Pub Date : 1987-06-01DOI: 10.1109/JRA.1987.1087092
J. Reif, J. Storer
The problem of movement in two-dimensional Euclidean space that is bounded by a (not necessarily convex) polygon is considered. Movement is restricted to be along straight line segments, and the objective is to minimize the number of bends or "turns" in a path. Most past work on this problem has addressed the movement between a source point and a destination point. An O(n ast log (n)) time algorithm is presented for computing a data structure that represents the minimal-turn paths from a source point to all other points in the polygon. An advantage of this algorithm is that it uses relatively simple data structures and is practical to implement. Another advantage is that it is easily generalized to accommodate the movement of a disk of radius r > 0.
{"title":"Minimizing turns for discrete movement in the interior of a polygon","authors":"J. Reif, J. Storer","doi":"10.1109/JRA.1987.1087092","DOIUrl":"https://doi.org/10.1109/JRA.1987.1087092","url":null,"abstract":"The problem of movement in two-dimensional Euclidean space that is bounded by a (not necessarily convex) polygon is considered. Movement is restricted to be along straight line segments, and the objective is to minimize the number of bends or \"turns\" in a path. Most past work on this problem has addressed the movement between a source point and a destination point. An O(n ast log (n)) time algorithm is presented for computing a data structure that represents the minimal-turn paths from a source point to all other points in the polygon. An advantage of this algorithm is that it uses relatively simple data structures and is practical to implement. Another advantage is that it is easily generalized to accommodate the movement of a disk of radius r > 0.","PeriodicalId":370047,"journal":{"name":"IEEE J. Robotics Autom.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127254620","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}
Pub Date : 1987-06-01DOI: 10.1109/JRA.1987.1087091
S. Harmon
The Ground Surveillance Robot (GSR) project has proceeded continuously since the Fall of 1980, and in that time an autonomous vehicle design and some degree of implementation has been achieved. The vehicle design has been partitioned into sensor, control, and planning subsystems. A distributed blackboard scheme has been developed which provides the mechanism by which these subsystems are coordinated. Vehicle position and orientation are supplied by vehicle attitude and navigation sensor subsystems. Obstacle avoidance capability has been implemented by fusing information from vision and acoustic ranging sensors into local goals and avoidance points. The influence of these points is combined through potential field techniques to accomplish obstacle avoidance control. Distant terrain characteristics are identified using information from a gray-level vision system, a color vision system, and a computer-controlled laser ranging sensor. These characteristics are used by a general planning engine to develop the desired path to a visible goal in the direction of the final goal. Progress to the final goal consists of a succession of movements from one distant but visible intermediate goal to another. The experience from implementing this autonomous vehicle has indicated the need for an integrated set of debugging tools which make the faults in subsystem hardware and software more distinguishable.
{"title":"The ground surveillance robot (GSR): An autonomous vehicle designed to transit unknown terrain","authors":"S. Harmon","doi":"10.1109/JRA.1987.1087091","DOIUrl":"https://doi.org/10.1109/JRA.1987.1087091","url":null,"abstract":"The Ground Surveillance Robot (GSR) project has proceeded continuously since the Fall of 1980, and in that time an autonomous vehicle design and some degree of implementation has been achieved. The vehicle design has been partitioned into sensor, control, and planning subsystems. A distributed blackboard scheme has been developed which provides the mechanism by which these subsystems are coordinated. Vehicle position and orientation are supplied by vehicle attitude and navigation sensor subsystems. Obstacle avoidance capability has been implemented by fusing information from vision and acoustic ranging sensors into local goals and avoidance points. The influence of these points is combined through potential field techniques to accomplish obstacle avoidance control. Distant terrain characteristics are identified using information from a gray-level vision system, a color vision system, and a computer-controlled laser ranging sensor. These characteristics are used by a general planning engine to develop the desired path to a visible goal in the direction of the final goal. Progress to the final goal consists of a succession of movements from one distant but visible intermediate goal to another. The experience from implementing this autonomous vehicle has indicated the need for an integrated set of debugging tools which make the faults in subsystem hardware and software more distinguishable.","PeriodicalId":370047,"journal":{"name":"IEEE J. Robotics Autom.","volume":"18 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114007492","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}
Pub Date : 1987-06-01DOI: 10.1109/JRA.1987.1087096
A. Elfes
A sonar-based mapping and navigation system developed for an autonomous mobile robot operating in unknown and unstructured environments is described. The system uses sonar range data to build a multileveled description of the robot's surroundings. Sonar readings are interpreted using probability profiles to determine empty and occupied areas. Range measurements from multiple points of view are integrated into a sensor-level sonar map, using a robust method that combines the sensor information in such a way as to cope with uncertainties and errors in the data. The resulting two-dimensional maps are used for path planning and navigation. From these sonar maps, multiple representations are developed for various kinds of problem-solving activities. Several dimensions of representation are defined: the abstraction axis, the geographical axis, and the resolution axis. The sonar mapping procedures have been implemented as part of an autonomous mobile robot navigation system called Dolphin. The major modules of this system are described and related to the various mapping representations used. Results from actual runs are presented, and further research is mentioned. The system is also situated within the wider context of developing an advanced software architecture for autonomous mobile robots.
{"title":"Sonar-based real-world mapping and navigation","authors":"A. Elfes","doi":"10.1109/JRA.1987.1087096","DOIUrl":"https://doi.org/10.1109/JRA.1987.1087096","url":null,"abstract":"A sonar-based mapping and navigation system developed for an autonomous mobile robot operating in unknown and unstructured environments is described. The system uses sonar range data to build a multileveled description of the robot's surroundings. Sonar readings are interpreted using probability profiles to determine empty and occupied areas. Range measurements from multiple points of view are integrated into a sensor-level sonar map, using a robust method that combines the sensor information in such a way as to cope with uncertainties and errors in the data. The resulting two-dimensional maps are used for path planning and navigation. From these sonar maps, multiple representations are developed for various kinds of problem-solving activities. Several dimensions of representation are defined: the abstraction axis, the geographical axis, and the resolution axis. The sonar mapping procedures have been implemented as part of an autonomous mobile robot navigation system called Dolphin. The major modules of this system are described and related to the various mapping representations used. Results from actual runs are presented, and further research is mentioned. The system is also situated within the wider context of developing an advanced software architecture for autonomous mobile robots.","PeriodicalId":370047,"journal":{"name":"IEEE J. Robotics Autom.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129294739","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}
Pub Date : 1987-06-01DOI: 10.1109/JRA.1987.1087099
C. Hoffmann, J. Hopcroft
The design of an extensible system is discussed in which the behavior of physical objects is simulated from their models. Complex objects can be defined in a multiplicity of domains, including their geometric shape, their dynamic response to applied forces, and their controlled behavior. In response to unforeseen changes, e.g., for unexpected collisions, the object models are modified automatically during the simulation.
{"title":"Simulation of physical systems from geometric models","authors":"C. Hoffmann, J. Hopcroft","doi":"10.1109/JRA.1987.1087099","DOIUrl":"https://doi.org/10.1109/JRA.1987.1087099","url":null,"abstract":"The design of an extensible system is discussed in which the behavior of physical objects is simulated from their models. Complex objects can be defined in a multiplicity of domains, including their geometric shape, their dynamic response to applied forces, and their controlled behavior. In response to unforeseen changes, e.g., for unexpected collisions, the object models are modified automatically during the simulation.","PeriodicalId":370047,"journal":{"name":"IEEE J. Robotics Autom.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131289472","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}
Pub Date : 1987-04-01DOI: 10.1109/JRA.1987.1087087
A. Bejczy, R. P. Paul
{"title":"A survey and the future","authors":"A. Bejczy, R. P. Paul","doi":"10.1109/JRA.1987.1087087","DOIUrl":"https://doi.org/10.1109/JRA.1987.1087087","url":null,"abstract":"","PeriodicalId":370047,"journal":{"name":"IEEE J. Robotics Autom.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120944072","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}
Pub Date : 1987-04-01DOI: 10.1109/JRA.1987.1087081
W. Miller
A practical learning control system is described which is applicable to the control of complex robotic systems involving multiple feedback sensors and multiple command variables during both repetitive and nonrepetitive operations. In the controller, a general learning algorithm is used to learn to reproduce the relationship between the sensor outputs and the system command variables over particular regions of the system state space. The learned information is then used to predict the command signals required to produce desired changes in the sensor outputs. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables, facilitating control system modification for specific applications. The results of two learning experiments using a General Electric P-5 manipulator are presented. The first involved learning to use the video image feedback to position the robot hand accurately relative to stationary objects on a table, assuming no knowledge of the robot kinematics or camera characteristics. The second involved learning to use video image feedback to intercept and track objects moving on a conveyor. In both experiments, control system performance was found to be limited by the resolution of the sensor feedback data, rather than by control structure limitations.
{"title":"Sensor-based control of robotic manipulators using a general learning algorithm","authors":"W. Miller","doi":"10.1109/JRA.1987.1087081","DOIUrl":"https://doi.org/10.1109/JRA.1987.1087081","url":null,"abstract":"A practical learning control system is described which is applicable to the control of complex robotic systems involving multiple feedback sensors and multiple command variables during both repetitive and nonrepetitive operations. In the controller, a general learning algorithm is used to learn to reproduce the relationship between the sensor outputs and the system command variables over particular regions of the system state space. The learned information is then used to predict the command signals required to produce desired changes in the sensor outputs. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables, facilitating control system modification for specific applications. The results of two learning experiments using a General Electric P-5 manipulator are presented. The first involved learning to use the video image feedback to position the robot hand accurately relative to stationary objects on a table, assuming no knowledge of the robot kinematics or camera characteristics. The second involved learning to use video image feedback to intercept and track objects moving on a conveyor. In both experiments, control system performance was found to be limited by the resolution of the sensor feedback data, rather than by control structure limitations.","PeriodicalId":370047,"journal":{"name":"IEEE J. Robotics Autom.","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126553225","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}
Pub Date : 1987-04-01DOI: 10.1109/JRA.1987.1087089
A. Waxman, J. L. Moigne, L. Davis, B. Srinivasan, T. Kushner, Eli Liang, T. Siddalingaiah
A modular system architecture has been developed to support visual navigation by an autonomous land vehicle. The system consists of vision modules performing image processing, three-dimensional shape recovery, and geometric reasoning, as well as modules for planning, navigating, and piloting. The system runs in two distinct modes, bootstrap and feedforward. The bootstrap mode requires analysis of entire images to find and model the objects of interest in the scene (e.g., roads). In the feedforward mode (while the vehicle is moving), attention is focused on small parts of the visual field as determined by prior views of the scene, to continue to track and model the objects of interest. General navigational tasks are decomposed into three categories, all of which contribute to planning a vehicle path. They are called long-, intermediate-, and short-range navigation, reflecting the scale to which they apply. The system has been implemented as a set of concurrent communicating modules and used to drive a camera (carried by a robot arm) over a scale model road network on a terrain board. A large subset of the system has been reimplemented on a VICOM image processor and has driven the DARPA Autonomous Land Vehicle (ALV) at Martin Marietta's test site in Denver, CO.
{"title":"A visual navigation system for autonomous land vehicles","authors":"A. Waxman, J. L. Moigne, L. Davis, B. Srinivasan, T. Kushner, Eli Liang, T. Siddalingaiah","doi":"10.1109/JRA.1987.1087089","DOIUrl":"https://doi.org/10.1109/JRA.1987.1087089","url":null,"abstract":"A modular system architecture has been developed to support visual navigation by an autonomous land vehicle. The system consists of vision modules performing image processing, three-dimensional shape recovery, and geometric reasoning, as well as modules for planning, navigating, and piloting. The system runs in two distinct modes, bootstrap and feedforward. The bootstrap mode requires analysis of entire images to find and model the objects of interest in the scene (e.g., roads). In the feedforward mode (while the vehicle is moving), attention is focused on small parts of the visual field as determined by prior views of the scene, to continue to track and model the objects of interest. General navigational tasks are decomposed into three categories, all of which contribute to planning a vehicle path. They are called long-, intermediate-, and short-range navigation, reflecting the scale to which they apply. The system has been implemented as a set of concurrent communicating modules and used to drive a camera (carried by a robot arm) over a scale model road network on a terrain board. A large subset of the system has been reimplemented on a VICOM image processor and has driven the DARPA Autonomous Land Vehicle (ALV) at Martin Marietta's test site in Denver, CO.","PeriodicalId":370047,"journal":{"name":"IEEE J. Robotics Autom.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122281142","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}
Pub Date : 1987-04-01DOI: 10.1109/JRA.1987.1087082
F. Azadivar
To position and orient the hand of an industrial robot to perform a particular manufacturing process, the joints are commanded to assume certain angles and/or displacements. However, due to position errors at joints, the assumed positions are almost always different from those commanded. These deviations induce a random error to the position and orientation of the hand. The ability of the hand to perform according to the required accuracy depends, among other things, on the extent of joint position errors. The effect of these errors on the accuracy of the operations are studied using a stochastic model. In addition, a procedure is suggested for determining the optimum position error to aim at in a given manufacturing situation. The results are applied to a T3type robot performing an assembly operation.
{"title":"The effect of joint position errors of industrial robots on their performance in manufacturing operations","authors":"F. Azadivar","doi":"10.1109/JRA.1987.1087082","DOIUrl":"https://doi.org/10.1109/JRA.1987.1087082","url":null,"abstract":"To position and orient the hand of an industrial robot to perform a particular manufacturing process, the joints are commanded to assume certain angles and/or displacements. However, due to position errors at joints, the assumed positions are almost always different from those commanded. These deviations induce a random error to the position and orientation of the hand. The ability of the hand to perform according to the required accuracy depends, among other things, on the extent of joint position errors. The effect of these errors on the accuracy of the operations are studied using a stochastic model. In addition, a procedure is suggested for determining the optimum position error to aim at in a given manufacturing situation. The results are applied to a T3type robot performing an assembly operation.","PeriodicalId":370047,"journal":{"name":"IEEE J. Robotics Autom.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115884859","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}
Pub Date : 1987-04-01DOI: 10.1109/JRA.1987.1087086
F. Merat
{"title":"Introduction to robotics: Mechanics and control","authors":"F. Merat","doi":"10.1109/JRA.1987.1087086","DOIUrl":"https://doi.org/10.1109/JRA.1987.1087086","url":null,"abstract":"","PeriodicalId":370047,"journal":{"name":"IEEE J. Robotics Autom.","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133593572","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}
Pub Date : 1987-04-01DOI: 10.1109/JRA.1987.1087080
Sanjiv Singh, M. D. Wagh
An automated path planning algorithm for a mobile robot in a structured environment is presented. An algorithm based on the Quine-McCluskey method of finding prime implicants in a logical expression is used to isolate all the largest rectangular free convex areas in a specified environment. The free convex areas are represented as nodes in a graph, and a graph traversal strategy that dynamically allocates costs to graph paths is used. Complexity of the algorithm and a strategy to trade optimality for smaller computation time are discussed.
{"title":"Robot path planning using intersecting convex shapes: Analysis and simulation","authors":"Sanjiv Singh, M. D. Wagh","doi":"10.1109/JRA.1987.1087080","DOIUrl":"https://doi.org/10.1109/JRA.1987.1087080","url":null,"abstract":"An automated path planning algorithm for a mobile robot in a structured environment is presented. An algorithm based on the Quine-McCluskey method of finding prime implicants in a logical expression is used to isolate all the largest rectangular free convex areas in a specified environment. The free convex areas are represented as nodes in a graph, and a graph traversal strategy that dynamically allocates costs to graph paths is used. Complexity of the algorithm and a strategy to trade optimality for smaller computation time are discussed.","PeriodicalId":370047,"journal":{"name":"IEEE J. Robotics Autom.","volume":"466 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132537919","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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