In manufacturing and automation settings, robots often have to perform highly-repetitive manipulation tasks in structured environments. In this work we are interested in settings where tasks are similar, yet not identical (e.g., due to uncertain orientation of objects) and motion planning needs to be extremely fast. Preprocessing-based approaches prove to be very beneficial in these settings; they analyze the configuration-space offline to generate some auxiliary information which can then be used in the query phase to speedup planning times. Typically, the tighter the requirement is on query times the larger the memory footprint will be. In particular, for high-dimensional spaces, providing real-time planning capabilities is extremely challenging. While there are planners that guarantee real-time performance by limiting the planning horizon, we are not aware of general-purpose planners capable of doing it for infinite horizon (i.e., planning to the goal). To this end, we propose a preprocessing-based method that provides provable bounds on the query time while incurring only a small amount of memory overhead in the query phase. We evaluate our method on a 7-DOF robot arm and show a speedup of over tenfold in query time when compared to the PRM algorithm, while provably guaranteeing a maximum query time of less than 3 milliseconds.
Finally, our notion of attractor states is similar to control-based methods that ensure safe operation over local regions of the free configuration space @cite_22 @cite_2 . These regions are then used within a high-level motion planner to compute collision-free paths.
@cite_22 @cite_2
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This paper develops a method of composing simple control policies, applicable over a limited region in a dynamical system's free space, such that the resulting composition completely solves the navigation and control problem for the given system operating in a constrained environment. The resulting control policy deployment induces a global control policy that brings the system to the goal, provided that there is a single connected component of the free space containing both the start and goal configurations. In this paper, control policies for both kinematic and simple dynamical systems are developed. This work assumes that the initial velocities are somewhat aligned with the desired velocity vector field. We conclude by offering an outline of an approach for accommodating arbitrary dynamical constraints and initial conditions. We present a method for defining a hybrid control system capable of simultaneously addressing the global navigation and control problem for a convex-bodied wheeled mobile robot navigating amongst obstacles. The method uses parameterized continuous local feedback control policies that ensure safe operation over local regions of the free configuration space; each local policy is designed to respect nonholonomic constraints, bounds on velocities (inputs), and obstacles in the environment. The hybrid control system makes use of a collection of these local control policies in concert with discrete planning tools in order to plan, and replan in the face of changing conditions, while preserving the safety and convergence guarantees of the underlying control policies. This work is validated in simulation and experiment with a convex-bodied wheeled mobile robot. The approach is one of the first that combines formal planning with continuous feedback control guarantees for systems subject to nonholonomic constraints, input bounds, and non-trivial body shape.