Dual dynamics: Designing behavior systems for autonomous robots

This paper describes the “dual dynamics” (DD) design scheme for robotic behavior control systems. Behaviors are formally specified as dynamical systems using differential equations. A key idea for the DD scheme is that a robotic agent can work in different “modes,” which lead to qualitatively different behavioral patterns. Mathematically, transitions between modes are bifurcations in the control system. This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1997     

Language games for autonomous robots

Integration and grounding are key AI challenges for human-robot dialogue. The author and his team are tackling these issues using language games and have experimented with them on progressively more complex platforms. A language game is a sequence of verbal interactions between two agents situated in a specific environment. Language games both integrate the various activities required for dialogue and ground unknown words or phrases in a specific context, which helps constrain possible meanings.     

Developmental learning for autonomous robots

Developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of competence.This paper uses ideas and inspiration from early infant psychology (up to three months of age) to examine how robot systems could discover the structure of their local sensory-motor spaces and learn how to coordinate these for the control of action.An experimental learning model is described and results from robotic experiments using the model are presented and discussed.     

Adaptive navigation for autonomous robots

In many robotic exploration missions, robots have to learn specific policies that allow them to: (i) select high level goals (e.g., identify specific destinations), (ii) navigate (reach those destinations), (iii) and adapt to their environment (e.g., modify their behavior based on changing environmental conditions). Furthermore, those policies must be robust to signal noise or unexpected situations, scalable to more complex environments, and account for the physical limitations of the robots (e.g., limited battery power and computational power).In this paper we evaluate reactive and learning navigation algorithms for exploration robots that must avoid obstacles and reach specific destinations in limited time and with limited observations. Our results show that neuro-evolutionary algorithms with well-designed evaluation functions can produce up to 50% better performance than reactive algorithms in complex domains where the robot’s goals are to select paths that lead to seek specific destinations while avoiding obstacles, particularly when facing significant sensor and actuator signal noise.     

Entropy-driven on-line control of autonomous robots

A new approach to on-line path planning is derived in this paper. The planning algorithm is motivated by robot navigation and manipulation tasks in uncertain, unstructured, dynamic environments. A minimum entropy evidential classifier is used to recognize targets and obstacles in the environment. An iterative Newton scheme is then used to generate a sequence of knot points that guide the motion of the robot. The acquisition and processing of sensory data continue during the motion, thus reducing the uncertainty about the environment. The classification of targets and obstacles is updated, and the path is replanned (locally) to adapt to those changes. A graphical tool based on the concept of Julia sets is used to ensure the predictability and smoothness of the paths.     

Khepera robots applied to highway autonomous mobiles

This article presents simulation models of autonomous Khepera robots which are assumed to be running on a highway. Each robot acts by following the fish-school algorithm. Although a school of fish does not need a special individual to lead it, an autonomous movement emerges from interactions among neighboring bodies. Our goal is multirobots which behave safely, with no accidents, solely through interactions with their surroundings. When Khepera robots run freely while sensing neighboring robots or the guard rails along the road by means of an infrared ray, the efficiency of their running, such as the distance covered and the number of accidents, is obtained with an evaluation function. Genetic algorithms (GA) with this evaluation function are applied to both the optimization of the discernible region, and the development of driving-type. As a result of optimization of the behavior models of a robot, multirobots could run smoothly while avoiding collisions with other robots or with guard rails, and yet run as fast as possible. The present study of autonomous multirobots approaches the realization of the autonomous control of vehicles running on a highway. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Introducing autonomous aerial robots in industrial manufacturing

Although ground robots have been successfully used for many years in manufacturing, the capability of aerial robots to agilely navigate in the often sparse and static upper part of factories makes them suitable for performing tasks of interest in many industrial sectors. This paper presents the design, development, and validation of a fully autonomous aerial robotic system for manufacturing industries. It includes modules for accurate pose estimation without using a Global Navigation Satellite System (GNSS), autonomous navigation, radio-based localization, and obstacle avoidance, among others, providing a fully onboard solution capable of autonomously performing complex tasks in dynamic indoor environments in which all necessary sensors, electronics, and processing are on the robot. It was developed to fulfill two use cases relevant in many industries: light object logistics and missing tool search. The presented robotic system, functionalities, and use cases have been extensively validated with Technology Readiness Level 7 (TRL-7) in the Centro Bahía de Cádiz (CBC) Airbus D&S factory in fully working conditions.     

Legal framework for small autonomous agricultural robots

Legal structures may form barriers to, or enablers of, adoption of precision agriculture management with small autonomous agricultural robots. This article develops a conceptual regulatory framework for small autonomous agricultural robots, from a practical, self-contained engineering guide perspective, sufficient to get working research and commercial agricultural roboticists quickly and easily up and running within the law. The article examines the liability framework, or rather lack of it, for agricultural robotics in EU, and their transpositions to UK law, as a case study illustrating general international legal concepts and issues. It examines how the law may provide mitigating effects on the liability regime, and how contracts can be developed between agents within it to enable smooth operation. It covers other legal aspects of operation such as the use of shared communications resources and privacy in the reuse of robot-collected data. Where there are some grey areas in current law, it argues that new proposals could be developed to reform these to promote further innovation and investment in agricultural robots.

     

Energetically autonomous robots: Food for thought

This paper reports on the robot EcoBot-II, which is designed to power itself solely by converting unrefined insect biomass into useful energy using on-board microbial fuel cells with oxygen cathodes. In bench experiments different ‘fuels’ (sugar, fruit and dead flies) were explored in the microbial fuel cell system and their efficiency of conversion to electricity is compared with the maximum available energy calculated from bomb calorimetry trials. In endurance tests EcoBot-II was able to run for 12 days while carrying out phototaxis, temperature sensing and radio transmission of sensed data approximately every 14 min.     

Deliberation and reactivity in autonomous mobile robots

This paper discusses issues related to the design of the control architectures for an autonomous mobile robot capable of performing tasks efficiently and intelligently, i.e. in a manner adapted to its environment, to its own state and to the execution status of its task. We present our developments and experimentations on mobile robot navigation and show how it is necessary to produce representations at several levels of abstraction, that are used by adequate processes for obstacle detection, target recognition, robot localization, and motion planning and control. We also show that deliberation is necessary for the robot in order to anticipate events, take efficient decisions, and react adequately to asynchronous events. We also discuss the organization of the system, i.e. the design of the control architecture.     

Measuring learning progress in intelligent autonomous robots

AI researchers claim to understand some aspect of human intelligence when their model is able to “emulate” it. In the contexts of mobile robots, the ability to go from teleoperation to autonomy should accordingly be treated not simply as a trick for robotics hackers but as an important epistemological and methodological goal. In this article the experience derived from designing locomotion control systems for both teleoperated and autonomous multi-legged articulated robots is described.     

Hybrid autonomous control for multi mobile robots

Reinforcement learning can be an adaptive and flexible control method for autonomous system. It does not need a priori knowledge; behaviors to accomplish given tasks are obtained automatically by repeating trial and error. However, with increasing complexity of the system, the learning costs are increased exponentially. Thus, application to complex systems, like a many redundant d.o.f. robot and multi-agent system, is very difficult. In the previous works in this field, applications were restricted to simple robots and small multi-agent systems, and because of restricted functions of the simple systems that have less redundancy, effectiveness of reinforcement learning is restricted. In our previous works, we had taken these problems into consideration and had proposed new reinforcement learning algorithm, 'Q-learning with dynamic structuring of exploration space based on GA (QDSEGA)'. Effectiveness of QDSEGA for redundant robots has been demonstrated using a 12-legged robot and a 50-link manipulator. However, previous works on QDSEGA were restricted to redundant robots and it was impossible to apply it to multi mobile robots. In this paper, we extend our previous work on QDSEGA by combining a rule-based distributed control and propose a hybrid autonomous control method for multi mobile robots. To demonstrate the effectiveness of the proposed method, simulations of a transportation task by 10 mobile robots are carried out. As a result, effective behaviors have been obtained.     

Conceptual representations of actions for autonomous robots

An autonomous robot involved in long and complex missions should be able to generate, update and process its own plans of action. In this perspective, it is not plausible that the meaning of the representations used by the robot is given from outside the system itself. Rather, the meaning of internal symbols must be firmly anchored to the world through the perceptual abilities and the overall activities of the robot. According to these premises, in this paper we present an approach to action representation that is based on a “conceptual” level of representation, acting as an intermediate level between symbols and data coming from sensors. Symbolic representations are interpreted by mapping them on the conceptual level through a mapping mechanism based on artificial neural networks. Examples of the proposed framework are reported, based on experiments performed on a RWI-B12 autonomous robot.     

Towards principled experimental study of autonomous mobile robots

We review the current state of research in autonomous mobile robots and conclude that there is an inadequate basis for predicting the reliability and behavior of robots operating in unengineered environments. We present a new approach to the study of autonomous mobile robot performance based on formal statistical analysis of independently reproducible experiments conducted on real robots. Simulators serve as models rather than experimental surrogates. We demonstrate three new results: 1) Two commonly used performance metrics (time and distance) are not as well correlated as is often tacitly assumed. 2) The probability distributions of these performance metrics are exponential rather than normal, and 3) a modular, object-oriented simulation accurately predicts the behavior of the real robot in a statistically significant manner.     

Portable autonomous walk calibration for 4-legged robots

In the present paper we describe an efficient and portable optimization method for calibrating the walk parameters of a quadruped robot, and its contribution for the robot control and localization. The locomotion of a legged robot presents not only the problem of maximizing the speed, but also the problem of obtaining a precise speed response, and achieving an acceptable odometry information. In this study we use a simulated annealing algorithm for calibrating different parametric sets for different speed ranges, with the goal of avoiding discontinuities. The results are applied to the robot AIBO in the RoboCup domain. Moreover, we outline the relevance of calibration to the control, showing the improvement obtained in odometry and, as a consequence, in robot localization.     

Bioinspired algorithm for area surveillance using autonomous robots

Territorial surveillance plays a constantly increasing role in security. However, completely automatic surveillance using autonomous robots is hard to implement and maintain. Current methods described in literature propose systems that include direct communication of the robots or the use of a centralised system to coordinate the robots. These systems are prone to equipment failure and/or malicious attacks to the centralised system. In this paper, we propose a bioinspired algorithm that allows indirect communication between the robots that are considered minimally equipped. This is achieved by applying a parallel and distributed technique inspired by the emergent behaviour of social insects, namely ant colonies. In particular, the development of a collective memory for robots and areas covered is achieved subsequently through self-organisation of the autonomous robots to a continuous dynamic coverage of the test space. The algorithm is shown to have a robust behaviour and competitive performance. Several simulations run for various space sizes, different number of robots, different pheromone evaporation rates as well various percentages of space covered by obstacles. In all cases the efficacy of the proposed algorithm has been successfully proven when compared with other well known techniques.     

Enhanced continuous valued Q-learning for real autonomous robots

A parallel-jaw gripper is a very useful tool for robot manipulation tasks due to its simple mechanism and control. This fact limits the range of successful grasps it can undergo, and also makes it unfeasible under uncertainties. Thus, it is desirable to improve its dexterity and manipulability. In this paper, we propose a new design of a two-fingered parallel gripper that utilizes rolling at the contacts for object repositioning and reorientation, aimed at effective firm grasps. We name it the scrollic gripper, an acronym for synchronously closing with rolling constraints. At first, the background to utilize the rolling constraints is described. Then, grasping and manipulation of the gripper are discussed. In grasp acquisition, we propose a quality function for evaluating grasp stability. The sophisticated hardware and functioning for the scrollic gripper consist, basically, on implementation of an additional degree-of-freedom to the conventional parallel-jaw gripper, leading to grasp acquisition and secure grasping.