Agricultural robotics is a comparatively new field. Recently, it has gotten special attention because of agricultural labor shortages and rising labor costs. With the increasing operational use of agricultural robots,...Agricultural robotics is a comparatively new field. Recently, it has gotten special attention because of agricultural labor shortages and rising labor costs. With the increasing operational use of agricultural robots, the need for a dedicated robotic simulator for such activity has been noted here. The need of a dedicated and specialty simulator for agricultural robotics research is the main focus area of this paper. Moreover, limitations have been pointed out when conventional robot simulators are used directly in agricultural settings. In a nutshell, it has been tried to emphasize that a dedicated simulator for agricultural robotics would be a timely advance and would accelerate the growth of agricultural robots.展开更多
Photon mapping can simulate some special effects efficiently such as shadows and caustics. Photon mapping runs in two phases： the photon map generating phase and the radiance estimation phase. In this paper, we focus...Photon mapping can simulate some special effects efficiently such as shadows and caustics. Photon mapping runs in two phases： the photon map generating phase and the radiance estimation phase. In this paper, we focus on the bandwidth selection process in the second phase, as it can affect the final quality significantly. Poor results with noise arise if few photons are collected, while bias appears if a large number of photons are collected. In order to solve this issue, we propose an adaptive radiance estimation solution to obtain trade-offs between noise and bias by changing the number of neighboring photons and the shape of the collected area according to the radiance gradient. Our approach can be applied in both the direct and the indirect illumination computation. Finally, experimental results show that our approach can produce smoother quality while keeping the high frequency features perfectly compared with the original photon mapping algorithm.展开更多
文摘Agricultural robotics is a comparatively new field. Recently, it has gotten special attention because of agricultural labor shortages and rising labor costs. With the increasing operational use of agricultural robots, the need for a dedicated robotic simulator for such activity has been noted here. The need of a dedicated and specialty simulator for agricultural robotics research is the main focus area of this paper. Moreover, limitations have been pointed out when conventional robot simulators are used directly in agricultural settings. In a nutshell, it has been tried to emphasize that a dedicated simulator for agricultural robotics would be a timely advance and would accelerate the growth of agricultural robots.
基金This work was partly supported by the National Natural Science Foundation of China under Grant Nos. 61472224 and 61472225, the National High Technology Research and Development 863 Program of China under Grant No. 2012AAOIA306, the Special Funding of Independent Innovation and Transformation of Achievements in Shandong Province of China under Grant No. 2014ZZCX08201, Shandong Key Research and Development Program under Grant No, 2015GGX106006, Young Scholars Program of Shandong University under Grant No. 2015WLJH41, and the Special Funds of Taishan Scholar Construction Project.
文摘Photon mapping can simulate some special effects efficiently such as shadows and caustics. Photon mapping runs in two phases： the photon map generating phase and the radiance estimation phase. In this paper, we focus on the bandwidth selection process in the second phase, as it can affect the final quality significantly. Poor results with noise arise if few photons are collected, while bias appears if a large number of photons are collected. In order to solve this issue, we propose an adaptive radiance estimation solution to obtain trade-offs between noise and bias by changing the number of neighboring photons and the shape of the collected area according to the radiance gradient. Our approach can be applied in both the direct and the indirect illumination computation. Finally, experimental results show that our approach can produce smoother quality while keeping the high frequency features perfectly compared with the original photon mapping algorithm.
