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There certainly has been research into the application of GPUs to motion planning. However, the paper presented is faster, and results in power consumption, approximately 10% of the power consumption of the GPU for the same task.

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Now that the paper has been posted, it does seem it's not just about power consumption but also parallelism. Although these factors were discussed in the conclusion with reference to their suggestion for future R&D with a hardware-based approach, the reality for implementors now is that commercial availability, cost and solution development time probably do heavily favour usible mobile GPUs. The biggest drawback in the paper seems to be that if the robot's physical configuration changes (for example, an arm is bent or it picks up a large object) the planning circuitry may no longer be accurate.

Finally, I couldn't help but think of this quote: Sin #3 - Picking the low-hanging fruit: It is unsurprising that it is easy to beat a general system by specializing it. Few researchers comment on the composability of their solutions. - Schwarzkopf et al, 'The seven deadly sins of cloud computing research' (2014) https://github.com/globalcitizen/taoup

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