Adaptive Sampling for Humanaware Path Planning in Dynamic Environments Nowadays, robots are increasingly used in densely populated dynamic environments. Robots not only need to complete the navigation tasks quickly, but also need to take into account the human trajectories and the constraints of social rules. In order to avoid the robot going into the crowed areas and improve robot acceptance in the crowded public environment, we propose a humanaware motion planning algorithm that is based on sampling method. Firstly, human will be annoyed and stressed if robots disturb them during operation. To alleviate this uncomfort brought by the robot, we use probabilistic representations to build the Human Domain Zone (HDZ) of individual or crowd behaviors. Besides, we propose a sampling strategy that is capable of biasing the sampling in the area where human feel comfortable or crowd is sparse. Moreover, we put forward an evaluation function to select the optimum trajectory. This function can avoid robot falling into the crowded area through VDM which is used to model the relationship between human and robot. The proposed approach is verified with extensive experiments in simulated environments. The results show that our method has the promising performance in crowded environment. It can also generate a smooth path with higher success rate.

Samplingbased motion planning of 3D solid objects guided by multiple approximate solutions Samplingbased motion planners are often used to solve motion planning problems for robots with many degrees of freedom. These planners explore the related configuration space by random sampling. The wellknown issue of the samplingbased planners is the narrow passage problem. Narrow passages are small collisionfree regions in the configuration space that are, due to their volume, difficult to cover by the random samples. The volume of the narrow passages can be artificially increased by reducing the size of the robot, e.g., by scalingdown its geometry, which increases the probability of placing the random samples into the narrow passages. This allows us to find an approximate solution (trajectory) and use it as a guide to find the solution for a larger robot. Guiding along an approximate solution may, however, fail if this solution leads through such parts of the configuration space that are not reachable or traversable by a larger robot. To improve this guiding process, we propose to compute several approximate solutions leading through different parts of the configuration space, and use all of them to guide the search for a larger robot. We introduce the concept of disabled regions that are prohibited from the exploration using the sampling process. The disabled regions are defined using trajectories already found in the space being searched. The proposed method can solve planning problems with narrow passages with higher success rate than other stateoftheart planners.