Synthesis of robots may be decomposed into two processes: {em structural synthesis} (determine the general arrangement of the mechanical structure such as the type and number of joints and the way they will be connected) and {em dimensional synthesis} (determine the length of the links, the axis and location of the joints, the necessary maximal joint forces/torques,$ldots$). The performances that may be obtained for a robot are drastically dependent on both synthesis. Although for serial robots general trends may be derived only from the structure a realistic comparison between two different structures may only be made after a careful dimensional synthesis and this is even more so for closed-loop robot (such as parallel robots). We will present a dimensional synthesis approach based on the design requirements that allows one to obtain almost all feasible design solutions that are guaranteed to satisfy the requirements, even taking into account manufacturing tolerances. Practical examples of 6-DOF robot design will be presented.
机器人的综合可以分解为两个过程:{em结构综合}(确定机械结构的总体布置，如关节的类型和数量及其连接方式)和{em尺寸综合}(确定连杆的长度，关节的轴和位置，所需的最大关节力/扭矩，$ em dots$)。机器人可能获得的性能在很大程度上取决于这两种合成。虽然对于串联机器人的总体趋势可能只从结构中得出，但两种不同结构之间的现实比较可能只有在仔细的尺寸综合之后才能进行，对于闭环机器人(如并联机器人)更是如此。我们将提出一种基于设计要求的尺寸综合方法，即使考虑到制造公差，也可以获得几乎所有保证满足要求的可行设计解决方案。介绍了六自由度机器人的设计实例。
{"title":"Optimal Design of Robots","authors":"J. Merlet","doi":"10.15607/RSS.2005.I.041","DOIUrl":"https://doi.org/10.15607/RSS.2005.I.041","url":null,"abstract":"Synthesis of robots may be decomposed into two processes: {em structural synthesis} (determine the general arrangement of the mechanical structure such as the type and number of joints and the way they will be connected) and {em dimensional synthesis} (determine the length of the links, the axis and location of the joints, the necessary maximal joint forces/torques,$ldots$). The performances that may be obtained for a robot are drastically dependent on both synthesis. Although for serial robots general trends may be derived only from the structure a realistic comparison between two different structures may only be made after a careful dimensional synthesis and this is even more so for closed-loop robot (such as parallel robots). We will present a dimensional synthesis approach based on the design requirements that allows one to obtain almost all feasible design solutions that are guaranteed to satisfy the requirements, even taking into account manufacturing tolerances. Practical examples of 6-DOF robot design will be presented.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"2 1","pages":"311-318"},"PeriodicalIF":0.0,"publicationDate":"2005-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88973863","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}
Autonomous navigation in natural environment requires three-dimensional (3-D) scene representation and interpretation. High density laser-based sensing is commonly used to capture the geometry of the scene, producing large amount of 3-D points with variable spatial density. We proposed a terrain classification method using such data. The approach relies on the computation of local features in 3-D using a support volume and belongs, as such, to a larger class of computational problems where range searches are necessary. This operation on traditional data structure is very expensive and, in this paper, we present an approach to address this issue. The method relies on reusing already computed data as the terrain classification process progresses over the environment representation. We present results that show significant speed improvement using ladar data collected in various environments with a ground mobile robot.
{"title":"Data Structure for Efficient Processing in 3-D","authors":"Jean-François Lalonde, N. Vandapel, M. Hebert","doi":"10.15607/RSS.2005.I.048","DOIUrl":"https://doi.org/10.15607/RSS.2005.I.048","url":null,"abstract":"Autonomous navigation in natural environment requires three-dimensional (3-D) scene representation and interpretation. High density laser-based sensing is commonly used to capture the geometry of the scene, producing large amount of 3-D points with variable spatial density. We proposed a terrain classification method using such data. The approach relies on the computation of local features in 3-D using a support volume and belongs, as such, to a larger class of computational problems where range searches are necessary. This operation on traditional data structure is very expensive and, in this paper, we present an approach to address this issue. The method relies on reusing already computed data as the terrain classification process progresses over the environment representation. We present results that show significant speed improvement using ladar data collected in various environments with a ground mobile robot.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"1 1","pages":"365-372"},"PeriodicalIF":0.0,"publicationDate":"2005-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74273657","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}
We consider coverage problems in robot sensor networks with minimal sensing capabilities. In particular, we demonstrate that a “blind” swarm of robots with no localization and only a weak form of distance estimation can rigorously determine coverage in a bounded planar domain of unknown size and shape. The methods we introduce come from algebraic topology. I. COVERAGE PROBLEMS Many of the potential applications of robot swarms require information about coverage in a given domain. For example, using a swarm of robot sensors for surveillance and security applications carries with it the charge to maximize, or, preferably, guarantee coverage. Such applications include networks of security cameras, mine field sweeping via networked robots [18], and oceanographic sampling [4]. In these contexts, each robot has some coverage domain, and one wishes to know about the union of these coverage domains. Such problems are also crucial in applications not involving robots directly, e.g., communication networks. As a preliminary analysis, we consider the static “field” coverage problem, in which robots are assumed stationary and the goal is to verify blanket coverage of a given domain. There is a large literature on this subject; see, e.g., [7], [1], [16]. In addition, there are variants on these problems involving “barrier” coverage to separate regions. Dynamic or “sweeping” coverage [3] is a common and challenging task with applications ranging from security to vacuuming. Although a sensor network composed of robots will have dynamic capabilities, we restrict attention in this brief paper to the static case in order to lay the groundwork for future inquiry. There are two primary approaches to static coverage problems in the literature. The first uses computational geometry tools applied to exact node coordinates. This typically involves ‘ruler-and-compass’ style geometry [10] or Delaunay triangulations of the domain [16], [14], [20]. Such approaches are very rigid with regards to inputs: one must know exact node coordinates and one must know the geometry of the domain precisely to determine the Delaunay complex. To alleviate the former requirement, many authors have turned to probabilistic tools. For example, in [13], the author assumes a randomly and uniformly distributed collection of nodes in a domain with a fixed geometry and proves expected area coverage. Other approaches [15], [19] give percolationtype results about coverage and network integrity for randomly distributed nodes. The drawback of these methods is the need for strong assumptions about the exact shape of the domain, as well as the need for a uniform distribution of nodes. In the sensor networks community, there is a compelling interest (and corresponding burgeoning literature) in determining properties of a network in which the nodes do not possess coordinate data. One example of a coordinate-free approach is in [17], which gives a heuristic method for geographic routing without coordinate d
{"title":"Blind Swarms for Coverage in 2-D","authors":"V. Silva, R. Ghrist, Abubakr Muhammad","doi":"10.15607/RSS.2005.I.044","DOIUrl":"https://doi.org/10.15607/RSS.2005.I.044","url":null,"abstract":"We consider coverage problems in robot sensor networks with minimal sensing capabilities. In particular, we demonstrate that a “blind” swarm of robots with no localization and only a weak form of distance estimation can rigorously determine coverage in a bounded planar domain of unknown size and shape. The methods we introduce come from algebraic topology. I. COVERAGE PROBLEMS Many of the potential applications of robot swarms require information about coverage in a given domain. For example, using a swarm of robot sensors for surveillance and security applications carries with it the charge to maximize, or, preferably, guarantee coverage. Such applications include networks of security cameras, mine field sweeping via networked robots [18], and oceanographic sampling [4]. In these contexts, each robot has some coverage domain, and one wishes to know about the union of these coverage domains. Such problems are also crucial in applications not involving robots directly, e.g., communication networks. As a preliminary analysis, we consider the static “field” coverage problem, in which robots are assumed stationary and the goal is to verify blanket coverage of a given domain. There is a large literature on this subject; see, e.g., [7], [1], [16]. In addition, there are variants on these problems involving “barrier” coverage to separate regions. Dynamic or “sweeping” coverage [3] is a common and challenging task with applications ranging from security to vacuuming. Although a sensor network composed of robots will have dynamic capabilities, we restrict attention in this brief paper to the static case in order to lay the groundwork for future inquiry. There are two primary approaches to static coverage problems in the literature. The first uses computational geometry tools applied to exact node coordinates. This typically involves ‘ruler-and-compass’ style geometry [10] or Delaunay triangulations of the domain [16], [14], [20]. Such approaches are very rigid with regards to inputs: one must know exact node coordinates and one must know the geometry of the domain precisely to determine the Delaunay complex. To alleviate the former requirement, many authors have turned to probabilistic tools. For example, in [13], the author assumes a randomly and uniformly distributed collection of nodes in a domain with a fixed geometry and proves expected area coverage. Other approaches [15], [19] give percolationtype results about coverage and network integrity for randomly distributed nodes. The drawback of these methods is the need for strong assumptions about the exact shape of the domain, as well as the need for a uniform distribution of nodes. In the sensor networks community, there is a compelling interest (and corresponding burgeoning literature) in determining properties of a network in which the nodes do not possess coordinate data. One example of a coordinate-free approach is in [17], which gives a heuristic method for geographic routing without coordinate d","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"61 1","pages":"335-342"},"PeriodicalIF":0.0,"publicationDate":"2005-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91190749","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}
Steve H. Suhr, Yun Seong Song, Sang Jun Lee, M. Sitti
Recent biological studies on water strider insects revealed the detailed mechanism of their staying and walking on water. While macro scale bodies use buoyancy to stay on water, these very light and small insects balance their weight using repulsive surface tension forces where the insect legs are covered with hydrophobic micro-hairs. Utilizing the unique scaling advantage of these insects, this paper proposes a biologically inspired miniature micro-robot walking on water with a similar principle. The paper focuses on understanding physical characteristics of the insect and designing a robot that mimics the insect's movement. Highly hydrophobic Teflon ® coated wires are used for the legs to take advantage of surface tension force, and the robot body is made of carbon fibers for minimal weight. A T-shaped actuation mechanism with three PZT-5H based unimorph actuators is utilized to move the side legs of the robot independently for controlled locomotion. Kinematics and dynamic properties of the robot prototype are analyzed and compared with the experimental results. The tethered robot can successfully move forward, backward and can also make turns. Maximum speed of the robot in forward motion is 2.3 cm/s. In the future, environmental monitoring applications on dams, lakes, sea, etc. would become possible using a network of these robots with miniature sensors, an on-board power source and electronics.
{"title":"Biologically Inspired Miniature Water Strider Robot","authors":"Steve H. Suhr, Yun Seong Song, Sang Jun Lee, M. Sitti","doi":"10.15607/RSS.2005.I.042","DOIUrl":"https://doi.org/10.15607/RSS.2005.I.042","url":null,"abstract":"Recent biological studies on water strider insects revealed the detailed mechanism of their staying and walking on water. While macro scale bodies use buoyancy to stay on water, these very light and small insects balance their weight using repulsive surface tension forces where the insect legs are covered with hydrophobic micro-hairs. Utilizing the unique scaling advantage of these insects, this paper proposes a biologically inspired miniature micro-robot walking on water with a similar principle. The paper focuses on understanding physical characteristics of the insect and designing a robot that mimics the insect's movement. Highly hydrophobic Teflon ® coated wires are used for the legs to take advantage of surface tension force, and the robot body is made of carbon fibers for minimal weight. A T-shaped actuation mechanism with three PZT-5H based unimorph actuators is utilized to move the side legs of the robot independently for controlled locomotion. Kinematics and dynamic properties of the robot prototype are analyzed and compared with the experimental results. The tethered robot can successfully move forward, backward and can also make turns. Maximum speed of the robot in forward motion is 2.3 cm/s. In the future, environmental monitoring applications on dams, lakes, sea, etc. would become possible using a network of these robots with miniature sensors, an on-board power source and electronics.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"19 1","pages":"319-326"},"PeriodicalIF":0.0,"publicationDate":"2005-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83691929","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}
{"title":"Data driven MCMC for Appearance-based Topological Mapping","authors":"Ananth Ranganathan, F. Dellaert","doi":"10.15607/RSS.2005.I.028","DOIUrl":"https://doi.org/10.15607/RSS.2005.I.028","url":null,"abstract":"Presented at the 2005 Robotics: Science and Systems Conference I (RSS), 8-11 June 2005, Cambridge, MA.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"40 1","pages":"209-216"},"PeriodicalIF":0.0,"publicationDate":"2005-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73605106","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}
Bethany R. Leffler, M. Littman, Alexander L. Strehl, Thomas J. Walsh
When interacting with a new environment, a robot can improve its online performance by efficiently exploring the effects of its actions. The efficiency of exploration can be expanded significantly by modeling and using latent structure to generalize experiences. We provide a theoretical development of the problem of exploration with latent structure, analyze several algorithms and prove matching lower bounds. We demonstrate our algorithmic ideas on a simple robot car repeatedly traversing a path with two different surface properties.
{"title":"Efficient Exploration With Latent Structure","authors":"Bethany R. Leffler, M. Littman, Alexander L. Strehl, Thomas J. Walsh","doi":"10.15607/RSS.2005.I.011","DOIUrl":"https://doi.org/10.15607/RSS.2005.I.011","url":null,"abstract":"When interacting with a new environment, a robot can improve its online performance by efficiently exploring the effects of its actions. The efficiency of exploration can be expanded significantly by modeling and using latent structure to generalize experiences. We provide a theoretical development of the problem of exploration with latent structure, analyze several algorithms and prove matching lower bounds. We demonstrate our algorithmic ideas on a simple robot car repeatedly traversing a path with two different surface properties.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"11 1","pages":"81-88"},"PeriodicalIF":0.0,"publicationDate":"2005-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74595371","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}
Solving the SLAM problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. We investigate smoothing approaches as a viable alternative to extended Kalman filter-based solutions to the problem. In particular, we look at approaches that factorize either the associated information matrix or the measurement matrix into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact, they can be used in either batch or incremental mode, are better equipped to deal with non-linear process and measurement models, and yield the entire robot trajectory, at lower cost. In addition, in an indirect but dramatic way, column ordering heuristics automatically exploit the locality inherent in the geographic nature of the SLAM problem. In this paper we present the theory underlying these methods, an interpretation of factorization in terms of the graphical model associated with the SLAM problem, and simulation results that underscore the potential of these methods for use in practice.
{"title":"Square Root SAM","authors":"Frank Dellaert","doi":"10.15607/RSS.2005.I.024","DOIUrl":"https://doi.org/10.15607/RSS.2005.I.024","url":null,"abstract":"Solving the SLAM problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. We investigate smoothing approaches as a viable alternative to extended Kalman filter-based solutions to the problem. In particular, we look at approaches that factorize either the associated information matrix or the measurement matrix into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact, they can be used in either batch or incremental mode, are better equipped to deal with non-linear process and measurement models, and yield the entire robot trajectory, at lower cost. In addition, in an indirect but dramatic way, column ordering heuristics automatically exploit the locality inherent in the geographic nature of the SLAM problem. In this paper we present the theory underlying these methods, an interpretation of factorization in terms of the graphical model associated with the SLAM problem, and simulation results that underscore the potential of these methods for use in practice.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"18 1","pages":"177-184"},"PeriodicalIF":0.0,"publicationDate":"2005-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82056175","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 majority of current image-based road following algorithms operate, at least in part, by assuming the presence of structural or visual cues unique to the roadway. As a result, these algorithms are poorly suited to the task of tracking unstructured roads typical in desert environments. In this paper, we propose a road following algorithm that operates in a selfsupervised learning regime, allowing it to adapt to changing road conditions while making no assumptions about the general structure or appearance of the road surface. An application of optical flow techniques, paired with one-dimensional template matching, allows identification of regions in the current camera image that closely resemble the learned appearance of the road in the recent past. The algorithm assumes the vehicle lies on the road in order to form templates of the road’s appearance. A dynamic programming variant is then applied to optimize the 1-D template match results while enforcing a constraint on the maximum road curvature expected. Algorithm output images, as well as quantitative results, are presented for three distinct road types encountered in actual driving video acquired in the California Mojave Desert.
{"title":"Adaptive Road Following using Self-Supervised Learning and Reverse Optical Flow","authors":"David Lieb, Andrew Lookingbill, S. Thrun","doi":"10.15607/RSS.2005.I.036","DOIUrl":"https://doi.org/10.15607/RSS.2005.I.036","url":null,"abstract":"The majority of current image-based road following algorithms operate, at least in part, by assuming the presence of structural or visual cues unique to the roadway. As a result, these algorithms are poorly suited to the task of tracking unstructured roads typical in desert environments. In this paper, we propose a road following algorithm that operates in a selfsupervised learning regime, allowing it to adapt to changing road conditions while making no assumptions about the general structure or appearance of the road surface. An application of optical flow techniques, paired with one-dimensional template matching, allows identification of regions in the current camera image that closely resemble the learned appearance of the road in the recent past. The algorithm assumes the vehicle lies on the road in order to form templates of the road’s appearance. A dynamic programming variant is then applied to optimize the 1-D template match results while enforcing a constraint on the maximum road curvature expected. Algorithm output images, as well as quantitative results, are presented for three distinct road types encountered in actual driving video acquired in the California Mojave Desert.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"2 1","pages":"273-280"},"PeriodicalIF":0.0,"publicationDate":"2005-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79136312","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}
We present a simple algorithm for complete motion planning using deterministic sampling. Our approach relies on computing a star-shaped roadmap of the free space. We partition the free space into star-shaped regions such that a single point called the guard can see every point in the starshaped region. The resulting set of guards capture the intraregion connectivity. We capture the inter-region connectivity by computing connectors that link guards of adjacent regions. We use the guards and connectors to construct a star-shaped roadmap of the free space. We present an efficient algorithm to compute the roadmap in a deterministic manner without computing an explicit representation of the free space. We show that the star-shaped roadmap captures the connectivity of the free space while providing sufficient information to perform complete motion planning. Our approach is relatively simple to implement for robots with translational and rotational degrees of freedom (dof). We highlight the performance of our algorithm on challenging scenarios with narrow passages or when there is no collision-free path for low-dof robots.
{"title":"Star-shaped Roadmaps - A Deterministic Sampling Approach for Complete Motion Planning","authors":"Gokul Varadhan, Dinesh Manocha","doi":"10.15607/RSS.2005.I.004","DOIUrl":"https://doi.org/10.15607/RSS.2005.I.004","url":null,"abstract":"We present a simple algorithm for complete motion planning using deterministic sampling. Our approach relies on computing a star-shaped roadmap of the free space. We partition the free space into star-shaped regions such that a single point called the guard can see every point in the starshaped region. The resulting set of guards capture the intraregion connectivity. We capture the inter-region connectivity by computing connectors that link guards of adjacent regions. We use the guards and connectors to construct a star-shaped roadmap of the free space. We present an efficient algorithm to compute the roadmap in a deterministic manner without computing an explicit representation of the free space. We show that the star-shaped roadmap captures the connectivity of the free space while providing sufficient information to perform complete motion planning. Our approach is relatively simple to implement for robots with translational and rotational degrees of freedom (dof). We highlight the performance of our algorithm on challenging scenarios with narrow passages or when there is no collision-free path for low-dof robots.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"9 1","pages":"25-32"},"PeriodicalIF":0.0,"publicationDate":"2005-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80763579","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 method to compute an exact cell decomposition and corresponding connectivity graph of the configuration space (C-space) of a planar closed chain manipulator moving among point obstacles is developed. By studying the global properties of the loop closure and collision constraint set, a cylindrical decomposition of the collision-free portion of C-space (C-free) is obtained without translating the constraints into polynomials as required by Collins’ method [1]. Once the graph is constructed, motion planning proceeds in the usual way; graph search followed by path construction. Experimental results demonstrate the effectiveness of the algorithm.
{"title":"Complete Path Planning for Planar Closed Chains Among Point Obstacles","authors":"Guanfeng Liu, J. Trinkle","doi":"10.15607/RSS.2005.I.005","DOIUrl":"https://doi.org/10.15607/RSS.2005.I.005","url":null,"abstract":"A method to compute an exact cell decomposition and corresponding connectivity graph of the configuration space (C-space) of a planar closed chain manipulator moving among point obstacles is developed. By studying the global properties of the loop closure and collision constraint set, a cylindrical decomposition of the collision-free portion of C-space (C-free) is obtained without translating the constraints into polynomials as required by Collins’ method [1]. Once the graph is constructed, motion planning proceeds in the usual way; graph search followed by path construction. Experimental results demonstrate the effectiveness of the algorithm.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"22 1","pages":"33-40"},"PeriodicalIF":0.0,"publicationDate":"2005-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84644660","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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