Using GPU and SIMD Implementations to Improve Performance of Robotic Emotional Processes

Abstract

Future robotic systems are being implemented using control architectures based on emotions. In these architectures, the emotional processes decide which behaviors the robot must activate to fulfill the objectives. The number of emotional processes increases with the complexity level of the application, limiting the processing capacity of the control processor to solve complex problems. Fortunately, the potential parallelism of emotional processes permits their execution in parallel. In this paper, different alternatives are used to exploit the parallelism of the emotional processes. On the one hand, we take advantage of the multiple cores and single instruction multiple data (SIMD) instructions sets already available on modern microprocessors. On the other hand, we also consider using a GPU. Different number of cores with and without enabling SIMD instructions and a GPU-based implementation are compared to analyze their suitability to cope with robotic applications. The applications are set-up taking into account different conditions and states of the robot. Experimental results show that the single processor can undertake most of the simple problems at a speed of 1 m/s. For a speed of 2 m/s, a 8-core processor permits solving most of the problems. When the most constrained problem is required, the solution is to combine SIMD instructions with multicore or to use a co-processor GPU to provide the needed computing power.