In this paper, we develop a multi-human detection system with a team of robots basically in an indoor environment. To start with, we propose a hybrid approach to resolve the problem of human leg detection using Laser Range Finder (LRF) for each robot, that returns not only “true” or “false” type of answer but also a probability. Specifically, the set of measurement data obtained from the laser range finder mounted on a robot is further decomposed into several sectors using an appropriate segmentation technique. Then, we apply a probabilistic model to compare these sectors with leg patterns to check if any of them belongs to the set of human leg patterns or not. Next, we examine the promising leg sectors with a modified Inscribe Angle Variance (IAV) method in order to confirm if these sectors are from human leg's arc feature or not. Moreover, we also use motion detector to check if these objects move or not as an enhancement of the detection. For the entire multi-human detection system, each robot of the team delivers the detected human information to our central control computer through the Inter-Process Communication (IPC). With prior map information of the residing environment and supposing each robot in the team has a localization module, we can then map these results of human detection from every robot into their global coordinates after process of data association. But in order to reduce the computational complexity while doing the data association among these robots in a team, we introduce a set of appropriate rules. Finally, we apply a particle filter based tracking algorithm to keep accurate track of people being detected and to improve the robustness of the detection outcome. This work has been evaluated through several experiments with a number of mobile robots and humans in an indoor environment, and promising performance has been observed.

This paper proposes a navigation algorithm that considers the states of humans and robots in order to achieve harmonious coexistence between them. A robot navigation in the presence of humans and other robots is rarely considered in the field of robotics. When navigating through a space filled with humans and robots with different functions, a robot should not only pay attention to obstacle avoidance and goal seeking, it should also take into account whether it interferes with other people or robots. To deal with this problem, we propose several harmonious rules, which guarantee a safe and smooth navigation in a human-robot environment. Based on these rules, a practical navigation method-human-centered sensitive navigation (HCSN)-is proposed. HCSN considers the fact that both humans and robots have sensitive zones, depending on their security regions or on a human's psychological state. We model these zones as various sensitive fields with priorities, whereby robots tend to yield socially acceptable movements.

This paper proposes a navigation algorithm that considers the states of humans and other robots in order to achieve harmonious coexistence between robots and humans. When navigating through humans and robots with different functions, a robot should not only pay attention to obstacle avoidance and goal seeking, it should also take care of whether it interferes with other people or robots. To deal with this problem, we propose several harmonious rules, which guarantee a safe and smooth navigation in multi-human and multi-robot (MHMR) environment. Based on those rules, a practical navigation method-human- centered sensitive navigation (HCSN)-is proposed. HCSN considers the fact that both humans and robots have sensitive zones depending on their security regions or on psychological feeling of people. We model these zones as various sensitive fields with priorities, whereby robots tend to yield socially acceptable movements.