Reconfigurable mobile planetary rovers are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. These results illustrate the generality of the planner in exploiting the platform’s mechanical ability to fluidly transition between various physical geometric configurations, and wheeled/legged locomotion modes. In minimizing an energy objective that combines the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion.

Experimental results are provided for a reconfigurable wheel-on-leg platform that benchmark the algorithm against state-of-the-art samplingbased planners. Importantly, probabilistic completeness and asymptotic optimality are preserved. This technique provides a novel way to incorporate prior knowledge of sub-spaces to efficiently bias search within the existing FMT* framework. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full state space. Our algorithm assumes a decomposition of the full state space into multiple sub-spaces, and begins by rapidly finding a set of paths through one such sub-space. We propose a novel hierarchical variant of the Fast Marching Tree (FMT*) algorithm. However, planning paths for these robots is challenging due to their many degrees of freedom. Reconfigurable mobile robots are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. We thereby demonstrate sparse multilevel roadmaps as an efficient tool for feasible and infeasible high-dimensional planning problems. In evaluations, we outperform sparse roadmap planners on challenging planning problems, in particular problems which are high-dimensional, contain narrow passages or are infeasible. We argue SMLR to be probabilistically complete and asymptotically near-optimal by inheritance from sparse roadmap planners. To this end, we represent multilevel abstractions using the language of fiber bundles, and generalize sparse roadmap planners by using the concept of restriction sampling with visibility regions. In this work, we generalize sparse roadmaps to multilevel abstractions by developing a novel algorithm, the sparse multilevel roadmap planner (SMLR). In prior work, we showed improved planning performance on high-dimensional planning problems by using multilevel abstractions to simplify state spaces. However, sparse roadmaps do not scale well to high-dimensional planning problems. Sparse roadmaps are important to compactly represent state spaces, to determine problems to be infeasible and to terminate in finite time.

Roadmap Planner 1.6 download