R. Meganathan, Aarthi Alagammai Kasi, Sujatha Jagannath
A key requirement in the development of intelligent and driverless vehicles is steering angle computation for efficient navigation. This paper presents a novel method for computing steering angle for driverless vehicles using computer vision based techniques of relatively lower computing cost. The proposed system consists of three major stages. The first stage includes Dynamic Road region extraction using Gaussian Mixture Model and Expectation Maximization algorithm. The second stage is to compute the steering angle based on the extracted roadregion. In addition, Kalman filtering technique is used to cancel spurious angle transition noises. The proposed algorithm was tested both on a simulator and real-time images and was found to give a good estimation of actual steering angle required for navigation. Further, it was also observed that this works in different lighting conditions as well as for both structured and unstructured road scenarios.
{"title":"Computer Vision Based Novel Steering Angle Calculation for Autonomous Vehicles","authors":"R. Meganathan, Aarthi Alagammai Kasi, Sujatha Jagannath","doi":"10.1109/IRC.2018.00029","DOIUrl":"https://doi.org/10.1109/IRC.2018.00029","url":null,"abstract":"A key requirement in the development of intelligent and driverless vehicles is steering angle computation for efficient navigation. This paper presents a novel method for computing steering angle for driverless vehicles using computer vision based techniques of relatively lower computing cost. The proposed system consists of three major stages. The first stage includes Dynamic Road region extraction using Gaussian Mixture Model and Expectation Maximization algorithm. The second stage is to compute the steering angle based on the extracted roadregion. In addition, Kalman filtering technique is used to cancel spurious angle transition noises. The proposed algorithm was tested both on a simulator and real-time images and was found to give a good estimation of actual steering angle required for navigation. Further, it was also observed that this works in different lighting conditions as well as for both structured and unstructured road scenarios.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125947091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tauhidul Alam, G. Reis, Leonardo Bobadilla, Ryan N. Smith
Processes of scientific interest in the aquatic environment occur across multiple spatio-temporal time scales. To properly assess and understand these processes, we must observe aquatic ecosystems over long time periods. This requires examination of the problem of deploying multiple, inexpensive, and minimally-actuated drifting vehicles. We aim to utilize these persistent assets to explore all locations on the water surface, and examine the entirety an underwater environment through the visibility of downward-facing cameras. In this work, we propose a data-driven approach for the deployment of drifters that creates a stochastic model, finds the generalized flow pattern of the water, and studies the long-term behavior of an aquatic environment from a flow point-of-view. Given the long-term behavior of the environment, our approach finds attractors and their transient groups as the domains of attractions. We then determine a minimum number of deployment locations for the drifters using these attractors and their transient groups. Our simulation results based on actual ocean model prediction data demonstrate the applicability of our approach.
{"title":"A Data-Driven Deployment Approach for Persistent Monitoring in Aquatic Environments","authors":"Tauhidul Alam, G. Reis, Leonardo Bobadilla, Ryan N. Smith","doi":"10.1109/IRC.2018.00030","DOIUrl":"https://doi.org/10.1109/IRC.2018.00030","url":null,"abstract":"Processes of scientific interest in the aquatic environment occur across multiple spatio-temporal time scales. To properly assess and understand these processes, we must observe aquatic ecosystems over long time periods. This requires examination of the problem of deploying multiple, inexpensive, and minimally-actuated drifting vehicles. We aim to utilize these persistent assets to explore all locations on the water surface, and examine the entirety an underwater environment through the visibility of downward-facing cameras. In this work, we propose a data-driven approach for the deployment of drifters that creates a stochastic model, finds the generalized flow pattern of the water, and studies the long-term behavior of an aquatic environment from a flow point-of-view. Given the long-term behavior of the environment, our approach finds attractors and their transient groups as the domains of attractions. We then determine a minimum number of deployment locations for the drifters using these attractors and their transient groups. Our simulation results based on actual ocean model prediction data demonstrate the applicability of our approach.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132309969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Vijayendra, Saumya Kumaar Saksena, Ravi M. Vishwanath, S. Omkar
Brain-computer interface (BCI), an actively re-searched multi-disciplinary domain, has completely trans-formed the approach to robotic control problems. Researchers have focused on developing algorithms that optimize robotic movement to achieve desired trajectories, and it's a general understanding that route optimization problems are difficult to solve mathematically. Humans, on the other hand, tend to optimize their day-to-day activities intuitively. In order to achieve the desired results, the brain exploits a multi-level filtering approach, where the macro features are weighted in the first layer and the microfeatures in further layers. This optimization inside the brain interestingly, leave distinct traces in electroencephalography (EEG) plots. Based on the observations, we propose to use artificial neural networks to classify the EEG data, which intuitively should give a high classification rate, because the human brain also exploits a network of neurons to classify auditory (time-series) and visual (spatial) data. In this paper, we discuss the performances of 14- channel and 5-channel EEG headsets for robotic applications. Data is acquired from 20 subjects corresponding to four different tasks. Using neural nets, we have been successfully able to classify the EEG input into four different classes. We get an overall classification accuracy of 98.8% for 14-channel and 84.5% 5-channel system. As a real-time demonstration of the interface, the predicted class number is sent to a multi-rotor via a wireless link as an appropriate velocity command.
{"title":"A Performance Study of 14-Channel and 5-Channel EEG Systems for Real-Time Control of Unmanned Aerial Vehicles (UAVs)","authors":"A. Vijayendra, Saumya Kumaar Saksena, Ravi M. Vishwanath, S. Omkar","doi":"10.1109/IRC.2018.00040","DOIUrl":"https://doi.org/10.1109/IRC.2018.00040","url":null,"abstract":"Brain-computer interface (BCI), an actively re-searched multi-disciplinary domain, has completely trans-formed the approach to robotic control problems. Researchers have focused on developing algorithms that optimize robotic movement to achieve desired trajectories, and it's a general understanding that route optimization problems are difficult to solve mathematically. Humans, on the other hand, tend to optimize their day-to-day activities intuitively. In order to achieve the desired results, the brain exploits a multi-level filtering approach, where the macro features are weighted in the first layer and the microfeatures in further layers. This optimization inside the brain interestingly, leave distinct traces in electroencephalography (EEG) plots. Based on the observations, we propose to use artificial neural networks to classify the EEG data, which intuitively should give a high classification rate, because the human brain also exploits a network of neurons to classify auditory (time-series) and visual (spatial) data. In this paper, we discuss the performances of 14- channel and 5-channel EEG headsets for robotic applications. Data is acquired from 20 subjects corresponding to four different tasks. Using neural nets, we have been successfully able to classify the EEG input into four different classes. We get an overall classification accuracy of 98.8% for 14-channel and 84.5% 5-channel system. As a real-time demonstration of the interface, the predicted class number is sent to a multi-rotor via a wireless link as an appropriate velocity command.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115064728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent developments in computing, sensing, and control have enabled the development of complex robot autonomy systems. Most such stacks involve time-critical tasks such as aerial vehicle control/collision avoidance and several noncritical tasks such as sensor processing and estimation. The vast majority of this software is written in low-level languages such as C/C++ with no formal platform support for expression of timeliness requirements requiring the developer hand-tunes algorithms and manually verify their effectiveness. However, other embedded real-time (RT) systems have successfully leveraged higher level languages such as Java (and its RT variants) for timeliness specification. Such platforms allow developers to specify their requirements and the platform ensures they are met, allowing the developer to focus on algorithm development rather then how they may affect critical system timeliness. In this work, we port a popular Unmanned Aerial Vehicle (UAV) autopilot called Paparazzi UAV to Java and the Real-Time Specification for Java (RTSJ). In simulation, we demonstrate that by leveraging a RT Java Virtual Machine (JVM), which uses Real-Time Operating System (RTOS) scheduling, predictable timeliness can be achieved when compared to a standard JVM.
{"title":"jUAV: A Real-Time Java UAV Autopilot","authors":"Adam Czerniejewski, Karthik Dantu, Lukasz Ziarek","doi":"10.1109/IRC.2018.00054","DOIUrl":"https://doi.org/10.1109/IRC.2018.00054","url":null,"abstract":"Recent developments in computing, sensing, and control have enabled the development of complex robot autonomy systems. Most such stacks involve time-critical tasks such as aerial vehicle control/collision avoidance and several noncritical tasks such as sensor processing and estimation. The vast majority of this software is written in low-level languages such as C/C++ with no formal platform support for expression of timeliness requirements requiring the developer hand-tunes algorithms and manually verify their effectiveness. However, other embedded real-time (RT) systems have successfully leveraged higher level languages such as Java (and its RT variants) for timeliness specification. Such platforms allow developers to specify their requirements and the platform ensures they are met, allowing the developer to focus on algorithm development rather then how they may affect critical system timeliness. In this work, we port a popular Unmanned Aerial Vehicle (UAV) autopilot called Paparazzi UAV to Java and the Real-Time Specification for Java (RTSJ). In simulation, we demonstrate that by leveraging a RT Java Virtual Machine (JVM), which uses Real-Time Operating System (RTOS) scheduling, predictable timeliness can be achieved when compared to a standard JVM.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114039569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. D’Auria, Fabio Persia, Fabio Bettini, S. Helmer, B. Siciliano
The aim of our work is to integrate a previously developed framework for high-level surveillance event detection with a smart robot to enhance the monitoring process. More specifically, the robot is able to improve the quality of a video stream, which in our earlier work was recorded from a static camera position, by providing a mobile camera, enabling us to get footage from different angles. Furthermore, a robot platform is able to go beyond just contacting security or the police by taking immediate action when a potentially dangerous event is detected: for instance, picking up an unattended package.
{"title":"SARRI: A SmArt Rapiro Robot Integrating a Framework for Automatic High-Level Surveillance Event Detection","authors":"D. D’Auria, Fabio Persia, Fabio Bettini, S. Helmer, B. Siciliano","doi":"10.1109/IRC.2018.00050","DOIUrl":"https://doi.org/10.1109/IRC.2018.00050","url":null,"abstract":"The aim of our work is to integrate a previously developed framework for high-level surveillance event detection with a smart robot to enhance the monitoring process. More specifically, the robot is able to improve the quality of a video stream, which in our earlier work was recorded from a static camera position, by providing a mobile camera, enabling us to get footage from different angles. Furthermore, a robot platform is able to go beyond just contacting security or the police by taking immediate action when a potentially dangerous event is detected: for instance, picking up an unattended package.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128856842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cooperation is often considered as one of the key and unclear concepts, which differentiates multi-agent systems from other related fields such as distributed computing. One of the popular benchmarks for the verification of the effectiveness of various cooperation algorithms is multi-agent foraging task. Different approaches have been proposed among which Markov game based ones are widely used, though they could not select consistent equilibrium for the group. In this paper, an evolutionary game based method is proposed. In this method, the interactions among the agents are modeled by snow-drift game to evolve the evolutionary stable strategy (ESS) and bring the maximal reward for the group of agents. The simulation verified the efficiency of the proposed algorithm.
{"title":"Multi-agent Cooperation Using Snow-Drift Evolutionary Game Model: Case Study in Foraging Task","authors":"Ahmad Esmaeili, Zahra Ghorrati, E. Matson","doi":"10.1109/IRC.2018.00065","DOIUrl":"https://doi.org/10.1109/IRC.2018.00065","url":null,"abstract":"Cooperation is often considered as one of the key and unclear concepts, which differentiates multi-agent systems from other related fields such as distributed computing. One of the popular benchmarks for the verification of the effectiveness of various cooperation algorithms is multi-agent foraging task. Different approaches have been proposed among which Markov game based ones are widely used, though they could not select consistent equilibrium for the group. In this paper, an evolutionary game based method is proposed. In this method, the interactions among the agents are modeled by snow-drift game to evolve the evolutionary stable strategy (ESS) and bring the maximal reward for the group of agents. The simulation verified the efficiency of the proposed algorithm.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130880068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Donghyun Lim, HeonGyeom Kim, SangGi Hong, Sanghee Lee, GaYoung Kim, Austin Snail, Lucy Gotwals, John C. Gallagher
In this work, we analyze the effectiveness of a simple neural network for the task of determining, by sound, if small unmanned vehicles are carrying potentially harmful payloads The goal of this work is to contribute to a real-time UAV detection system that requires a means of assessing threat level of incoming vehicles whose positions are determined by other sensors. Further, we operated under a minimal cost constraints to enable eventual adoption at scale by law enforcement agencies. Our system classifies payload carrying vs. non-payload carrying DJI Phantom II UAVs by presenting sound spectrum data to a simple Convolutional Neural Networks (CNN). These networks, along with a simple voting system, provided a 99.92% recognition rate for this problem without a need to violate our minimal cost constraint.
{"title":"Practically Classifying Unmanned Aerial Vehicles Sound Using Convolutional Neural Networks","authors":"Donghyun Lim, HeonGyeom Kim, SangGi Hong, Sanghee Lee, GaYoung Kim, Austin Snail, Lucy Gotwals, John C. Gallagher","doi":"10.1109/IRC.2018.00051","DOIUrl":"https://doi.org/10.1109/IRC.2018.00051","url":null,"abstract":"In this work, we analyze the effectiveness of a simple neural network for the task of determining, by sound, if small unmanned vehicles are carrying potentially harmful payloads The goal of this work is to contribute to a real-time UAV detection system that requires a means of assessing threat level of incoming vehicles whose positions are determined by other sensors. Further, we operated under a minimal cost constraints to enable eventual adoption at scale by law enforcement agencies. Our system classifies payload carrying vs. non-payload carrying DJI Phantom II UAVs by presenting sound spectrum data to a simple Convolutional Neural Networks (CNN). These networks, along with a simple voting system, provided a 99.92% recognition rate for this problem without a need to violate our minimal cost constraint.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127031915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Senthilnath Jayavelu, Harikumar Kandath, S. Sundaram
This paper focuses on increasing the duration of autonomous missions performed by Unmanned Aerial Vehicles (UAVs) by deploying a swarm of Unmanned Ground Vehicles (UGVs) as mobile refueling and maintenance stations. Conventionally UAVs are refueled with the fixed centralized Main Charging Stations (MCS). An algorithm is developed for efficiently distributing the swarm of UGVs to act as mobile refueling stations for UAVs. We have proposed a two-stage density estimation approach. In the first-stage, the optimal number of UGVs and its distribution were computed. In the second-stage, the UGVs coordinates with the nearest UAVs dynamically, while minimizing the average distance for refueling. The performance of the algorithm is compared with the static placement of control station for UAVs to coordinate. The numerical simulation shows a considerable advantage of distributed UGVs over the static placement of control stations.
{"title":"Dynamic Area Coverage for Multi-UAV Using Distributed UGVs: A Two-Stage Density Estimation Approach","authors":"Senthilnath Jayavelu, Harikumar Kandath, S. Sundaram","doi":"10.1109/IRC.2018.00033","DOIUrl":"https://doi.org/10.1109/IRC.2018.00033","url":null,"abstract":"This paper focuses on increasing the duration of autonomous missions performed by Unmanned Aerial Vehicles (UAVs) by deploying a swarm of Unmanned Ground Vehicles (UGVs) as mobile refueling and maintenance stations. Conventionally UAVs are refueled with the fixed centralized Main Charging Stations (MCS). An algorithm is developed for efficiently distributing the swarm of UGVs to act as mobile refueling stations for UAVs. We have proposed a two-stage density estimation approach. In the first-stage, the optimal number of UGVs and its distribution were computed. In the second-stage, the UGVs coordinates with the nearest UAVs dynamically, while minimizing the average distance for refueling. The performance of the algorithm is compared with the static placement of control station for UAVs to coordinate. The numerical simulation shows a considerable advantage of distributed UGVs over the static placement of control stations.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122322205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tasks that change the physical state of a robot take a considerable amount of time to execute. However, many robot applications spend the execution time waiting, although the following tasks might require time to prepare. This paper proposes to amend tasks with a description of their expected outcomes, which allows planning successive tasks based on this information. The suggested approach allows sequential and parallel composition of tasks, as well as reactive behavior modeled as state machines. The paper describes the means of modeling and executing these tasks, details different possibilities of planning in state machine tasks, and evaluates the benefits achievable using the approach.
{"title":"Integrating Reactive Behavior and Planning: Optimizing Execution Time Through Predictive Preparation of State Machine Tasks","authors":"A. Schierl, A. Hoffmann, Ludwig Nägele, W. Reif","doi":"10.1109/IRC.2018.00022","DOIUrl":"https://doi.org/10.1109/IRC.2018.00022","url":null,"abstract":"Tasks that change the physical state of a robot take a considerable amount of time to execute. However, many robot applications spend the execution time waiting, although the following tasks might require time to prepare. This paper proposes to amend tasks with a description of their expected outcomes, which allows planning successive tasks based on this information. The suggested approach allows sequential and parallel composition of tasks, as well as reactive behavior modeled as state machines. The paper describes the means of modeling and executing these tasks, details different possibilities of planning in state machine tasks, and evaluates the benefits achievable using the approach.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128126347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The field of software engineering is seeking ways to close the gaps that exist between the phases of software development, right from requirements specification until testing to deliver reliable software systems. The concepts discussed in this paper intends to reduce that gap using formal methods. This paper introduces new methods and tools that potentially would significantly reduce the time and cost of developing software systems while increasing their reliability. This paper describes a methodology using formal methods to verify software specifications. The use of formal methods during the different phases of a software development life cycle has proven advantages of resulting in a reliable software system. Understanding the importance of using formal methods to specify software systems and then to verify the specifications for correctness forms the first sub problem. Tools and framework needed to formally verify software specifications written for agent systems is also discussed in this paper. This research explores using the model checking methods along with the executable Descartes – Agent specifications to provide a basis for formal verification of agent systems and, generally reliable systems. The Descartes – Agent grammar previously developed was used to implement an algorithm that converts the agent specifications into an intermediate form in linear temporal logic form. Popular model checking tools such as Promela and SPIN were also used to provide a complete framework of verifiable formal specifications.
{"title":"Descartes-Agent: Verifying Formal Specifications Using the Model Checking Technique","authors":"V. Subburaj, J. E. Urban","doi":"10.1109/IRC.2018.00081","DOIUrl":"https://doi.org/10.1109/IRC.2018.00081","url":null,"abstract":"The field of software engineering is seeking ways to close the gaps that exist between the phases of software development, right from requirements specification until testing to deliver reliable software systems. The concepts discussed in this paper intends to reduce that gap using formal methods. This paper introduces new methods and tools that potentially would significantly reduce the time and cost of developing software systems while increasing their reliability. This paper describes a methodology using formal methods to verify software specifications. The use of formal methods during the different phases of a software development life cycle has proven advantages of resulting in a reliable software system. Understanding the importance of using formal methods to specify software systems and then to verify the specifications for correctness forms the first sub problem. Tools and framework needed to formally verify software specifications written for agent systems is also discussed in this paper. This research explores using the model checking methods along with the executable Descartes – Agent specifications to provide a basis for formal verification of agent systems and, generally reliable systems. The Descartes – Agent grammar previously developed was used to implement an algorithm that converts the agent specifications into an intermediate form in linear temporal logic form. Popular model checking tools such as Promela and SPIN were also used to provide a complete framework of verifiable formal specifications.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134421909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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