We present a method for autonomous exploration in complex three-dimensional (3D) environments. Our method demonstrates exploration faster than the current state-of-the-art using a hierarchical framework -one level maintains data densely and computes a detailed path within a local planning horizon, while another level maintains data sparsely and computes a coarse path at the global scale. Such a framework shares the insight that detailed processing is most effective close to the robot, and gains computational speed by trading-off computation of details far away from the robot. The method optimizes an overall exploration path with respect to the length of the path. In addition, the path in the local area is kinodynamically feasible for the vehicle to follow at a high speed. In experiments, our systems autonomously explore indoor and outdoor environments at a high degree of complexity, with ground and aerial robots. The method produces 80% more exploration efficiency, defined as the average explored volume per second through a run, and consumes less than 50% of computation compared to the stateof-the-art.
This paper addresses a detection problem where sparse measurements are utilized to estimate the source parameters in a mixed multi-modal radiation field. As the limitation of dimensional scalability and the unimodal characteristic, most existing algorithms fail to detect the multi-point sources gathered in narrow regions, especially with no prior knowledge about intensity and source number. The proposed Peak Suppressed Particle Filter (PSPF) method utilizes a hybrid scheme of multi-layer particle filter, mean-shift clustering technique and peak suppression correction to solve the major challenges faced by current existing algorithms. Firstly, the algorithm realizes sequential estimation of multi-point sources in a cross-mixed radiation field by using particle filtering and suppressing intensity peak value, while existing algorithms could just identify single point or spatially separated point sources. Secondly, the number of radioactive sources could be determined in a non-parametric manner as the fact that invalid particle swarms would disperse automatically. In contrast, existing algorithms either require prior information or rely on expensive statistic estimation and comparison. Additionally, to improve the prediction stability and convergent performance, distance correction module and configuration maintenance machine are developed to sustain the multimodal prediction stability. Finally, simulations and physical experiments are carried out in aspects such as different noise level, non-parametric property, processing time and large-scale estimation, to validate the effectiveness and robustness of the PSPF algorithm.
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