Real-time quasi dense two-frames depth map for Autonomous Guided Vehicles

Ducrot, Arnaud; Dumortier, Yann; Herlin, Isabelle; Ducrot, Vincent

doi:10.1109/ivs.2011.5940507

Cited by 4 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the cost of migrating all the operating processes of the robot to the FPGA resulted in a quite prohibitive solution. Ducrot et al [20] present a map estimating process with 2 depths and a partial implementation using GPU processors. They use a configuration of the Cuda-Core 448 architecture combined with dual-core processors.…”

Section: Related Workmentioning

confidence: 99%

Embedded GPU and multicore processors for emotional-based mobile robotic agents

Almenar

Domínguez

Hassan

et al. 2016

Future Generation Computer Systems

View full text Add to dashboard Cite

h i g h l i g h t s• We present alternatives to accelerate the behavior motivation's processes of emotional robotic agents.• We compare three accelerators based on GPUs, Multicores and SIMD-instructions (SSE and AVX) to a base single-core implementation.• We have simulated a mobile robotic application to compare the accelerating alternatives. a b s t r a c tControl architectures based on emotions are becoming promising solutions for the implementation of future robotic systems. The basic controllers of this architecture are the emotional processes that decide which behaviors the robot must activate to fulfill the objectives. The number of emotional processes increases (hundreds of millions/s) with the complexity level of the application, limiting the processing capacity of a main processor to solve the complex problems. Fortunately, the potential parallelism of emotional processes permits their execution in parallel, hence enabling the computing power to tackle the complex dynamic problems. In this paper, Graphic Processing Unit (GPU), multicore processors and single instruction multiple data (SIMD) instructions are used to provide parallelism for the emotional processes. Different GPUs, multicore processors and SIMD instruction sets are evaluated and compared to analyze their suitability to cope with robotic applications. The applications are set-up taking into account different environmental conditions, robot dynamics and emotional states. Experimental results show that, despite the fact that GPUs have a bottleneck in the data transmission between the host and the device, the evaluated GTX 670 GPU provides a performance of more than one order of magnitude greater than the initial implementation of the architecture on a single core. Thus, all complex proposed application problems can be solved using the GPU technology in contrast to the first prototype where only 55% of them could be solved. Using AVX SIMD instructions, the performance of the architecture is increased in 3.25 times in relation to the first implementation. Thus, from the 27 proposed applications about 88.8% are solved. In the case of the SSE SIMD instructions, the performance is almost doubled and the robot could solve about 74% of the proposed application problems. The use of AVX and SSE SIMD instructions provides almost the same performance as a quad-and a dual-core, respectively, with the advantage that they do not add any additional hardware cost.

show abstract

Section: Related Workmentioning

confidence: 99%