S. M. LaValle and S. A. Hutchinson. An objectivebased stochastic framework for manipulation planning. In IEEE/RSJ/GI International Conference on Robots and Systems (to appear), September 1994.  Home/Search   Document Details and Download   Summary   Related Articles   Check

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S. M. LaValle and S. A. Hutchinson. An objectivebased stochastic framework for manipulation planning. In IEEE/RSJ/GI International Conference on Robots and Systems (to appear), September 1994.

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S. M. LaValle and S. A. Hutchinson. An objectivebased stochastic framework for manipulation planning. In IEEE/RSJ/GI International Conference on Robots and Systems, September 1994.

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S. M. LaValle and S. A. Hutchinson. An objective-based stochastic framework for manipulation planning. In Proc. IEEE/RSJ/GI Int'l Conf. on Intelligent Robots and Systems, pages 1772-1779, September 1994.

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S. M. LaValle and S. A. Hutchinson. An objective-based stochastic framework for manipulation planning. In Proc. IEEE/RSJ/GI Int'l Conf. on Intelligent Robots and Systems, pages 1772-- 1779, September 1994.

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S. M. LaValle and S. A. Hutchinson. An objectivebased stochastic framework for manipulation planning. In Proc. IEEE/RSJ/GI Int'l Conf. on Intelligent Robots and Systems, pages 1772--1779, September 1994.

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S. M. LaValle and S. A. Hutchinson. An objective-based stochastic framework for manipulation planning. In review for IEEE/RSJ International Conference on Intelligent Robots and Systems, 1994.

....choice of a control function that incorporates state feedback, represented as u i (t) fl i (x; t) In terms of control laws, this is equivalent to a closed loop controller. In principle, extensions that incorporate incomplete or imperfect information feedback can be made (see, e.g. 6] [113], 117] 116] The distinction between using fl i and u i will become more important in the coming sections. We refer to fl = ffl 1 ; fl 2 ; fl N g as a strategy. Let Gamma denote the set of all allowable strategies. A stationary strategy is a special form of strategy that ....

S. M. LaValle and S. A. Hutchinson. An objective-based stochastic framework for manipulation planning. In Proc. IEEE/RSJ/GI Int'l Conf. on Intelligent Robots and Systems, pages 1772--1779, September 1994.

....k [1] x k 1 [1] and x k [2] x k 1 [2] however, m k 1 is not necessarily equal to m k because the assembly transition equation determines m k 1 . We prohibit the robot from considering actions that produce an obstacle collision; however, one could also consider compliant or constrained motions [24, 31, 32, 40]. We now define the notion of a robot strategy for our context. A strategy at stage k of A is a function fl k : X U . For each state, x k , the function fl k yields an action u k = fl k (x k ) The set of mappings ffl 1 ; fl 2 ; fl K g is denoted by fl and termed a strategy. This is ....

....However, the uncertainty involved in fine motion planning can be included directly into the model. A treatment of additional forms of uncertainty in fine motion planning (position and control uncertainty) that is compatible with our treatment of the uncertainty with respect to time is reported in [24]. 7 Conclusions Robot motion optimization over time is important since efficient motion planning eventually translates into an increased throughput in an assembly system. Moreover, optimizing the efficiency of the individual robot cell can play a significant role in ensuring the stability of the ....

S. M. LaValle and S. A. Hutchinson. An objective-based stochastic framework for manipulation planning. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, 1994.

....= x k 1 [1] and x k [2] x k 1 [2] however, e k 1 is not necessarily equal to e k because the environment transition equation determines e k 1 . We prohibit the robot from considering actions that produce an obstacle collision; however, one could also consider compliant or constrained motions [40, 45, 51, 73]. We now define the notion of a robot strategy for our context. At first it might seem appropriate to define some action u k for each stage; however, we want a motion plan that is prepared for the various contingencies presented by the changing environment. Therefore, we define a strategy at ....

....can be defined stochastically, to reflect Type CP uncertainty. The information space concepts from Section 6.1 can be expanded to include complete sensing history that characterizes uncertainty in the robot configuration. Extensions to multiple robot coordination problems can also be considered [40, 39]. Computational issues involved in these complex combinations will depend mostly on the dimensions of the state and information spaces for the problem. 43 Acknowledgments We thank Steve Sullivan for providing some of the numerical dynamic programming code. A Incremental Motion Models We have ....

S. M. LaValle and S. A. Hutchinson. An objective-based stochastic framework for manipulation planning. In Proc. IEEE/RSJ/GI Int'l Conf. on Intelligent Robots and Systems, pages 1772--1779, September 1994.

.... motion strategy aspects; however, by using the game theoretic formulation as a representational tool, particular models, analysis, algorithms, and computed solutions have so far been obtained for four particular classes of problems: 1) motion strategies under uncertainty in sensing and control [73, 71]; 2) motion strategies under environment uncertainties [70, 76] 3) multiple robot motion strategies [70, 72] and (4) maintaining visibility of a predictable target in a cluttered workspace [75] For the first problem class, a general method for determining feedback strategies is developed by ....

S. M. LaValle and S. A. Hutchinson. An objective-based stochastic framework for manipulation planning. In Proc. IEEE/RSJ/GI Int'l Conf. on Intelligent Robots and Systems, pages 1772--1779, September 1994.

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