Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems

This paper proposes a novel distributed coverage controller for a multi-agent system with constant-speed unicycle robots (CSUR). The work is motivated by the limitation of the conventional method that does not ensure the satisfaction of hard state- and input-dependent constraints and leads to feasibility issues for multi-CSUR systems. In this paper, we solve these problems by designing a novel coverage cost function and a saturated gradient-search-based control law. Invariant set theory and Lyapunov-based techniques are used to prove the state-dependent confinement and the convergence of the system state to the optimal coverage configuration, respectively. The controller is implemented in a distributed manner based on a novel communication standard among the agents. A series of simulation case studies are conducted to validate the effectiveness of the proposed coverage controller in different initial conditions and with control parameters. A comparison study in simulation reveals the advantage of the proposed method in terms of avoiding infeasibility. The experiment study verifies the applicability of the method to real robots with uncertainties. The development procedure of the method from theoretical analysis to experimental validation provides a novel framework for multi-agent system coordinate control with complex agent dynamics

Taking Inspiration from Flying Insects to Navigate inside Buildings

These days, flying insects are seen as genuinely agile micro air vehicles fitted with smart sensors and also parsimonious in their use of brain resources. They are able to visually navigate in unpredictable and GPS-denied environments. Understanding how such tiny animals work would help engineers to figure out different issues relating to drone miniaturization and navigation inside buildings. To turn a drone of ~1 kg into a robot, miniaturized conventional avionics can be employed; however, this results in a loss of their flight autonomy. On the other hand, to turn a drone of a mass between ~1 g (or less) and ~500 g into a robot requires an innovative approach taking inspiration from flying insects both with regard to their flapping wing propulsion system and their sensory system based mainly on motion vision in order to avoid obstacles in three dimensions or to navigate on the basis of visual cues. This chapter will provide a snapshot of the current state of the art in the field of bioinspired optic flow sensors and optic flow-based direct feedback loops applied to micro air vehicles flying inside buildings

Omni-directional catadioptric vision for soccer robots

This paper describes the design of a multi-part mirror catadioptric vision system and its use for self-localization and detection of relevant objects in soccer robots. The mirror and associated algorithms have been used in robots participating in the middle-size league of RoboCup — The World Cup of Soccer Robots.This work was supported by grant PRAXIS XXI BM/21091/99 of the Portuguese Foundation for Science and Technolog

Human-Robot Handshaking: A Review

For some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours.Comment: Pre-print version. Accepted for publication in the International Journal of Social Robotic

Mobile Manipulation with a Kinematically Redundant Manipulator for a Pick-and-Place Scenario

Mobile robots and robotic manipulators have traditionally been used separately performing different types of tasks. For example, industrial robots have typically been programmed to follow trajectories using position sensors. If combining the two types of robots and adding sensors new possibilities emerge. This enables new applications, but it also raises the question of how to combine the sensors and the added kinematic complexity. An omni-directional mobile robot together with a new type of kinematically redundant manipulator for future use as a service robot for grocery stores is proposed. The scenario is that of distributing groceries on refilling shelves, and a constraint- based task specification methodology to incorporate sensors and geometric uncertainties into the task is employed. Sensor fusion is used to estimate the pose of the mobile base online. Force sensors are utilized to resolve remaining uncertainties. The approach is verified with experiments

Survey: Robot Programming by Demonstration

Robot PbD started about 30 years ago, growing importantly during the past decade. The rationale for moving from purely preprogrammed robots to very flexible user-based interfaces for training the robot to perform a task is three-fold. First and foremost, PbD, also referred to as {\em imitation learning} is a powerful mechanism for reducing the complexity of search spaces for learning. When observing either good or bad examples, one can reduce the search for a possible solution, by either starting the search from the observed good solution (local optima), or conversely, by eliminating from the search space what is known as a bad solution. Imitation learning is, thus, a powerful tool for enhancing and accelerating learning in both animals and artifacts. Second, imitation learning offers an implicit means of training a machine, such that explicit and tedious programming of a task by a human user can be minimized or eliminated (Figure \ref{fig:what-how}). Imitation learning is thus a ``natural'' means of interacting with a machine that would be accessible to lay people. And third, studying and modeling the coupling of perception and action, which is at the core of imitation learning, helps us to understand the mechanisms by which the self-organization of perception and action could arise during development. The reciprocal interaction of perception and action could explain how competence in motor control can be grounded in rich structure of perceptual variables, and vice versa, how the processes of perception can develop as means to create successful actions. PbD promises were thus multiple. On the one hand, one hoped that it would make the learning faster, in contrast to tedious reinforcement learning methods or trials-and-error learning. On the other hand, one expected that the methods, being user-friendly, would enhance the application of robots in human daily environments. Recent progresses in the field, which we review in this chapter, show that the field has make a leap forward the past decade toward these goals and that these promises may be fulfilled very soon

Gait transition and modulation in a quadruped robot : a brainstem-like modulation approach

In this article, we propose a bio-inspired architecture for a quadruped robot that is able to initiate/stop locomotion; generate different gaits, and to easily select and switch between the different gaits according to the speed and/or the behavioral context. This improves the robot stability and smoothness while locomoting. We apply nonlinear oscillators to model Central Pattern Generators (CPGs). These generate the rhythmic locomotor movements for a quadruped robot. The generated trajectories are modulated by a tonic signal, that encodes the required activity and/or modulation. This drive signal strength is mapped onto sets of CPG parameters. By increasing the drive signal, locomotion can be elicited and velocity increased while switching to the appropriate gaits. This drive signal can be specified according to sensory information or set a priori. The system is implemented in a simulated and real AIBO robot. Results demonstrate the adequacy of the architecture to generate and modulate the required coordinated trajectories according to a velocity increase; and to smoothly and easily switch among the different motor behaviors.The authors gratefully acknowledge Keir Pearson for all the discussions and help. This work is funded by FEDER Funding supported by the Operational Program Competitive Factors COMPETE and National Funding supported by the FCT - Foundation for Science and Technology through project PTDC/EEACRO/100655/2008

Learning how to combine sensory-motor functions into a robust behavior

AbstractThis article describes a system, called Robel, for defining a robot controller that learns from experience very robust ways of performing a high-level task such as “navigate to”. The designer specifies a collection of skills, represented as hierarchical tasks networks, whose primitives are sensory-motor functions. The skills provide different ways of combining these sensory-motor functions to achieve the desired task. The specified skills are assumed to be complementary and to cover different situations. The relationship between control states, defined through a set of task-dependent features, and the appropriate skills for pursuing the task is learned as a finite observable Markov decision process (MDP). This MDP provides a general policy for the task; it is independent of the environment and characterizes the abilities of the robot for the task

Drama, a connectionist model for robot learning: experiments on grounding communication through imitation in autonomous robots

The present dissertation addresses problems related to robot learning from demonstra¬ tion. It presents the building of a connectionist architecture, which provides the robot with the necessary cognitive and behavioural mechanisms for learning a synthetic lan¬ guage taught by an external teacher agent. This thesis considers three main issues: 1) learning of spatio-temporal invariance in a dynamic noisy environment, 2) symbol grounding of a robot's actions and perceptions, 3) development of a common symbolic representation of the world by heterogeneous agents.We build our approach on the assumption that grounding of symbolic communication creates constraints not only on the cognitive capabilities of the agent but also and especially on its behavioural capacities. Behavioural skills, such as imitation, which allow the agent to co-ordinate its actionn to that of the teacher agent, are required aside to general cognitive abilities of associativity, in order to constrain the agent's attention to making relevant perceptions, onto which it grounds the teacher agent's symbolic expression. In addition, the agent should be provided with the cognitive capacity for extracting spatial and temporal invariance in the continuous flow of its perceptions. Based on this requirement, we develop a connectionist architecture for learning time series. The model is a Dynamical Recurrent Associative Memory Architecture, called DRAMA. It is a fully connected recurrent neural network using Hebbian update rules. Learning is dynamic and unsupervised. The performance of the architecture is analysed theoretically, through numerical simulations and through physical and simulated robotic experiments. Training of the network is computationally fast and inexpensive, which allows its implementation for real time computation and on-line learning in a inexpensive hardware system. Robotic experiments are carried out with different learning tasks involving recognition of spatial and temporal invariance, namely landmark recognition and prediction of perception-action sequence in maze travelling.The architecture is applied to experiments on robot learning by imitation. A learner robot is taught by a teacher agent, a human instructor and another robot, a vocabulary to describe its perceptions and actions. The experiments are based on an imitative strategy, whereby the learner robot reproduces the teacher's actions. While imitating the teacher's movements, the learner robot makes similar proprio and exteroceptions to those of the teacher. The learner robot grounds the teacher's words onto the set of common perceptions they share. We carry out experiments in simulated and physical environments, using different robotic set-ups, increasing gradually the complexity of the task. In a first set of experiments, we study transmission of a vocabulary to designate actions and perception of a robot. Further, we carry out simulation studies, in which we investigate transmission and use of the vocabulary among a group of robotic agents. In a third set of experiments, we investigate learning sequences of the robot's perceptions, while wandering in a physically constrained environment. Finally, we present the implementation of DRAMA in Robota, a doll-like robot, which can imitate the arms and head movements of a human instructor. Through this imitative game, Robota is taught to perform and label dance patterns. Further, Robota is taught a basic language, including a lexicon and syntactical rules for the combination of words of the lexicon, to describe its actions and perception of touch onto its body