Vision-Language-Action (VLA) models enable robots to map visual observations and language instructions directly to robotic actions. However, existing VLA models struggle with complex multi-step tasks requiring logical planning and precise manipulations. Current Chain-of-Thought approaches have limitations in simultaneously capturing low-level visual details and high-level logical planning, as well as suffering from high inference latency with compounding errors. This paper proposes DualCoT-VLA, a novel visual-linguistic CoT method with parallel reasoning mechanism that integrates visual CoT for comprehensive multi-modal reasoning, enabling robots to perform effective thinking before acting for manipulation tasks.