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Sampling-based motion planning algorithms provide a means to adapt the behaviors of autonomous robots to changing or unknown a priori environmental conditions. However, as the size of the space over which a sampling-based approach needs to search is increased (perhaps due to considering robots with many degree of freedom) the computational limits necessary for real-time operation are quickly exceeded. To address this issue, this paper presents a novel sampling-based approach to locomotion planning for highly-articulated robots wherein the parameters associated with a class of locomotive behaviors (e.g., inter-leg coordination, stride length, etc.) are inferred in real-time using a sample-efficient algorithm. More specifically, this work presents a data-based approach wherein offline-learned optimal behaviors, represented using central pattern generators (CPGs), are used to train a class of probabilistic graphical models (PGMs). The trained PGMs are then used to inform a sampling distribution of inferred walking gaits for legged hexapod robots. Simulated as well as hardware results are presented to demonstrate the successful application of the online inference algorithm. 

Sampling-based motion planning algorithms provide a means to adapt the behaviors of autonomous robots to changing or unknown a priori environmental conditions. However, as the size of the space over which a sampling-based approach needs to search is increased (perhaps due to considering robots with many degree of freedom) the computational limits necessary for real-time operation are quickly exceeded. To address this issue, this paper presents a novel sampling-based approach to locomotion planning for highly-articulated robots wherein the parameters associated with a class of locomotive behaviors (e.g., inter-leg coordination, stride length, etc.) are inferred in real-time using a sample-efficient algorithm. More specifically, this work presents a data-based approach wherein offline-learned optimal behaviors, represented using central pattern generators (CPGs), are used to train a class of probabilistic graphical models (PGMs). The trained PGMs are then used to inform a sampling distribution of inferred walking gaits for legged hexapod robots. Simulated as well as hardware results are presented to demonstrate the successful application of the online inference algorithm. 

Approaches to autonomous navigation for unmanned ground vehicles rely on motion planning algorithms that optimize maneuvers under kinematic and environmental constraints. Algorithms that combine heuristic search with local optimization are well suited to domains where solution optimality is favored over speed and memory resources are limited as they often improve the optimality of solutions without increasing the sampling density. To address the runtime performance limitations of such algorithms, this paper introduces Predictively Adapted State Lattices, an extension of recombinant motion planning search space construction that adapts the representation by selecting regions to optimize using a learned model trained to predict the expected improvement. The model aids in prioritizing computations that optimize regions where significant improvement is anticipated. We evaluate the performance of the proposed method through statistical and qualitative comparisons to alternative State Lattice approaches for a simulated mobile robot with nonholonomic constraints. Results demonstrate an advance in the ability of recombinant motion planning search spaces to improve relative optimality at reduced runtime in varyingly complex environments.