Autonomous Robot Control System Research Articles

Automation of autonomous mobile robot docking based on the counter growth rapidly exploring random tree method

Objectives. The article substantiates the relevance of automatic docking of autonomous mobile robots. Specific examples show that the implementation of the automatic docking functions of autonomous robots reveals the potential for creating multi-agent systems with a transformable structure. The aim of the work is to develop means for automatic docking of autonomous mobile robots in complex scenarios and an uncertain environment.Methods. The proposed approach to automating autonomous mobile robot docking is reduced to a modification of the counter-growth rapidly-exploring random tree (RRT) method. It is based on the parallel execution of a decentralized route planning algorithm with mutual coordination of distributed computing processes. The effectiveness of the complex of algorithmic and software tools developed was evaluated using computer and natural simulation methods. The final series of full-scale experiments was carried out on the example of JetBot AI kit Nvidia platforms for automatic docking of autonomous robots. This was performed using the means and methods of intelligent control, visual navigation, technical vision and wireless network communication.Results. The study analyzed the features of automatic docking as one of the tasks of group control of autonomous robots. This is part of multi-agent systems, capable of reconfiguring structures for purposeful changes to the existing set of functional properties and application possibilities. The study also proposes a decentralized modification of the counter-growth RRT method. This allows the movements of autonomous mobile robots in the course of their mutual approach and subsequent docking to be planned. A set of software-algorithmic tools was developed to automate the docking of autonomous robots. A series of model and full-scale experiments were carried out to confirm the effectiveness of the approach developed herein.Conclusions. The modification presented herein of the counter-growth RRT method, traditionally used for planning the movements of manipulators and mobile platforms, is complementary to the tasks it resolves. This enables the docking of autonomous robots to be automated. The results obtained open up the potential for universal schedulers with extended functionality for autonomous robot control systems to be designed.

A possible approach to the creation of self-learning control systems for autonomous robots

Unmanned systems often function in new, non-predetermined environment. This demands flexible and simultaneously stable function of these systems. This can be implemented by their adaptation and, as a final goal, self-learning or self-organization in control systems. Autonomous functioning in extreme environment requires self-learning drastically. Such environment can be hostile towards unmanned systems, it may contain sophisticated communication conditions. All these are deteriorated by poor on-board control algorithms. In some of such cases unmanned systems may become inefficient or, even more, useless in some cases. This brings to front necessity to create adaptive self-learning control systems for robots. These should be capable to generate efficient, or even optimal decisions in extreme environment. This paper presents on of possible approaches to create self-learning control systems based on so called decision along analogues.

Situational analysis model in an intelligent system based on multi-agent neurocognitive architectures

An approach to the development of intelligent decision-making and control systems based on the hypothesis of the organization of neural activity of the brain in the process of performing cognitive functions is proposed. This approach, based on intelligent software agents with a developed cognitive architecture, is able to provide the process of extracting knowledge from an unstructured data flow, generalizing the knowledge and learning gained, to implement effective methods of synthesizing behavior aimed at solving various problems. A multi-agent model of situational analysis based on self-organization of distributed recursive neurocognitive architectures is presented. In particular, the basic principles of situational analysis based on multi-agent neurocognitive architectures are formulated and an algorithm for the preventive synthesis of the behavior of an intelligent agent aimed at avoiding negative situations for itself is developed. The performed computational experiment showed that on the basis of training the neurocognitive architecture by forming new agents-neurons and connections between them, a complex logical function of behavior control (in particular, situational analysis) develops (forms). The results of this study can be used to create intelligent decision-making and control systems for autonomous robots and robotic systems for various purposes.

Creating and Capturing Artificial Emotions in Autonomous Robots and Software Agents

This paper presents ARTEMIS, a control system for autonomous robots or software agents. ARTEMIS can create human-like artificial emotions during interactions with their environment. We describe the underlying mechanisms for this. The control system also captures its past artificial emotions. A specific interpretation of a knowledge graph, called an Agent Knowledge Graph, stores these artificial emotions. ARTEMIS then utilizes current and stored emotions to adapt decision making and planning processes. As proof of concept, we realize a concrete software agent based on the ARTEMIS control system. This software agent acts as a user assistant and executes their orders and instructions. The environment of this user assistant consists of several other autonomous agents that offer their services. The execution of a user’s orders requires interactions of the user assistant with these autonomous service agents. These interactions lead to the creation of artificial emotions within the user assistant. The first experiments show that it is possible to realize an autonomous user assistant with plausible artificial emotions with ARTEMIS and record these artificial emotions in its Agent Knowledge Graph. The results also show that captured emotions support successful planning and decision making in complex dynamic environments. The user assistant with emotions surpasses an emotionless version of the user assistant.

An autonomous robot control system based on an inverse problems method in dynamics

An autonomous robot control system based on an inverse problems method in dynamics

Feasibility of Synergy-Based Exoskeleton Robot Control in Hemiplegia.

Here, we present a study on exoskeleton robot control based on inter-limb locomotor synergies using a robot control method developed to target hemiparesis. The robot control is based on inter-limb locomotor synergies and kinesiological information from the non-paretic leg and a walking aid cane to generate motion patterns for the assisted leg. The developed synergy-based system was tested against an autonomous robot control system in five patients with hemiparesis and varying locomotor abilities. Three of the participants were able to walk using the robot. Results from these participants showed an improved spatial symmetry ratio and more consistent step length with the synergy-based method compared with that for the autonomous method, while the increase in the range of motion for the assisted joints was larger with the autonomous system. The kinematic synergy distribution of the participants walking without the robot suggests a relationship between each participant's synergy distribution and his/her ability to control the robot: participants with two independent synergies accounting for approximately 80% of the data variability were able to walk with the robot. This observation was not consistently apparent with conventional clinical measures such as the Brunnstrom stages. This paper contributes to the field of robot-assisted locomotion therapy by introducing the concept of inter-limb synergies, demonstrating performance differences between synergy-based and autonomous robot control, and investigating the range of disability in which the system is usable.

Software Product Line Engineering for Robotic Perception Systems

Control systems for autonomous robots are concurrent, distributed, embedded, real-time and data intensive software systems. A real-world robot control system is composed of tens of software components. For each component providing robotic functionality, tens of different implementations may be available. The difficult challenge in robotic system engineering consists in selecting a coherent set of components, which provide the functionality required by the application requirements, taking into account their mutual dependencies. This challenge is exacerbated by the fact that robotics system integrators and application developers are usually not specifically trained in software engineering. In various application domains, software product line (SPL) development has proven to be the most effective approach to face this kind of challenges. In a previous paper [D. Brugali and N. Hochgeschwender, Managing the functional variability of robotic perception systems, in First IEEE Int. Conf. Robotic Computing, 2017, pp. 277–283.] we have presented a model-based approach to the development of SPL for robotic perception systems, which integrates two modeling technologies developed by the authors: The HyperFlex toolkit [L. Gherardi and D. Brugali, Modeling and reusing robotic software architectures: The HyperFlex toolchain, in IEEE Int. Conf. Robotics and Automation, 2014, pp. 6414–6420.] and the Robot Perception Specification Language (RPSL) [N. Hochgeschwender, S. Schneider, H. Voos and G. K. Kraetzschmar, Declarative specification of robot perception architectures, in 4th Int. Conf. Simulation, Modeling, and Programming for Autonomous Robots, 2014, pp. 291–302.]. This paper extends our previous work by illustrating the entire development process of an SPL for robot perception systems with a real case study.

A Cognition-Based Control System for Autonomous Robots

Most approaches to controlling autonomous systems require extensive pre-mission preparation, intensive effort by the human operator, or have strong limitations on the range of possible missions that can be accomplished. In this paper we describe an approach called Cognitive Patterns that promises to alleviate these challenges by replicating three key processes of human cognition—pattern generation, perception/action, and adaptation—and instantiating them in a new architecture which can then be embedded into an autonomous system. An early version of this approach connected high-level knowledge representations in an ontology with a robot's sensing and acting abilities. The advantages of this approach were then demonstrated in a simulation environment. A more refined version, based on lessons learned and called Cognitive Patterns Knowledge Generation, can deal with anomalies, unexpected events, and uncertainties, and is also described in terms of its components, their interactions, and benefits.

Fitness functions in evolutionary robotics: A survey and analysis

This paper surveys fitness functions used in the field of evolutionary robotics (ER). Evolutionary robotics is a field of research that applies artificial evolution to generate control systems for autonomous robots. During evolution, robots attempt to perform a given task in a given environment. The controllers in the better performing robots are selected, altered and propagated to perform the task again in an iterative process that mimics some aspects of natural evolution. A key component of this process–one might argue, the key component–is the measurement of fitness in the evolving controllers. ER is one of a host of machine learning methods that rely on interaction with, and feedback from, a complex dynamic environment to drive synthesis of controllers for autonomous agents. These methods have the potential to lead to the development of robots that can adapt to uncharacterized environments and which may be able to perform tasks that human designers do not completely understand. In order to achieve this, issues regarding fitness evaluation must be addressed. In this paper we survey current ER research and focus on work that involved real robots. The surveyed research is organized according to the degree of a priori knowledge used to formulate the various fitness functions employed during evolution. The underlying motivation for this is to identify methods that allow the development of the greatest degree of novel control, while requiring the minimum amount of a priori task knowledge from the designer.

A hybrid control approach to action coordination for mobile robots

In this paper, the problem concerning how to coordinate the contributions from concurrent controllers, when controlling mobile robots, is investigated. It is shown how a behavior based control system for autonomous robots can be modeled as a hybrid automaton, where each node corresponds to a distinct robot behavior. This type of construction gives rise to chattering executions, but it is shown how regularized automata can be used to solve this problem. As an illustration, the obstacle-negotiation problem is solved by using a combination of a robust path-following behavior and a reactive obstacle-avoidance behavior that move the robot around a given obstacle at a predefined safety distance.

Artificial Evolution: A New Path for Artificial Intelligence?

Recently there have been a number of proposals for the use of artificial evolution as a radically new approach to the development of control systems for autonomous robots. This paper explains the artificial evolution approach, using work at Sussex to illustrate it. The paper revolves around a case study on the concurrent evolution of control networks and visual sensor morphologies for a mobile robot. Wider intellectual issues surrounding the work are discussed, as is the use of more abstract evolutionary simulations as a new potentially useful tool in theoretical biology.

Evolving mobile robots in simulated and real environments.

The problem of the validity of simulation is particularly relevant for methodologies that use machine learning techniques to develop control systems for autonomous robots, as, for instance, the artificial life approach known as evolutionary robotics. In fact, although it has been demonstrated that training or evolving robots in real environments is possible, the number of trials needed to test the system discourages the use of physical robots during the training period. By evolving neural controllers for a Khepera robot in computer simulations and then transferring the agents obtained to the real environment we show that (a) an accurate model of a particular robot-environment dynamics can be built by sampling the real world through the sensors and the actuators of the robot; (b) the performance gap between the obtained behaviors in simulated and real environments may be significantly reduced by introducing a "conservative" form of noise; (c) if a decrease in performance is observed when the system is transferred to a real environment, successful and robust results can be obtained by continuing the evolutionary process in the real environment for a few generations.

Evolving Mobile Robots in Simulated and Real Environments

The problem of the validity of simulation is particularly relevant for methodologies that use machine learning techniques to develop control systems for autonomous robots, as, for instance, the artificial life approach known as evolutionary robotics. In fact, although it has been demonstrated that training or evolving robots in real environments is possible, the number of trials needed to test the system discourages the use of physical robots during the training period. By evolving neural controllers for a Khepera robot in computer simulations and then transferring the agents obtained to the real environment we show that (a) an accurate model of a particular robot-environment dynamics can be built by sampling the real world through the sensors and the actuators of the robot; (b) the performance gap between the obtained behaviors in simulated and real environments may be significantly reduced by introducing a "conservative" form of noise; (c) if a decrease in performance is observed when the system is transferred to a real environment, successful and robust results can be obtained by continuing the evolutionary process in the real environment for a few generations.

Evolving mobile robots in simulated and real environments

The problem of the validity of simulation is particularly relevant for methodologies that use machine learning techniques to develop control systems for autonomous robots, as, for instance, the art...