Path planning is one of the key functional blocks for any autonomous aerial vehicle (UAV). The goal of a path planner module is to constantly update the route of the vehicle based on information sensed in real-time. Given the high computational requirements of this task, heterogeneous many-cores are appealing candidates for its execution. Approximate path computation has proven a promising approach to reduce total execution time, at the cost of a slight loss in accuracy. In this work we study performance and accuracy of state-of-the-art, near-optimal parallel path planning in combination with program transformations aimed at ensuring efficient use of embedded GPU resources. We propose a profile-based algorithmic variant which boosts GPU execution by up to â 7Ã, while maintaining the accuracy loss below 5%.
Palossi, D., Marongiu, A., Benini, L. (2017). On the accuracy of near-optimal CPU-based path planning for UAVs. Association for Computing Machinery, Inc [10.1145/3078659.3079072].
On the accuracy of near-optimal CPU-based path planning for UAVs
Marongiu, Andrea;Benini, Luca
2017
Abstract
Path planning is one of the key functional blocks for any autonomous aerial vehicle (UAV). The goal of a path planner module is to constantly update the route of the vehicle based on information sensed in real-time. Given the high computational requirements of this task, heterogeneous many-cores are appealing candidates for its execution. Approximate path computation has proven a promising approach to reduce total execution time, at the cost of a slight loss in accuracy. In this work we study performance and accuracy of state-of-the-art, near-optimal parallel path planning in combination with program transformations aimed at ensuring efficient use of embedded GPU resources. We propose a profile-based algorithmic variant which boosts GPU execution by up to â 7Ã, while maintaining the accuracy loss below 5%.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
