Abstract
We introduce a novel approach to long-range path planning that relies on a learned model to predict the outcome of local motions using possibly partial knowledge. The model is trained from a dataset of trajectories acquired in a self-supervised way. Sampling-based path planners use this component to evaluate edges to be added to the planning tree. We illustrate the application of this pipeline with two robots: a complex, simulated, quadruped robot (ANYmal) moving on rough terrains; and a simple, real, differential-drive robot (Mighty Thymio), whose geometry is assumed unknown, moving among obstacles. We quantitatively evaluate the model performance in predicting the outcome of short moves and long-range paths; finally, we show that planning results in reasonable paths.
Highlights
S AMPLING-BASED planning [1] is a powerful and general solution to path planning that casts the problem as a search for a sequence of feasible local motions, each of which links two nearby states: the first local motion starts at the source state and the last ends at the target state
The feasibility of local motions is determined by a local motion estimator, which should account for the robot kinematics, the local planners and controllers at play, and all available knowledge about the environment
Data-driven approach to robotic path planning: first, we learn to predict the outcome of short trajectories from local information; we use this model to discriminate between potential edges while building a dense random tree to connect source and target states
Summary
S AMPLING-BASED planning [1] is a powerful and general solution to path planning that casts the problem as a search for a sequence of feasible local motions, each of which links two nearby states: the first local motion starts at the source state and the last ends at the target state. The local motion estimator is typically a simple component that checks whether a direct move linking two states collides with known obstacles and complies with the robot’s kinematic constraints. This approach is unsuitable for cases with complex, possibly stochastic interactions between the robot and its environment. A possible alternative to evaluate whether a local motion is feasible is to accurately simulate the robot moving on the terrain patch between two locations Such simulation must account, among other factors, Manuscript received September 10, 2019; accepted January 8, 2020. This letter was recommended for publication by Associate Editor Dr C.
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