This paper presents a capacitive sensing system, addressing the issue of collision avoidance in partially modelled or unknown robot-assisted industrial environment by means of object distance measurement, motion tracking, and surface profile detection. The sensor consists of a mesh of multiple electrodes, a digital control module, a capacitance to digital converter, and a data processing module. The mesh is composed of 16 metal squares organized to form a 4x4 capacitor matrix. The electrode connections within the matrix can be reconfigured at run time by the digital control logic to provide multiple sense functionalities. Statistical regression models are applied to derive the distance and track the motion. A machine learning algorithm (Support Vector Machine, SVM) is applied to measured data to classify surface profiles. The fabricated sensing system has the ability of detecting objects at distances up to 20 cm from the sensor, and shows accuracy over 90% in profile recognition.