A Programmable Architecture for Robot Motion Planning Acceleration

Murray, Sean; Floyd-Jones, Will; Konidaris, George; Sorin, Daniel J.

doi:10.1109/asap.2019.000-4

Cited by 18 publications

(19 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, approximate methods such as probabilistic roadmaps [55] are widely used [61], [90] over the last 20 years. Many hardware accelerators [6], [66], [76], [77], [93], [116] have been proposed for these algorithms.…”

Section: Motion Planningmentioning

confidence: 99%

“…Leven and Hutchinson [62] proposed a real-time motion planning approach based on probabilistic roadmaps for a dynamic environment and is also used by MPAs [39], [66], [76], [77], [93], [116]. In this approach, the motion set is generated for an obstaclefree environment.…”

Section: Probabilistic Roadmapsmentioning

confidence: 99%

“…The collision detection step is the most time-and energyconsuming in motion planning and takes up to 99% of the total runtime on a CPU [10]. As collision detection must be performed in real-time with low latency to ensure safety in an environment with dynamic obstacles, MPAs typically dedicate more than 85% of their total area to accelerate collision detection [66], [76], [77], making the CDM the most vulnerable component in an MPA. Unfortunately, an erroneous collision detection can potentially lead to a collision between the robot and an obstacle in its surroundings, making collision detection safety-critical.…”

Section: Probabilistic Roadmapsmentioning

confidence: 99%

“…The computational and real-time demands of motion planning exceed those provided by typical CPUs. Recently, several approaches have been proposed to accelerate motion planning on different hardware platforms, including GPUs [10], [29], FPGAs [6], [78], [93], and ASICs [66], [76], [95], [116]. Motion Planning Accelerators (MPAs) have achieved impressive performance gains and are being adopted in industry [46], [84], [89].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we study soft errors in the collision detection module (CDM), which is the largest, most energy-consuming, and safety-critical component in MPAs [6], [66], [76], [95]. We find that CDMs in these accelerators consist of on-chip storage elements to store the information about space that the robot passes through for possible motions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Characterizing and Improving the Resilience of Accelerators in Autonomous Robots

Shah¹,

Xue²,

Pattabiraman³

et al. 2021

Preprint

View full text Add to dashboard Cite

Motion planning is a computationally intensive and well-studied problem in autonomous robots. However, motion planning hardware accelerators (MPA) must be soft-error resilient for deployment in safety-critical applications, and blanket application of traditional mitigation techniques is ill-suited due to cost, power, and performance overheads. We propose Collision Exposure Factor (CEF), a novel metric to assess the failure vulnerability of circuits processing spatial relationships, including motion planning. CEF is based on the insight that the safety violation probability increases with the surface area of the physical space exposed by a bit-flip. We evaluate CEF on four MPAs. We demonstrate empirically that CEF is correlated with safety violation probability, and that CEF-aware selective error mitigation provides 12.3×, 9.6×, and 4.2× lower Failures-In-Time (FIT) rate on average for the same amount of protected memory compared to uniform, bit-position, and access-frequency-aware selection of critical data. Furthermore, we show how to employ CEF to enable fault characterization using 23, 000× fewer fault injection (FI) experiments than exhaustive FI, and evaluate our FI approach on different robots and MPAs. We demonstrate that CEF-aware FI can provide insights on vulnerable bits in an MPA while taking the same amount of time as uniform statistical FI. Finally, we use the CEF to formulate guidelines for designing soft-error resilient MPAs.

show abstract