robots interact with the physical and social environments, considerable research contributions have been devoted to robotic sensing, cognition, motion path planning and control @cite_37 . A multi-robot system aims at achieving challenging tasks or significantly improving mission performance compared with a single robot, which demands consensus and cooperation among robots @cite_11 . Therefore, maintaining the connectivity quality for information exchange among robots becomes vital. As mobile robots are less likely to be connected via wires, the wireless communications and networking among robots and the infrastructure would play an crucial role and the wireless-connected robotic networks (WCRNs) are very likely to be incorporated into the next-generation communication networks.
@cite_37 @cite_11
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Future life pictures humans having intelligent humanoid robotic systems taking part in their everyday life. Thus researchers strive to supply robots with an adequate artificial intelligence in order to achieve a natural and intuitive interaction between human being and robotic system. Within the German Humanoid Project we focus on learning and cooperating multimodal robotic systems. In this paper we present a first cognitive architecture for our humanoid robot: The architecture is a mixture of a hierarchical three-layered form on the one hand and a composition of behaviour-specific modules on the other hand. Perception, learning, planning of actions, motor control, and human-like communication play an important role in the robotic system and are embedded step by step in our architecture This paper presents HiDDeN, a high-level distributed architecture for multi-robot cooperation. HiDDeN aims at controlling a team of heterogeneous robots in environments with uncertain communications. It relies on a mission plan defined as an instantiated HTN, i.e. a hierarchical decomposition of robots' tasks. This hierarchical structure also benefits to plan repair operations in case of failure detections. This repair is made as local as possible, in order to avoid unnecessary communications between robots. Traditionally, motion planning involved navigating one robot from source to goal for accomplishing a task. Now, tasks mostly require movement of a team of robots to the goal site, requiring a chain of robots to reach the desired goal. While numerous efforts are made in the literature for solving the problems of motion planning of a single robot and collective robot navigation in isolation, this paper fuses the two paradigms to let a chain of robot navigate. Further, this paper uses SLAM to first make a static map using a high-end robot, over which the physical low-sensing robots run. Deliberative Planning uses A* algorithm to plan the path. Reactive planning uses the Potential Field Approach to avoid obstacles and stay as close to the initial path planned as possible. These two algorithms are then merged to provide an algorithm that allows the robot to reach its goal via the shortest path possible while avoiding obstacles. The algorithm is further extended to multiple robots so that one robot is followed by the next robot and so on, thus forming a chain. In order to maintain the robots in a chain form, the Elastic Strip model is used. The algorithm proposed successfully executes the above stated when tested on Amigobot robots in an office environment using a map made by the Pioneer LX robot. The proposed algorithm works well for moving a group of robots in a chain in a mapped environment. Nowadays, we can no longer imagine that Internet can be considered just a network of computers. We also have to state that it's becoming even more a network of things. With this in mind, this paper introduces a long range Internet connected robot teleoperation system based on Internet of Things (IoT). The aim is to support operators during remote teleoperation of robotic systems in situations in which the remote control devices loose the connection with the on-board receivers. This situation can occur either due to the lost of the line-of-sight or due to possible damages of the devices. IoT allows to connect remote and mobile things or machines. It also assets through the use of wireless communications and low-cost sensors, computing and storage devices. Approaches based on IoT have been already applied in traffic monitoring, smart homes, smart parking management, vehicle tracking system and other industrial applications. We used the system to control a mobile robot without range barriers provided that the robot and the remote control device must be connected to the Internet. In social insects, both self-organisation and communication play a crucial role for the accomplishment of many tasks at a collective level. Communication is performed with different modalities, which can be roughly classified into three classes: indirect (stigmergic) communication, direct interactions and direct communication. The use of stigmergic communication is predominant in social insects (e.g. the pheromone trails in ants), where, however, direct interactions (e.g. antennation in ants) and direct communication (e.g. the waggle dance in honey bees) can also be observed. Taking inspiration from insect societies, we present an experimental study of self-organising behaviours for a group of robots, which exploit communication to coordinate their activities. In particular, the robots are placed in an arena presenting holes and open borders, which they should avoid while moving coordinately. Artificial evolution is responsible for the synthesis in a simulated environment of the robot’s neural controllers, which are subsequently tested on physical robots. We study different communication strategies among the robots: no direct communication, handcrafted signalling and a completely evolved approach. We show that the latter is the most efficient, suggesting that artificial evolution can produce behaviours that are more adaptive than those obtained with conventional design methodologies. Moreover, we show that the evolved controllers produce a self-organising system that is robust enough to be tested on physical robots, notwithstanding the huge gap between simulation and reality. This self-contained introduction to the distributed control of robotic networks offers a distinctive blend of computer science and control theory. The book presents a broad set of tools for understanding coordination algorithms, determining their correctness, and assessing their complexity; and it analyzes various cooperative strategies for tasks such as consensus, rendezvous, connectivity maintenance, deployment, and boundary estimation. The unifying theme is a formal model for robotic networks that explicitly incorporates their communication, sensing, control, and processing capabilities--a model that in turn leads to a common formal language to describe and analyze coordination algorithms.Written for first- and second-year graduate students in control and robotics, the book will also be useful to researchers in control theory, robotics, distributed algorithms, and automata theory. The book provides explanations of the basic concepts and main results, as well as numerous examples and exercises.Self-contained exposition of graph-theoretic concepts, distributed algorithms, and complexity measures for processor networks with fixed interconnection topology and for robotic networks with position-dependent interconnection topology Detailed treatment of averaging and consensus algorithms interpreted as linear iterations on synchronous networks Introduction of geometric notions such as partitions, proximity graphs, and multicenter functions Detailed treatment of motion coordination algorithms for deployment, rendezvous, connectivity maintenance, and boundary estimation Planetary exploration scenarios illustrate the need for autonomous robots that are capable to operate in unknown environments without direct human interaction. At the DARPA Robotics Challenge, we demonstrated that our Centaur-like mobile manipulation robot Momaro can solve complex tasks when teleoperated. Motivated by the DLR SpaceBot Cup 2015, where robots should explore a Mars-like environment, find and transport objects, take a soil sample, and perform assembly tasks, we developed autonomous capabilities for Momaro. Our robot perceives and maps previously unknown, uneven terrain using a 3D laser scanner. Based on the generated height map, we assess drivability, plan navigation paths, and execute them using the omnidirectional drive. Using its four legs, the robot adapts to the slope of the terrain. Momaro perceives objects with cameras, estimates their pose, and manipulates them with its two arms autonomously. For specifying missions, monitoring mission progress, on-the-fly reconfiguration, and teleoperation, we developed a ground station with suitable operator interfaces. To handle network communication interruptions and latencies between robot and ground station, we implemented a robust network layer for the ROS middleware. With the developed system, our team NimbRo Explorer solved all tasks of the DLR SpaceBot Camp 2015. We also discuss the lessons learned from this demonstration. We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors. Although human-multi-robot systems have received increased attention in recent years, current implementations rely on structured environments and utilize specialized, research-grade hardware to operate. This letter presents approaches that leverage the visual and inertial sensing of mobile devices to address the estimation and control challenges of multi-robot systems that function in shared spaces with human operators such that both the mobile device camera and robots can move freely in the environment. It is shown that a subset of robots in the system can be used to maintain a reference frame that facilitates tracking and control of the remaining robots to perform tasks, such as object retrieval, using an operator's mobile device as the only sensing and computational platform in the system. To evaluate the performance of the proposed approaches, experiments are conducted in which a system of mobile robots is commanded to retrieve objects in an environment. Results show that, compared to using the visual data alone, integrating both the visual and inertial data from mobile devices yields improvements in performance, flexibility, and computational efficiency in implementing human-multi-robot systems. To accomplish cooperative tasks, robotic systems are often required to communicate with each other. Thus, maintaining connectivity of the communication graph is a fundamental issue in the field of multi-robot systems. In this paper we present a completely decentralized control strategy for global connectivity maintenance of the communication graph. Considering the disk communication model, we describe a gradient-based control strategy that exploits decentralized estimation of the algebraic connectivity. Unlike previous approaches available in the literature, the proposed control algorithm solves the global connectivity problem in a decentralized manner providing theoretical guarantees, without requiring maintenance of the local connectivity between robotic systems. Moreover, results obtained with simulations and experiments on real robots are described for demonstrating the efficacy of the proposed algorithm. The upcoming generation of humanoid robots will have to be equipped with state-of-the-art technical features along with high industrial quality, but they should also offer the prospect of effective physical human interaction. In this paper we introduce a new humanoid robot capable of interacting with a human environment and targeting industrial applications. Limitations are outlined and used together with the feedback from the DARPA Robotics Challenge, and other teams leading the field in creating new humanoid robots. The resulting robot is able to handle weights of 6 kg with an out-stretched arm, and has powerful motors to carry out fast movements. Its kinematics have been specially designed for screwing and drilling motions. In order to make interaction with human operators possible, this robot is equipped with torque sensors to measure joint effort and high resolution encoders to measure both motor and joint positions. The humanoid robotics field has reached a stage where robustness and repeatability is the next watershed. We believe that this robot has the potential to become a powerful tool for the research community to successfully navigate this turning point, as the humanoid robot HRP-2 was in its own time. To accomplish cooperative tasks, robotic systems are often required to communicate with each other. Thus, maintaining connectivity of the interagent communication graph is a fundamental issue in the field of multi-robot systems. In this paper we present a completely decentralized control strategy for global connectivity maintenance of the interagent communication graph. We describe a gradient-based control strategy that exploits decentralized estimation of the algebraic connectivity. The proposed control algorithm guarantees the global connectivity of the communication graph without requiring maintenance of the local connectivity between the robotic systems. The control strategy is validated by means of an analytical proof and simulative results. The theoretical ability of modular robots to reconfigure in response to complex tasks in a priori unknown environments has frequently been cited as an advantage, but has never been experimentally demonstrated. For the first time, we present a system that integrates perception, high-level mission planning, and modular robot hardware, allowing a modular robot to autonomously reconfigure in response to an a priori unknown environment in order to complete high-level tasks. Three hardware experiments validate the system, and demonstrate a modular robot autonomously exploring, reconfiguring, and manipulating objects to complete high-level tasks in unknown environments. We present system architecture, software and hardware in a general framework that enables modular robots to solve tasks in unknown environments using autonomous, reactive reconfiguration. The physical robot is composed of modules that support multiple robot configurations. An onboard 3D sensor provides information about the environment and informs exploration, reconfiguration decision making and feedback control. A centralized high-level mission planner uses information from the environment and the user-specified task description to autonomously compose low-level controllers to perform locomotion, reconfiguration, and other behaviors. A novel, centralized self-reconfiguration method is used to change robot configurations as needed. In this work we address the Multi-Robot Task Allocation Problem (MRTA). We assume that the decision making environment is decentralized with as many decision makers (agents) as the robots in the system. To solve this problem, we developed a distributed version of the Hungarian Method for the assignment problem. The robots autonomously perform different substeps of the Hungarian algorithm on the base of the individual and the information received through the messages from the other robots in the system. It is assumed that each robot agent has an information regarding its distance from the targets in the environment. The inter-robot communication is performed over a connected dynamic communication network and the solution to the assignment problem is reached without any common coordinator or a shared memory of the system. The algorithm comes up with a global optimum solution in O(n3) cumulative time (O(n2) for each robot), with O(n3) number of messages exchanged among the n robots. Thanks to the efforts of the robotics and autonomous systems community, the myriad applications and capacities of robots are ever increasing. There is increasing demand from end users for autonomous service robots that can operate in real environments for extended periods. In the Spatiotemporal Representations and Activities for Cognitive Control in Long-Term Scenarios (STRANDS) project (http: strandsproject.eu), we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots and deploying these systems for long-term installations in security and care environments. Our robots have been operational for a combined duration of 104 days over four deployments, autonomously performing end-user-defined tasks and traversing 116 km in the process. In this article, we describe the approach we used to enable long-term autonomous operation in everyday environments and how our robots are able to use their long run times to improve their own performance. An abstraction of a problem of rearranging group of mobile robots is addressed in this paper (the problem of multi-robot path planning). The robots are moving in an environment in which they must avoid obstacles and each other. An abstraction where the environment is modeled as an undirected graph is adopted throughout this work. A case when the graph modeling the environment is bi-connected is particularly studied. This paper puts into a relation the well known problems of moving pebbles on graphs (sliding box puzzles) with problems of multi-robot path planning. Theoretical results gained for problems of pebble motion on graphs are utilized for the development of algorithms for multi-robot path planning. As the opti-mization variant of both problems (a shortest solution is required) is known to be computationally hard (NP-hard), we concentrate on construction of sub-optimal solving procedures. However, the quality of solution is still an objective. Therefore a process of composition of a sub-optimal solution of the problem (a plan) of the pre-calculated optimal plans for the sub-problems (macros) is suggested. The plan composition using macros was inte-grated into two existing sub-optimal solving algorithms. In both cases, substantial improvements of the quality of resulting plans were achieved in comparison to the origi-nal algorithms. The no less important result is that one of the existing algorithms was generalized by integrating macros for larger class of problems of multi-robot path planning. Evolutionary Robotics (ER) is one of promising approaches to design robot controllers which essentially have complicated and or complex properties. In most ER research, the sensory-motor mappings of robots are represented as artificial neural networks, and their connection weights (and sometimes the structure of the networks) can be optimized in the parameter spaces by using evolutionary computation. However, generally, the evolved neural controllers could be fragile in unexperienced environments, especially in real worlds, because the evolutionary optimization processes would be executed in idealized simulators. This is known as the gap problem between the simulated and real worlds. To overcome this, the author focused on evolving an on-line learning ability instead of weight parameters in a simulated environment. According to recent biological findings, actually, the kinds of on-line adaptation abilities can be found in real nervous systems of insects and crustaceans, and it is also known that a variety of neuromodulators (NMs) play crucial roles to regulate the network characteristics (i.e. activating blocking changing of synaptic connections). Based on this, a neuromodulatory neural network model was proposed and it was utilized as a mobile robot controller. In the paper, the detail behavior analysis of the evolved neuromodulatory neural network is also discussed. From an engineering point of view, the problem of coordinating a set of autonomous, mobile robots for the purpose of cooperatively performing a task has been studied extensively over the past decade. In contrast, in this paper we aim to understand the fundamental algorithmic limitations on what a set of autonomous mobile robots can or cannot achieve. We therefore study a hard task for a set of weak robots. The task is for the robots in the plane to form any arbitrary pattern that is given in advance. This task is fundamental in the sense that if the robots can form any pattern, they can agree on their respective roles in a subsequent, coordinated action. The robots are weak in several aspects. They are anonymous; they cannot explicitly communicate with each other, but only observe the positions of the others; they cannot remember the past; they operate in a very strong form of asynchronicity. We show that the tasks that such a system of robots can perform depend strongly on their common agreement about their environment, i.e. the readings of their environment sensors. If the robots have no common agreement about their environment, they cannot form an arbitrary pattern. If each robot has a compass needle that indicates North (the robot world is a flat surface, and compass needles are parallel), then any odd number of robots can form an arbitrary pattern, but an even number cannot (in the worst case). If each robot has two independent compass needles, say North and East, then any set of robots can form any pattern. For robots to coexist with humans in a social world like ours, it is crucial that they possess human-like social interaction skills. Programming a robot to possess such skills is a challenging task. In this paper, we propose a Multimodal Deep Q-Network (MDQN) to enable a robot to learn human-like interaction skills through a trial and error method. This paper aims to develop a robot that gathers data during its interaction with a human, and learns human interaction behavior from the high dimensional sensory information using end-to-end reinforcement learning. This paper demonstrates that the robot was able to learn basic interaction skills successfully, after 14 days of interacting with people. In this paper, a physically interactive control scheme is developed for a manipulator robot arm. The human touches the robot and applies force in order to make it behave as he she likes. The communication between the robot and the human is maintained by a physical contact with no sensors. The intent of the human is estimated by observing the change in control effort. The robot receives the estimated human intent and updates its position reference accordingly. The developed method uses the principle of conservation of zero momentum for position-controlled systems. A switching scheme is developed that goes between the modes of pure impedance control with a fixed-position reference and interactive control under human intent. The switching mechanism uses neither a physical switch nor a sensor; it observes the human intent and puts the robot into interactive mode, if there is any. When the human intent disappears, the robot goes into the pure-impedance-control mode, thus stabilizing in the left position.