Robotic grasping has become one of the most important domains of robotics research over the past few decades due to its wide range of applications in industrial automation. The model of grasping objects by robot hand depends on a good number of factors, such as type and size of the object, the morphology of object, type of hand, degree of freedom, etc. Thus, the model sometimes becomes mathematically intractable. With the progress in computational capability, soft computing methods have found a way to address the challenges faced by traditional methods while dealing with the robotic grasping problem. This article aims to summarize the outcome of a systematic study in the field of application of soft computing methods in robotic grasping and manipulation. The key processes of robotic grasping where soft computing methods are applied are identified, and research contributions of all processes are analyzed. This review presents a state-of-the-art survey and attempts to find the research gaps in the area of soft computing applications to address the robotic grasping problem.
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