Design Of Autonomous Robots Research Articles

Robot Proficiency Self-Assessment Using Assumption-Alignment Tracking

While the design of autonomous robots often emphasizes developing proficient robots, another important attribute of autonomous robot systems is their ability to evaluate their own proficiency. A robot should be able to assess how well it can perform a task before, during, and after it has attempted the task. How can autonomous robots be designed to self-assess their behavior? This article presents the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">assumption-alignment tracking</i> (AAT) method for designing autonomous robots that can effectively evaluate their own performance. In AAT, the robot a) tracks the veracity of assumptions made by the robot's decision-making algorithms to measure how well these algorithms fit, or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">align with</i> , its environment and hardware systems, and b) uses the measurement of alignment to assess the robot's ability to succeed at a given task based on its past experiences. The efficacy of AAT is illustrated through three case studies: a simulated robot navigating in a maze-based (discrete time) Markov chain environment, a simulated robot navigating in a continuous environment, and a real-world robot arranging blocks of different shapes and colors in a specific order on a table. Results show that AAT is able to accurately predict robot performance and, hence, determine robot proficiency in real time.

The Impact of Autonomous Robot Design and Programming on Student Creativity

The Impact of Autonomous Robot Design and Programming on Student Creativity

Collaborative Autonomy: Human–Robot Interaction to the Test of Intelligent Help

A big challenge in human–robot interaction (HRI) is the design of autonomous robots that collaborate effectively with humans, exposing behaviors similar to those exhibited by humans when they interact with each other. Indeed, robots are part of daily life in multiple environments (i.e., cultural heritage sites, hospitals, offices, touristic scenarios and so on). In these contexts, robots have to coexist and interact with a wide spectrum of users not necessarily able or willing to adapt their interaction level to the kind requested by a machine: the users need to deal with artificial systems whose behaviors must be adapted as much as possible to the goals/needs of the users themselves, or more in general, to their mental states (beliefs, goals, plans and so on). In this paper, we introduce a cognitive architecture for adaptive and transparent human–robot interaction. The architecture allows a social robot to dynamically adjust its level of collaborative autonomy by restricting or expanding a delegated task on the basis of several context factors such as the mental states attributed to the human users involved in the interaction. This collaboration has to be based on different cognitive capabilities of the robot, i.e., the ability to build a user’s profile, to have a Theory of Mind of the user in terms of mental states attribution, to build a complex model of the context, intended both as a set of physical constraints and constraints due to the presence of other agents, with their own mental states. Based on the defined cognitive architecture and on the model of task delegation theorized by Castelfranchi and Falcone, the robot’s behavior is explainable by considering the abilities to attribute specific mental states to the user, the context in which it operates and its attitudes in adapting the level of autonomy to the user’s mental states and the context itself. The architecture has been implemented by exploiting the well known agent-oriented programming framework Jason. We provide the results of an HRI pilot study in which we recruited 26 real participants that have interacted with the humanoid robot Nao, widely used in HRI scenarios. The robot played the role of a museum assistant with the main goal to provide the user the most suitable museum exhibition to visit.

Responsible development of autonomous robotics in agriculture.

Despite the potential contributions of autonomous robots to agricultural sustainability, social, legal and ethical issues threaten adoption. We discuss how responsible innovation principles can be embedded into the user-centred design of autonomous robots and identify areas for further empirical research.

Multi-Sensor Fusion for Navigation and Mapping in Autonomous Vehicles: Accurate Localization in Urban Environments

The combination of data from multiple sensors, also known as sensor fusion or data fusion, is a key aspect in the design of autonomous robots. In particular, algorithms able to accommodate sensor fusion techniques enable increased accuracy, and are more resilient against the malfunction of individual sensors. The development of algorithms for autonomous navigation, mapping and localization have seen big advancements over the past two decades. Nonetheless, challenges remain in developing robust solutions for accurate localization in dense urban environments, where the so-called last-mile delivery occurs. In these scenarios, local motion estimation is combined with the matching of real-time data with a detailed pre-built map. In this paper, we utilize data gathered with an autonomous delivery robot to compare different sensor fusion techniques and evaluate which are the algorithms providing the highest accuracy depending on the environment. The techniques we analyze and propose in this paper utilize 3D lidar data, inertial data, GNSS data and wheel encoder readings. We show how lidar scan matching combined with other sensor data can be used to increase the accuracy of the robot localization and, in consequence, its navigation. Moreover, we propose a strategy to reduce the impact on navigation performance when a change in the environment renders map data invalid or part of the available map is corrupted.

ARDEA—An MAV with skills for future planetary missions

AbstractWe introduce a prototype flying platform for planetary exploration: autonomous robot design for extraterrestrial applications (ARDEA). Communication with unmanned missions beyond Earth orbit suffers from time delay, thus a key criterion for robotic exploration is a robot's ability to perform tasks without human intervention. For autonomous operation, all computations should be done on‐board and Global Navigation Satellite System (GNSS) should not be relied on for navigation purposes. Given these objectives ARDEA is equipped with two pairs of wide‐angle stereo cameras and an inertial measurement unit (IMU) for robust visual‐inertial navigation and time‐efficient, omni‐directional 3D mapping. The four cameras cover a vertical field of view, enabling the system to operate in confined environments such as caves formed by lava tubes. The captured images are split into several pinhole cameras, which are used for simultaneously running visual odometries. The stereo output is used for simultaneous localization and mapping, 3D map generation and collision‐free motion planning. To operate the vehicle efficiently for a variety of missions, ARDEA's capabilities have been modularized into skills which can be assembled to fulfill a mission's objectives. These skills are defined generically so that they are independent of the robot configuration, making the approach suitable for different heterogeneous robotic teams. The diverse skill set also makes the micro aerial vehicle (MAV) useful for any task where autonomous exploration is needed. For example terrestrial search and rescue missions where visual navigation in GNSS‐denied indoor environments is crucial, such as partially collapsed man‐made structures like buildings or tunnels. We have demonstrated the robustness of our system in indoor and outdoor field tests.

Particle Swarm Optimization for robot target tracking application

Tracking is a process of finding continuous path of a selected target. Tracking is important in various civil and defense applications. For tracking applications, various adaptive filters are being used such as Kalman Filter and it's variants. In this paper, a popular meta-heuristic algorithm, namely, Particle Swarm Optimization (PSO) algorithm is applied to track a target which is a robot and it is transmitting noisy data due to installation of cheap quality sensors onboard. These noisy observations are filtered by PSO algorithm in order to estimate true path of the robot. The simulated results are presented by using MATLAB software. From the results, it is observed that the PSO algorithm is very effective algorithm in estimating the target location continuously with almost zero estimation error. Thus, Particle Swarm Optimization algorithm is a suitable algorithm for robot tracking applications and it can also be used for nonlinear data processing in autonomous robot design applications.

A Safety Monitoring Model for a Faulty Mobile Robot

The continued development of mobile robots (MR) must be accompanied by an increase in robotics’ safety measures. Not only must MR be capable of detecting and diagnosing faults, they should also be capable of understanding when the dangers of a mission, to themselves and the surrounding environment, warrant the abandonment of their endeavors. Analysis of fault detection and diagnosis techniques helps shed light on the challenges of the robotic field, while also showing a lack of research in autonomous decision-making tools. This paper proposes a new skill-based architecture for mobile robots, together with a novel risk assessment and decision-making model to overcome the difficulties currently felt in autonomous robot design.

A Highly-Maneuverable Demining Autonomous Robot

Based on the well-known advantages of using an over-actuated mechanism for robots, this research proposes a holonomic highly-maneuverable autonomous robot design for demining service applications. ...

A Highly-Maneuverable Demining Autonomous Robot: an Over-Actuated Design

Based on the well-known advantages of using an over-actuated mechanism for robots, this research proposes a holonomic highly-maneuverable autonomous robot design for demining service applications. The proposed approach provides an interesting compromise between the design requirements of the demining robot applications and the over-actuated autonomous robots. The robot body is mainly divided into two parts: the first part provides the robot with its required locomotion and it consists of a driving/steering subsystem with four driving wheels (4WD), four steering mechanisms (4SW), and a passive suspension subsystem. The second part is a manipulator with three degrees of freedom that is designed based on two parallelogram mechanisms. The proposed design insures many advantages over existing designs, including stability, maneuverability, autonomous navigation, and simplicity of the control effort constraints. The robot model and its corresponding stability analysis were conducted and simulated in order to evaluate the motion of the robot over different environments rough terrains and slanted surfaces. Moreover, a prototype of the proposed robot was developed and built and different types of sensors were used in order to help it take precise actuation decisions for navigation and control. The prototype was experimentally tested for different scenarios and environments in order to validate the proposed design. The testing results demonstrated decent performance of the robot in autonomous navigation and in localizing the detected objects.

Autonomous Robots and the SP Theory of Intelligence

This article is about how the "SP theory of intelligence" and its realisation in the "SP machine" (both outlined in the article) may help to solve computer-related problems in the design of autonomous robots, meaning robots that do not depend on external intelligence or power supplies, are mobile, and are designed to exhibit as much human-like intelligence as possible. The article is about: how to increase the computational and energy efficiency of computers and reduce their bulk; how to achieve human-like versatility in intelligence; and likewise for human-like adaptability in intelligence. The SP system has potential for substantial gains in computational and energy efficiency and reductions in the bulkiness of computers: by reducing the size of data to be processed; by exploiting statistical information that the system gathers; and via an updated version of Donald Hebb's concept of a "cell assembly". Towards human-like versatility in intelligence, the SP system has strengths in unsupervised learning, natural language processing, pattern recognition, information retrieval, several kinds of reasoning, planning, problem solving, and more, with seamless integration amongst structures and functions. The SP system's strengths in unsupervised learning and other aspects of intelligence may help to achieve human-like adaptability in intelligence via: the learning of natural language; learning to see; building 3D models of objects and of a robot's surroundings; learning regularities in the workings of a robot and in the robot's environment; exploration and play; learning major skills; and secondary forms of learning. Also discussed are: how the SP system may process parallel streams of information; generalisation of knowledge, correction of over-generalisations, and learning from dirty data; how to cut the cost of learning; and reinforcements, motivations, goals, and demonstration.

Modeling the insect mushroom bodies: Application to a delayed match-to-sample task

Despite their small brains, insects show advanced capabilities in learning and task solving. Flies, honeybees and ants are becoming a reference point in neuroscience and a main source of inspiration for autonomous robot design issues and control algorithms. In particular, honeybees demonstrate to be able to autonomously abstract complex associations and apply them in tasks involving different sensory modalities within the insect brain. Mushroom Bodies (MBs) are worthy of primary attention for understanding memory and learning functions in insects. In fact, even if their main role regards olfactory conditioning, they are involved in many behavioral achievements and learning capabilities, as has been shown in honeybees and flies. Owing to the many neurogenetic tools, the fruit fly Drosophila became a source of information for the neuroarchitecture and biochemistry of the MBs, although the MBs of flies are by far simpler in organization than their honeybee orthologs. Electrophysiological studies, in turn, became available on the MBs of locusts and honeybees. In this paper a novel bio-inspired neural architecture is presented, which represents a generalized insect MB with the basic features taken from fruit fly neuroanatomy. By mimicking a number of different MB functions and architecture, we can replace and improve formerly used artificial neural networks. The model is a multi-layer spiking neural network where key elements of the insect brain, the antennal lobes, the lateral horn region, the MBs, and their mutual interactions are modeled. In particular, the model is based on the role of parts of the MBs named MB-lobes, where interesting processing mechanisms arise on the basis of spatio-temporal pattern formation. The introduced network is able to model learning mechanisms like olfactory conditioning seen in honeybees and flies and was found able also to perform more complex and abstract associations, like the delayed matching-to-sample tasks known only from honeybees. A biological basis of the proposed model is presented together with a detailed description of the architecture. Simulation results and remarks on the biological counterpart are also reported to demonstrate the possible applications of the designed computational model. Such neural architecture, able to autonomously learn complex associations is envisaged to be a suitable basis for an immediate implementation within an robot control architecture.

Aristotle, autonomy and the explanation of behaviour

This paper examines Aristotle’s notion of autonomy and its implication for the mechanicism/autonomy debate. We introduce the basic principles of Aristotle’s scientific framework, including his theory of four causes for the explanation of nature. We draw parallels between these notions of autonomy and causation and autopoietic theory, dynamical systems and robotics, suggesting that they may be compatible with Aristotle’s framework. We argue that understanding the problem of design of autonomous robots may benefit from the consideration of integration of Aristotle’s causes, while robotics, in turn, may contribute to the debate providing a common ground for epistemological and ontological notions of autonomy.

Self-Organization, Embodiment, and Biologically Inspired Robotics

Robotics researchers increasingly agree that ideas from biology and self-organization can strongly benefit the design of autonomous robots. Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, indefinite richness, and limited availability of information. Industrial robots, in contrast, operate in highly controlled environments with no or very little uncertainty. Although many challenges remain, concepts from biologically inspired (bio-inspired) robotics will eventually enable researchers to engineer machines for the real world that possess at least some of the desirable properties of biological organisms, such as adaptivity, robustness, versatility, and agility.

Learning Invariant Sensorimotor Behaviors: A Developmental Approach to Imitation Mechanisms

This paper examines the interest of a developmental approach applied to the design of autonomous robots and the understanding of adaptive behaviors, such as imitation. The proposed model is a neural network architecture that learns and uses associations between vision and arm movements, even if the problem is ill posed (in the case of mapping problems between the visual space and the joints space of the arm). The central part of the model is a visuo-motor map able to represent the arm end point’s position in an ego-centered space (constrained by the vision) according to motor information (the proprioception). Sensorimotor behaviors such as tracking, pointing, spontaneous imitating, and sequences learning can then be obtained as the consequence of different internal dynamics computed on neural fields triggered by the visuo-motor map. The readout mechanism also explains how an apparently complex behavior can be generated and controlled from one simple internal dynamics and how at the same time the learning problems can be simplified. While highlighting the generic aspect of our model, we show that our robot can autonomously imitate and learn more complex sequences of gestures after the online learning of the visual and proprioceptive control of its hand extremity. Finally, we defend the idea of a co-development of imitative and sensorimotor capabilities, allowing the acquisition and the building of increasingly complex behavioral capabilities.

Function Meets Style: Insights From Emotion Theory Applied to HRI

As robot designers, we tend to emphasize the cognitive aspect of intelligence when designing robot architectures while viewing the affective aspect with skepticism. However, scientific studies continue to reveal the deeply intertwined and complementary roles that cognition and emotion play in intelligent decision-making, planning, learning, attention, communication, social interaction, memory, and more. Such findings provide valuable insights and lessons for the design of autonomous robots that must operate in complex and uncertain environments and perform in cooperation with people. This paper presents a concrete implementation of how these insights have guided our work, focusing on the design of sociable autonomous robots that interact with people as capable partners.

Robot navigation inspired by principles of insect vision

Recent studies of insect visual behaviour and navigation reveal a number of elegant strategies that can be profitably applied to the design of autonomous robots. The peering behaviour of grasshoppers, for example, has inspired the design of new rangefinding systems. The centring response of bees flying through a tunnel has led to simple methods for navigating through corridors. Experimental investigation of the bee's “odometer” has led to the implementation of schemes for visually driven odometry. These and other visually mediated insect behaviours are described along with a number of applications to robot navigation.

Lifelong robot learning

Learning provides a useful tool for the automatic design of autonomous robots. Recent research on learning robot control has predominantly focussed on learning single tasks that were studied in isolation. If robots encounter a multitude of control learning tasks over their entire lifetime there is an opportunity to transfer knowledge between them. In order to do so, robots may learn the invariants and the regularities of their individual tasks and environments. This task-independent knowledge can be employed to bias generalization when learning control, which reduces the need for real-world experimentation. We argue that knowledge transfer is essential if robots are to learn control with moderate learning times in complex scenarios. Two approaches to lifelong robot learning which both capture invariant knowledge about the robot and its environments are presented. Both approaches have been evaluated using a HERO-2000 mobile robot. Learning tasks included navigation in unknown indoor environments and a simple find-and-fetch task.