A Novel Approach for Automatically Measuring the Bidirectional Reflectance Distribution Function (BRDF) of Surfaces Using Spectral Sensors and a Robotic Arm.

The Bidirectional Reflectance Distribution Function (BRDF) quantifies the reflected light from a surface as a function of illumination and observation angles. It is a crucial yet challenging aspect of remote sensing, essential for characterizing surface reflectance and properties. Accurate BRDF measurements are integral to surface property analysis and various remote sensing applications. Conventional methods, such as goniometers, provide precise angle-dependent evaluations. However, their high cost, bulkiness, and limited portability significantly hinder their deployment in diverse real-world scenarios. Alternatively, free-handed BRDF measurement techniques eliminate fixed setups but suffer from human error and subjectivity, leading to inconsistent results.

We propose a novel automated system combining a robotic arm and spectral sensors to address these limitations. The system utilizes a robotic arm to precisely maneuver the sensor on a hemispherical trajectory around the target surface, ensuring consistent angles and distances throughout the measurement process. Specifically, the UR10e robotic arm by Universal Robots, with its 12.5 kg payload, 1300 mm reach, and six flexible joints, was employed for its precision, flexibility, and advanced motion control capabilities.

Programming the robotic arm for BRDF measurements required solving a constrained generalized inverse kinematics problem optimized using fuzzy logic to ensure collision-free movement and clear sensor fields of view. Experimental validation demonstrated exceptional sensor localization accuracy, achieving an angular precision of 0.1° under optimal conditions. This automated system facilitates spectral BRDF measurement and modeling across various surfaces with enhanced accuracy, speed, and operational feasibility.

Once the method is fully validated under controlled laboratory conditions, we intend to extend the application of this system to outdoor, real-life scenarios. The robotic arm will be mounted on a platform to conduct measurements in natural environments. This next step aims to evaluate the system's robustness and effectiveness in capturing BRDF data under varying environmental conditions, ultimately confirming its suitability for real-world applications. Such advancements will significantly enhance the accuracy and practicality of BRDF measurements for diverse industries and research domains.