1,414 research outputs found

    Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

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    This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments

    Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

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    This researchwas funded by projects TecNM-5654.19-P and DemocratAI PID2020-115570GB-C22.In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human users. Moreover, a new technique that uses three different paths to validate the performance of each candidate configuration is presented. We extend on our previous work by adding two more membership functions to the previous fuzzy model, intending to have a finer-grained adjustment. We tuned the controller using several well-known metaheuristic methods, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), GreyWolf Optimizer (GWO), Harmony Search (HS), and the recent Aquila Optimizer (AO) and Arithmetic Optimization Algorithms. Experiments validate that, compared to published results, the proposed fuzzy controllers have better RMSE-measured performance. Nevertheless, experiments also highlight problems with the common practice of evaluating the performance of fuzzy controllers with a single problem case and performance metric, resulting in controllers that tend to be overtrained.TecNM-5654.19-PDemocratAI PID2020-115570GB-C2

    Supervised Control of a Flying Performing Robot using its Intrinsic Sound

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    We present the current results of our ongoing research in achieving efficient control of a flying robot for a wide variety of possible applications. A lightweight small indoor helicopter has been equipped with an embedded system and relatively simple sensors to achieve autonomous stable flight. The controllers have been tuned using genetic algorithms to further enhance flight stability. A number of additional sensors would need to be attached to the helicopter to enable it to sense more of its environment such as its current location or the location of obstacles like the walls of the room it is flying in. The lightweight nature of the helicopter very much restricts the amount of sensors that can be attached to it. We propose utilising the intrinsic sound signatures of the helicopter to locate it and to extract features about its current state, using another supervising robot. The analysis of this information is then sent back to the helicopter using an uplink to enable the helicopter to further stabilise its flight and correct its position and flight path without the need for additional sensors

    Genetic learning of fuzzy reactive controllers

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    P. 33-41This paper concerns the learning of basic behaviors in an autonomous robot. It presents a method to adapt basic reactive behaviors using a genetic algorithm. Behaviors are implemented as fuzzy controllers and the genetic algorithm is used to evolve their rules. These rules will be formulated in a fuzzy way using prefixed linguistic labels. In order to test the rules obtained in each generation of the genetic evolution process, a real robot has been used. Numerical results from the evolution rate of the different experiments, as well as an example of the fuzzy rules obtained, are presented and discussedS

    Intelligent approaches in locomotion - a review

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    A Novel Self-Organizing PID Approach for Controlling Mobile Robot Locomotion

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    A novel self-organizing fuzzy proportional-integral-derivative (SOF-PID) control system is proposed in this paper. The proposed system consists of a pair of control and reference models, both of which are implemented by a first-order autonomous learning multiple model (ALMMo) neuro-fuzzy system. The SOF-PID controller self-organizes and self-updates the structures and meta-parameters of both the control and reference models during the control process "on the fly". This gives the SOF-PID control system the capability of quickly adapting to entirely new operating environments without a full re-training. Moreover, the SOF-PID control system is free from user- and problem-specific parameters and is entirely data-driven. Simulations and real-world experiments with mobile robots demonstrate the effectiveness and validity of the proposed SOF-PID control system

    Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots

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    This paper proposes two applications of Grey Wolf Optimizer (GWO) algorithms to a path planning (PaPl) problem and a Proportional-Integral (PI)-fuzzy controller tuning problem. Both optimization problems solved by GWO algorithms are explained in detail. An off-line GWO-based PaPl approach for Nonholonomic Wheeled Mobile Robots (NWMRs) in static environments is proposed. Once the PaPl problem is solved resulting in the reference trajectory of the robots, the paper also suggests a GWO-based approach to tune cost-effective PI-fuzzy controllers in tracking control problem for NWMRs. The experimental results are demonstrated through simple multiagent settings conducted on the nRobotic platform developed at the Politehnica University of Timisoara, Romania, and they prove both the effectiveness of the two GWO-based approaches and major performance improvement

    Evolution of Robotic Behaviour Using Gene Expression Programming

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    The main objective in automatic robot controller development is to devise mechanisms whereby robot controllers can be developed with less reliance on human developers. One such mechanism is the use of evolutionary algorithms (EAs) to automatically develop robot controllers and occasionally, robot morphology. This area of research is referred to as evolutionary robotics (ER). Through the use of evolutionary techniques such as genetic algorithms (GAs) and genetic programming (GP), ER has shown to be a promising approach through which robust robot controllers can be developed. The standard ER techniques use monolithic evolution to evolve robot behaviour: monolithic evolution involves the use of one chromosome to code for an entire target behaviour. In complex problems, monolithic evolution has been shown to suffer from bootstrap problems; that is, a lack of improvement in fitness due to randomness in the solution set [103, 105, 100, 90]. Thus, approaches to dividing the tasks, such that the main behaviours emerge from the interaction of these simple tasks with the robot environment have been devised. These techniques include the subsumption architecture in behaviour based robotics, incremental learning and more recently the layered learning approach [55, 103, 56, 105, 136, 95]. These new techniques enable ER to develop complex controllers for autonomous robot. Work presented in this thesis extends the field of evolutionary robotics by introducing Gene Expression Programming (GEP) to the ER field. GEP is a newly developed evolutionary algorithm akin to GA and GP, which has shown great promise in optimisation problems. The presented research shows through experimentation that the unique formulation of GEP genes is sufficient for robot controller representation and development. The obtained results show that GEP is a plausible technique for ER problems. Additionally, it is shown that controllers evolved using GEP algorithm are able to adapt when introduced to new environments. Further, the capabilities of GEP chromosomes to code for more than one gene have been utilised to show that GEP can be used to evolve manually sub-divided robot behaviours. Additionally, this thesis extends the GEP algorithm by proposing two new evolutionary techniques named multigenic GEP with Linker Evolution (mgGEP-LE) and multigenic GEP with a Regulator Gene (mgGEP-RG). The results obtained from the proposed algorithms show that the new techniques can be used to automatically evolve modularity in robot behaviour. This ability to automate the process of behaviour sub-division and optimisation in a modular chromosome is unique to the GEP formulations discussed, and is an important advance in the development of machines that are able to evolve stratified behavioural architectures with little human intervention

    Aneka : A control software framework for autonomous robotic systems

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