Computer Science Robotics at Rensselaer Faculty: Srinivas Akella Wes Huang Volkan Isler Jeff Trinkle John Wen Thanks to Barry Bharathm from the GRASP Lab at University of Pennsylvania for providing the last four slides.

Computer Science Summer 2004 at Lake George

Computer Science Current Research Foci: Motivations and Issues Model-based manipulation planning –Robots can observe unstructured environs, but cannot do physical work –Simulation with unilateral contact – one cannot plan if one cannot predict –Curse of dimensionality Dimension of space of inputs and design parameters is very large Need methods to prune away large chunks of the search space –Family of models of mechanics is needed –Methods must provide tractible mechanism for handling uncertainty Exact motion planning –Exact MP algorithms for general problems are complete but intractable –Sample-based algorithms suffer inefficiencies due to lack of knowledge of global structure of C-space –We are extending the space of problems solvable by exact methods –We are using knowledge of global topology to “inform” sample-based methods

Computer Science Hierarchical Family of Models Models range from pure geometric to dynamic with contact compliance Required model “resolution” is dependent on design or planning task Approach: –Plan with low resolution model first –Use low resolution results to speed planning with high resolution model –Repeat until plan/design with required accuracy is achieved Current modeling research –Focusing on a continuous family of models covering the region shaded region –Understanding relationships will facilitate translating results across model upgrade steps during planning and design Dynamic Quasistatic Rigid Compliant Geometric Kinematic

Computer Science Two Examples Valid quasistatic plan exists No quasistatic plan found, but dynamic plan exists Dexterous Manipulation Planning Part enters cg down Parts Feeder Design Parts feeder design goals: 1)Exit orientation independent of entering orientation 2)High throughput Design geometry of feeder to guarantee 1) and maximize 2). Feeder geometry has 12 design parameters Evaluate feeder design via simulation

Computer Science Geometric feasibility of part feeder design using RRT RRT simulation finds Geometrically feasible design points in design space.

Computer Science RRT simulation shows Geometrically infeasible design parameters

Computer Science RRT simulation finds kinematically feasible path starting with geometrically feasible parameters

Computer Science A feasible initial design (Geometric & Kinematic) used to bootstrap dynamic analysis and optimization