4 research outputs found
Motion Planning as Online Learning: A Multi-Armed Bandit Approach to Kinodynamic Sampling-Based Planning
Kinodynamic motion planners allow robots to perform complex manipulation tasks under dynamics constraints or with black-box models. However, they struggle to find high-quality solutions, especially when a steering function is unavailable. This letter presents a novel approach that adaptively biases the sampling distribution to improve the planner's performance. The key contribution is to formulate the sampling bias problem as a non-stationary multi-armed bandit problem, where the arms of the bandit correspond to sets of possible transitions. High-reward regions are identified by clustering transitions from sequential runs of kinodynamic RRT and a bandit algorithm decides what region to sample at each timestep. The letter demonstrates the approach on several simulated examples as well as a 7-degree-of-freedom manipulation task with dynamics uncertainty, suggesting that the approach finds better solutions faster and leads to a higher success rate in execution
Asymptotically Optimal Sampling-Based Motion Planning Methods
Motion planning is a fundamental problem in autonomous robotics that requires
finding a path to a specified goal that avoids obstacles and takes into account
a robot's limitations and constraints. It is often desirable for this path to
also optimize a cost function, such as path length.
Formal path-quality guarantees for continuously valued search spaces are an
active area of research interest. Recent results have proven that some
sampling-based planning methods probabilistically converge toward the optimal
solution as computational effort approaches infinity. This survey summarizes
the assumptions behind these popular asymptotically optimal techniques and
provides an introduction to the significant ongoing research on this topic.Comment: Posted with permission from the Annual Review of Control, Robotics,
and Autonomous Systems, Volume 4. Copyright 2021 by Annual Reviews,
https://www.annualreviews.org/. 25 pages. 2 figure
Efficient Path Planning in Narrow Passages via Closed-Form Minkowski Operations
Path planning has long been one of the major research areas in robotics, with
PRM and RRT being two of the most effective classes of path planners. Though
generally very efficient, these sampling-based planners can become
computationally expensive in the important case of "narrow passages". This
paper develops a path planning paradigm specifically formulated for narrow
passage problems. The core is based on planning for rigid-body robots
encapsulated by unions of ellipsoids. The environmental features are enclosed
geometrically using convex differentiable surfaces (e.g., superquadrics). The
main benefit of doing this is that configuration-space obstacles can be
parameterized explicitly in closed form, thereby allowing prior knowledge to be
used to avoid sampling infeasible configurations. Then, by characterizing a
tight volume bound for multiple ellipsoids, robot transitions involving
rotations are guaranteed to be collision-free without traditional collision
detection. Furthermore, combining the stochastic sampling strategy, the
proposed planning framework can be extended to solving higher dimensional
problems in which the robot has a moving base and articulated appendages.
Benchmark results show that, remarkably, the proposed framework outperforms the
popular sampling-based planners in terms of computational time and success rate
in finding a path through narrow corridors and in higher dimensional
configuration spaces