3,224 research outputs found

    Correct-by-Construction Approach for Self-Evolvable Robots

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    The paper presents a new formal way of modeling and designing reconfigurable robots, in which case the robots are allowed to reconfigure not only structurally but also functionally. We call such kind of robots "self-evolvable", which have the potential to be more flexible to be used in a wider range of tasks, in a wider range of environments, and with a wider range of users. To accommodate such a concept, i.e., allowing a self-evovable robot to be configured and reconfigured, we present a series of formal constructs, e.g., structural reconfigurable grammar and functional reconfigurable grammar. Furthermore, we present a correct-by-construction strategy, which, given the description of a workspace, the formula specifying a task, and a set of available modules, is capable of constructing during the design phase a robot that is guaranteed to perform the task satisfactorily. We use a planar multi-link manipulator as an example throughout the paper to demonstrate the proposed modeling and designing procedures.Comment: The paper has 17 pages and 4 figure

    Time-Optimal Trajectories for Cooperative Multi-Manipulator Systems

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    In this paper we present two schemes for planning the time-optimal trajectory for cooperative multi-manipulator system(CMMS) carrying a common object. We assume that the desired path is given and parameterizable by an arclength variable. Both approaches take into account the dynamics of the manipulators and the dynamics of the object. The first approach employs linear programming techniques, and it allows us to obtain the timeoptimal execution of the given task utilizing the maximum torque capacities of the joint motors. The second approach is a sub-time-optimal method which is computationally very efficient. In the second approach we try to divide the given load into a share for each manipulator in the CMMS in a manner in which the trajectory acceleration/deceleration is maximized, hence the trajectory execution time is minimized. This load distribution approach uses optimization schemes which degenerate to a linear search algorithm for the case of two robots manipulating a common load, and this results in significant savings on the computation time. The load distribution scheme not only enables us to reduce the computation time but also gives us the possibility of applying this method in real time planning and control of CMMS. Further, we show that under certain object trajectories the load distribution scheme yields truly time-optimal trajectories

    On the Dynamic Properties of Flexible Parallel Manipulators in the Presence of Type 2 Singularities

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    International audienceIn the present paper, we expand information about the conditions for passing through Type 2 singular configurations of a parallel manipulator. It is shown that any parallel manipulator can cross the singular configurations via an optimal control permitting the favourable force distribution, i.e. the wrench applied on the end-effector by the legs and external efforts must be reciprocal to the twist along the direction of the uncontrollable motion. The previous studies have proposed the optimal control conditions for the manipulators with rigid links and flexible actuated joints. The different polynomial laws have been obtained and validated for each examined case. The present study considers the conditions for passing through Type 2 singular configurations for the parallel manipulators with flexible links. By computing the inverse dynamic model of a general flexible parallel robot, the necessary conditions for passing through Type 2 singular configurations are deduced. The suggested approach is illustrated by a 5R parallel manipulator with flexible elements and joints. It is shown that a 16 th order polynomial law is necessary for the optimal force generation. The obtained results are validated by numerical simulations carried out using the software ADAMS

    ๋ชจ์…˜ ํ”„๋ฆฌ๋จธํ‹ฐ๋ธŒ๋ฅผ ์ด์šฉํ•œ ๋ณต์žกํ•œ ๋กœ๋ด‡ ์ž„๋ฌด ํ•™์Šต ๋ฐ ์ผ๋ฐ˜ํ™” ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ•ญ๊ณต์šฐ์ฃผ๊ณตํ•™๊ณผ, 2020. 8. ๊น€ํ˜„์ง„.Learning from demonstrations (LfD) is a promising approach that enables robots to perform a specific movement. As robotic manipulations are substituting a variety of tasks, LfD algorithms are widely used and studied for specifying the robot configurations for the various types of movements. This dissertation presents an approach based on parametric dynamic movement primitives (PDMP) as a motion representation algorithm which is one of relevant LfD techniques. Unlike existing motion representation algorithms, this work not only represents a prescribed motion but also computes the new behavior through a generalization of multiple demonstrations in the actual environment. The generalization process uses Gaussian process regression (GPR) by representing the nonlinear relationship between the PDMP parameters that determine motion and the corresponding environmental variables. The proposed algorithm shows that it serves as a powerful optimal and real-time motion planner among the existing planning algorithms when optimal demonstrations are provided as dataset. In this dissertation, the safety of motion is also considered. Here, safety refers to keeping the system away from certain configurations that are unsafe. The safety criterion of the PDMP internal parameters are computed to check the safety. This safety criterion reflects the new behavior computed through the generalization process, as well as the individual motion safety of the demonstration set. The demonstrations causing unsafe movement are identified and removed. Also, the demolished demonstrations are replaced by proven demonstrations upon this criterion. This work also presents an extension approach reducing the number of required demonstrations for the PDMP framework. This approach is effective where a single mission consists of multiple sub-tasks and requires numerous demonstrations in generalizing them. The whole trajectories in provided demonstrations are segmented into multiple sub-tasks representing unit motions. Then, multiple PDMPs are formed independently for correlated-segments. The phase-decision process determines which sub-task and associated PDMPs to be executed online, allowing multiple PDMPs to be autonomously configured within an integrated framework. GPR formulations are applied to obtain execution time and regional goal configuration for each sub-task. Finally, the proposed approach and its extension are validated with the actual experiments of mobile manipulators. The first two scenarios regarding cooperative aerial transportation demonstrate the excellence of the proposed technique in terms of quick computation, generation of efficient movement, and safety assurance. The last scenario deals with two mobile manipulations using ground vehicles and shows the effectiveness of the proposed extension in executing complex missions.์‹œ์—ฐ ํ•™์Šต ๊ธฐ๋ฒ•(Learning from demonstrations, LfD)์€ ๋กœ๋ด‡์ด ํŠน์ • ๋™์ž‘์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ์œ ๋งํ•œ ๋™์ž‘ ์ƒ์„ฑ ๊ธฐ๋ฒ•์ด๋‹ค. ๋กœ๋ด‡ ์กฐ์ž‘๊ธฐ๊ฐ€ ์ธ๊ฐ„ ์‚ฌํšŒ์—์„œ ๋‹ค์–‘ํ•œ ์—…๋ฌด๋ฅผ ๋Œ€์ฒดํ•ด ๊ฐ์— ๋”ฐ๋ผ, ๋‹ค์–‘ํ•œ ์ž„๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๋กœ๋ด‡์˜ ๋™์ž‘์„ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•ด LfD ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค์€ ๋„๋ฆฌ ์—ฐ๊ตฌ๋˜๊ณ , ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ LfD ๊ธฐ๋ฒ• ์ค‘ ๋ชจ์…˜ ํ”„๋ฆฌ๋จธํ‹ฐ๋ธŒ ๊ธฐ๋ฐ˜์˜ ๋™์ž‘ ์žฌ์ƒ์„ฑ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ธ Parametric dynamic movement primitives(PDMP)์— ๊ธฐ์ดˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์‹œํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ์ž„๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๋ชจ๋ฐ”์ผ ์กฐ์ž‘๊ธฐ์˜ ๊ถค์ ์„ ์ƒ์„ฑํ•œ๋‹ค. ๊ธฐ์กด์˜ ๋™์ž‘ ์žฌ์ƒ์„ฑ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๋‹ฌ๋ฆฌ, ์ด ์—ฐ๊ตฌ๋Š” ์ œ๊ณต๋œ ์‹œ์—ฐ์—์„œ ํ‘œํ˜„๋œ ๋™์ž‘์„ ๋‹จ์ˆœํžˆ ์žฌ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์— ๊ทธ์น˜์ง€ ์•Š๊ณ , ์ƒˆ๋กœ์šด ํ™˜๊ฒฝ์— ๋งž๊ฒŒ ์ผ๋ฐ˜ํ™” ํ•˜๋Š” ๊ณผ์ •์„ ํฌํ•จํ•œ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์ œ์‹œํ•˜๋Š” ์ผ๋ฐ˜ํ™” ๊ณผ์ •์€ PDMPs์˜ ๋‚ด๋ถ€ ํŒŒ๋ผ๋ฏธํ„ฐ ๊ฐ’์ธ ์Šคํƒ€์ผ ํŒŒ๋ผ๋ฏธํ„ฐ์™€ ํ™˜๊ฒฝ ๋ณ€์ˆ˜ ์‚ฌ์ด์˜ ๋น„์„ ํ˜• ๊ด€๊ณ„๋ฅผ ๊ฐ€์šฐ์Šค ํšŒ๊ท€ ๊ธฐ๋ฒ• (Gaussian process regression, GPR)์„ ์ด์šฉํ•˜์—ฌ ์ˆ˜์‹์ ์œผ๋กœ ํ‘œํ˜„ํ•œ๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์€ ๋˜ํ•œ ์ตœ์  ์‹œ์—ฐ๋ฅผ ํ•™์Šตํ•˜๋Š” ๋ฐฉ์‹์„ ํ†ตํ•ด ๊ฐ•๋ ฅํ•œ ์ตœ์  ์‹ค์‹œ๊ฐ„ ๊ฒฝ๋กœ ๊ณ„ํš ๊ธฐ๋ฒ•์œผ๋กœ๋„ ์‘์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋˜ํ•œ ๋กœ๋ด‡์˜ ๊ตฌ๋™ ์•ˆ์ „์„ฑ๋„ ๊ณ ๋ คํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์—์„œ ๋‹ค๋ฃจ์–ด์ง„ ์‹œ์—ฐ ๊ด€๋ฆฌ ๊ธฐ์ˆ ์ด ๋กœ๋ด‡์˜ ๊ตฌ๋™ ํšจ์œจ์„ฑ์„ ๊ฐœ์„ ํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์ œ์‹œ๋œ ๊ฒƒ๊ณผ ๋‹ฌ๋ฆฌ, ์ด ์—ฐ๊ตฌ๋Š” ๊ฐ•ํ•œ ๊ตฌ์†์กฐ๊ฑด์œผ๋กœ ๋กœ๋ด‡์˜ ๊ตฌ๋™ ์•ˆ์ „์„ฑ์„ ํ™•๋ณดํ•˜๋Š” ์‹œ์—ฐ ๊ด€๋ฆฌ ๊ธฐ์ˆ ์„ ํ†ตํ•ด ์•ˆ์ •์„ฑ์„ ๊ณ ๋ คํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ์‹์„ ์ œ์‹œํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ์‹์€ ์Šคํƒ€์ผ ํŒŒ๋ผ๋ฏธํ„ฐ ๊ฐ’ ์ƒ์—์„œ ์•ˆ์ „์„ฑ ๊ธฐ์ค€์„ ๊ณ„์‚ฐํ•˜๋ฉฐ, ์ด ์•ˆ์ „ ๊ธฐ์ค€์„ ํ†ตํ•ด ์‹œ์—ฐ์„ ์ œ๊ฑฐํ•˜๋Š” ์ผ๋ จ์˜ ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ๋˜ํ•œ, ์ œ๊ฑฐ๋œ ์‹œ์œ„๋ฅผ ์•ˆ์ „ ๊ธฐ์ค€์— ๋”ฐ๋ผ ์ž…์ฆ๋œ ์‹œ์œ„๋กœ ๋Œ€์ฒดํ•˜์—ฌ ์ผ๋ฐ˜ํ™” ์„ฑ๋Šฅ์„ ์ €ํ•˜์‹œํ‚ค์ง€ ์•Š๋„๋ก ์‹œ์œ„๋ฅผ ๊ด€๋ฆฌํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋‹ค์ˆ˜์˜ ์‹œ์—ฐ ๊ฐ๊ฐ ๊ฐœ๋ณ„ ๋™์ž‘ ์•ˆ์ „์„ฑ ๋ฟ ์•„๋‹ˆ๋ผ ์˜จ๋ผ์ธ ๋™์ž‘์˜ ์•ˆ์ „์„ฑ๊นŒ์ง€ ๊ณ ๋ คํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์‹ค์‹œ๊ฐ„ ๋กœ๋ด‡ ์กฐ์ž‘๊ธฐ ์šด์šฉ์‹œ ์•ˆ์ „์„ฑ์ด ํ™•๋ณด๋  ์ˆ˜ ์žˆ๋‹ค. ์ œ์•ˆ๋œ ์•ˆ์ •์„ฑ์„ ๊ณ ๋ คํ•œ ์‹œ์—ฐ ๊ด€๋ฆฌ ๊ธฐ์ˆ ์€ ๋˜ํ•œ ํ™˜๊ฒฝ์˜ ์ •์  ์„ค์ •์ด ๋ณ€๊ฒฝ๋˜์–ด ๋ชจ๋“  ์‹œ์—ฐ์„ ๊ต์ฒดํ•ด์•ผ ํ•  ์ˆ˜ ์žˆ๋Š” ์ƒํ™ฉ์—์„œ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์—ฐ๋“ค์„ ํŒ๋ณ„ํ•˜๊ณ , ํšจ์œจ์ ์œผ๋กœ ์žฌ์‚ฌ์šฉํ•˜๋Š” ๋ฐ ์‘์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๋ณธ ๋…ผ๋ฌธ์€ ๋ณต์žกํ•œ ์ž„๋ฌด์—์„œ ์ ์šฉ๋  ์ˆ˜ ์žˆ๋Š” PDMPs์˜ ํ™•์žฅ ๊ธฐ๋ฒ•์ธ seg-PDMPs๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์ด ์ ‘๊ทผ๋ฐฉ์‹์€ ๋ณต์žกํ•œ ์ž„๋ฌด๊ฐ€ ์ผ๋ฐ˜์ ์œผ๋กœ ๋ณต์ˆ˜๊ฐœ์˜ ๊ฐ„๋‹จํ•œ ํ•˜์œ„ ์ž‘์—…์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค๊ณ  ๊ฐ€์ •ํ•œ๋‹ค. ๊ธฐ์กด PDMPs์™€ ๋‹ฌ๋ฆฌ seg-PDMPs๋Š” ์ „์ฒด ๊ถค์ ์„ ํ•˜์œ„ ์ž‘์—…์„ ๋‚˜ํƒ€๋‚ด๋Š” ์—ฌ๋Ÿฌ ๊ฐœ์˜ ๋‹จ์œ„ ๋™์ž‘์œผ๋กœ ๋ถ„ํ• ํ•˜๊ณ , ๊ฐ ๋‹จ์œ„๋™์ž‘์— ๋Œ€ํ•ด ์—ฌ๋Ÿฌ๊ฐœ์˜ PDMPs๋ฅผ ๊ตฌ์„ฑํ•œ๋‹ค. ๊ฐ ๋‹จ์œ„ ๋™์ž‘ ๋ณ„๋กœ ์ƒ์„ฑ๋œ PDMPs๋Š” ํ†ตํ•ฉ๋œ ํ”„๋ ˆ์ž„์›Œํฌ๋‚ด์—์„œ ๋‹จ๊ณ„ ๊ฒฐ์ • ํ”„๋กœ์„ธ์Šค๋ฅผ ํ†ตํ•ด ์ž๋™์ ์œผ๋กœ ํ˜ธ์ถœ๋œ๋‹ค. ๊ฐ ๋‹จ๊ณ„ ๋ณ„๋กœ ๋‹จ์œ„ ๋™์ž‘์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•œ ์‹œ๊ฐ„ ๋ฐ ํ•˜์œ„ ๋ชฉํ‘œ์ ์€ ๊ฐ€์šฐ์Šค ๊ณต์ • ํšŒ๊ท€(GPR)๋ฅผ ์ด์šฉํ•œ ํ™˜๊ฒฝ๋ณ€์ˆ˜์™€์˜์˜ ๊ด€๊ณ„์‹์„ ํ†ตํ•ด ์–ป๋Š”๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ, ์ด ์—ฐ๊ตฌ๋Š” ์ „์ฒด์ ์œผ๋กœ ์š”๊ตฌ๋˜๋Š” ์‹œ์—ฐ์˜ ์ˆ˜๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ์ค„์ผ ๋ฟ ์•„๋‹ˆ๋ผ, ๊ฐ ๋‹จ์œ„๋™์ž‘์˜ ํ‘œํ˜„ ์„ฑ๋Šฅ์„ ๊ฐœ์„ ํ•œ๋‹ค. ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ํ˜‘๋™ ๋ชจ๋ฐ”์ผ ๋กœ๋ด‡ ์กฐ์ž‘๊ธฐ ์‹คํ—˜์„ ํ†ตํ•˜์—ฌ ๊ฒ€์ฆ๋œ๋‹ค. ์„ธ ๊ฐ€์ง€์˜ ์‹œ๋‚˜๋ฆฌ์˜ค๊ฐ€ ๋ณธ ๋…ผ๋ฌธ์—์„œ ๋‹ค๋ฃจ์–ด์ง€๋ฉฐ, ํ•ญ๊ณต ์šด์†ก๊ณผ ๊ด€๋ จ๋œ ์ฒซ ๋‘ ๊ฐ€์ง€ ์‹œ๋‚˜๋ฆฌ์˜ค๋Š” PDMPs ๊ธฐ๋ฒ•์ด ๋กœ๋ด‡ ์กฐ์ž‘๊ธฐ์—์„œ ๋น ๋ฅธ ์ ์‘์„ฑ, ์ž„๋ฌด ํšจ์œจ์„ฑ๊ณผ ์•ˆ์ „์„ฑ ๋ชจ๋‘ ๋งŒ์กฑํ•˜๋Š” ๊ฒƒ์„ ์ž…์ฆํ•œ๋‹ค. ๋งˆ์ง€๋ง‰ ์‹œ๋‚˜๋ฆฌ์˜ค๋Š” ์ง€์ƒ ์ฐจ๋Ÿ‰์„ ์ด์šฉํ•œ ๋‘ ๊ฐœ์˜ ๋กœ๋ด‡ ์กฐ์ž‘๊ธฐ์— ๋Œ€ํ•œ ์‹คํ—˜์œผ๋กœ ๋ณต์žกํ•œ ์ž„๋ฌด ์ˆ˜ํ–‰์„ ํ•˜๊ธฐ ์œ„ํ•ด ํ™•์žฅ๋œ ๊ธฐ๋ฒ•์ธ seg-PDMPs๊ฐ€ ํšจ๊ณผ์ ์œผ๋กœ ๋ณ€ํ™”ํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ ์ผ๋ฐ˜ํ™”๋œ ๋™์ž‘์„ ์ƒ์„ฑํ•จ์„ ๊ฒ€์ฆํ•œ๋‹ค.1 Introduction 1 1.1 Motivations 1 1.2 Literature Survey 3 1.2.1 Conventional Motion Planning in Mobile Manipulations 3 1.2.2 Motion Representation Algorithms 5 1.2.3 Safety-guaranteed Motion Representation Algorithms 7 1.3 Research Objectives and Contributions 7 1.3.1 Motion Generalization in Motion Representation Algorithm 9 1.3.2 Motion Generalization with Safety Guarantee 9 1.3.3 Motion Generalization for Complex Missions 10 1.4 Thesis Organization 11 2 Background 12 2.1 DMPs 12 2.2 Mobile Manipulation Systems 13 2.2.1 Single Mobile Manipulation 14 2.2.2 Cooperative Mobile Manipulations 14 2.3 Experimental Setup 17 2.3.1 Test-beds for Aerial Manipulators 17 2.3.2 Test-beds for Robot Manipulators with Ground Vehicles 17 3 Motion Generalization in Motion Representation Algorithm 22 3.1 Parametric Dynamic Movement Primitives 22 3.2 Generalization Process in PDMPs 26 3.2.1 Environmental Parameters 26 3.2.2 Mapping Function 26 3.3 Simulation Results 29 3.3.1 Two-dimensional Hurdling Motion 29 3.3.2 Cooperative Aerial Transportation 30 4 Motion Generalization with Safety Guarantee 36 4.1 Safety Criterion in Style Parameter 36 4.2 Demonstration Management 39 4.3 Simulation Validation 42 4.3.1 Two-dimensional Hurdling Motion 46 4.3.2 Cooperative Aerial Transportation 47 5 Motion Generalization for Complex Missions 51 5.1 Overall Structure of Seg-PDMPs 51 5.2 Motion Segments 53 5.3 Phase-decision Process 54 5.4 Seg-PDMPs for Single Phase 54 5.5 Simulation Results 55 5.5.1 Initial/terminal Offsets 56 5.5.2 Style Generalization 59 5.5.3 Recombination 61 6 Experimental Validation and Results 63 6.1 Cooperative Aerial Transportation 63 6.2 Cooperative Mobile Hang-dry Mission 70 6.2.1 Demonstrations 70 6.2.2 Simulation Validation 72 6.2.3 Experimental Results 78 7 Conclusions 82 Abstract (in Korean) 93Docto

    Space robotics: Recent accomplishments and opportunities for future research

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    The Langley Guidance, Navigation, and Control Technical Committee (GNCTC) was one of six technical committees created in 1991 by the Chief Scientist, Dr. Michael F. Card. During the kickoff meeting Dr. Card charged the chairmen to: (1) establish a cross-Center committee; (2) support at least one workshop in a selected discipline; and (3) prepare a technical paper on recent accomplishments in the discipline and on opportunities for future research. The Guidance, Navigation, and Control Committee was formed and selected for focus on the discipline of Space robotics. This report is a summary of the committee's assessment of recent accomplishments and opportunities for future research. The report is organized as follows. First is an overview of the data sources used by the committee. Next is a description of technical needs identified by the committee followed by recent accomplishments. Opportunities for future research ends the main body of the report. It includes the primary recommendation of the committee that NASA establish a national space facility for the development of space automation and robotics, one element of which is a telerobotic research platform in space. References 1 and 2 are the proceedings of two workshops sponsored by the committee during its June 1991, through May 1992 term. The focus of the committee for the June 1992 - May 1993 term will be to further define to the recommended platform in space and to add an additional discipline which includes aircraft related GN&C issues. To the latter end members performing aircraft related research will be added to the committee. (A preliminary assessment of future opportunities in aircraft-related GN&C research has been included as appendix A.

    Collision-free motion of two robot arms in a common workspace

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    Collision-free motion of two robot arms in a common workspace is investigated. A collision-free motion is obtained by detecting collisions along the preplanned trajectories using a sphere model for the wrist of each robot and then modifying the paths and/or trajectories of one or both robots to avoid the collision. Detecting and avoiding collisions are based on the premise that: preplanned trajectories of the robots follow a straight line; collisions are restricted to between the wrists of the two robots (which corresponds to the upper three links of PUMA manipulators); and collisions never occur between the beginning points or end points on the straight line paths. The collision detection algorithm is described and some approaches to collision avoidance are discussed

    Prototyping environment for robot manipulators

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    Journal ArticlePrototyping is an important activity in engineering. Prototype development is a good test for checking the viability of a proposed system. Prototypes can also help in determining system parameters, ranges, or in designing better systems. We are proposing a prototyping environment for electro-mechanical systems, and we chosen a 3-link robot manipulator as an example. In Designing a robot manipulator, the interaction between several modules (S/W, VLSI, CAD, CAM, Robotics, and Control) illustrates an interdisciplinary prototyping environment that includes different types of information that are radically different but combined in a coordinated way. This environment will enable optimal and flexible design using reconfigurable links, joints, actuators, and sensors. Such an environment should have the right "mix" of software and hardware components for designing the physical parts and the controllers, and for the algorithmic control for the robot modules (kinematics, inverse kinematics, dynamics, trajectory planning, analog control and computer (digital) control). Specifying object-based communications and catalog mechanisms between the software modules, controllers, physical parts, CAD designs, and actuator and sensor components is a necessary step in the prototyping activities. In this report a framework for flexible prototyping environment for robot manipulators is proposed along with the required sub-systems and interfaces between the different components of this environment

    Aspects of an open architecture robot controller and its integration with a stereo vision sensor.

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    The work presented in this thesis attempts to improve the performance of industrial robot systems in a flexible manufacturing environment by addressing a number of issues related to external sensory feedback and sensor integration, robot kinematic positioning accuracy, and robot dynamic control performance. To provide a powerful control algorithm environment and the support for external sensor integration, a transputer based open architecture robot controller is developed. It features high computational power, user accessibility at various robot control levels and external sensor integration capability. Additionally, an on-line trajectory adaptation scheme is devised and implemented in the open architecture robot controller, enabling a real-time trajectory alteration of robot motion to be achieved in response to external sensory feedback. An in depth discussion is presented on integrating a stereo vision sensor with the robot controller to perform external sensor guided robot operations. Key issues for such a vision based robot system are precise synchronisation between the vision system and the robot controller, and correct target position prediction to counteract the inherent time delay in image processing. These were successfully addressed in a demonstrator system based on a Puma robot. Efforts have also been made to improve the Puma robot kinematic and dynamic performance. A simple, effective, on-line algorithm is developed for solving the inverse kinematics problem of a calibrated industrial robot to improve robot positioning accuracy. On the dynamic control aspect, a robust adaptive robot tracking control algorithm is derived that has an improved performance compared to a conventional PID controller as well as exhibiting relatively modest computational complexity. Experiments have been carried out to validate the open architecture robot controller and demonstrate the performance of the inverse kinematics algorithm, the adaptive servo control algorithm, and the on-line trajectory generation. By integrating the open architecture robot controller with a stereo vision sensor system, robot visual guidance has been achieved with experimental results showing that the integrated system is capable of detecting, tracking and intercepting random objects moving in 3D trajectory at a velocity up to 40mm/s
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