The human-friendly navigation of mobile robots is a significant social and technological issue. There are many potential applications of human-following technology. Good examples are human-following shopping carts, porter robots at airports, and museum guide robots. In this paper, we propose detection and tracking schemes for human legs by the use of a single laser range finder. The leg detection algorithm takes an inductive approach by the application of a support vector data description scheme and simple attributes. We establish an efficient leg-tracking scheme by exploiting a human walking model to achieve robust tracking under occlusions. The proposed schemes are successfully verified through experiments.

We present one approach to achieve safe navigation in an indoor dynamic environment. So far, there have been various useful collision avoidance algorithms and path planning schemes. However, those algorithms possess fundamental limitations in that the robot can avoid only ldquovisiblerdquo ones among surrounded obstacles. In a real environment, it is not possible to detect all the dynamic obstacles around the robot. There are many occluded regions due to the limited field of view. In order to avoid collisions, it is desirable to exploit visibility information. This paper proposes a safe navigation scheme to reduce collision risk considering occluded dynamic obstacles. The robot's motion is controlled by the hybrid control scheme. The possibility of collision is dually reflected to path planning and speed control. The proposed scheme clearly indicates the structural procedure on how to model and to exploit the risk of navigation. The proposed scheme is experimentally tested in a real office building. The experimental results show that the robot moves along the safe path to obtain sufficient field of view. In addition, safe speed constraints are applied in motion control. It is experimentally verified that a robot safely navigates in dynamic indoor environment by adopting the proposed scheme.

A bacteria-powered microrobot (BPM) is a hybrid robotic system consisting of an SU-8 microstructure with active surfaces or bacterial carpets, in which massive arrays of biomolecular flagellar motors work cooperatively. This paper suggests an obstacle-avoidance method based on a BPM's response to electric fields. The negatively charged bacteria enable the BPM to follow electric fields. In our previous demonstration of the single BPM controllability, we observed a vast change in the control dynamics when obstructions distorted the applied electric field and affected BPM steering and control. In this paper, we demonstrate an obstacle avoidance method that takes the electric field distortion into account to navigate a BPM through multiple static obstacles in real time. We used an artificial potential field and configuration space in our algorithm to generate an objective function for the electric field distortion and collision around/with obstacles, respectively. In addition, finite-element modeling through COMSOL Multiphysics engineering software was used to simulate charged-particle trajectories in a distorted electric field. Finally, we describe the feasibility of our proposed obstacle avoidance approach through experiments and compared these data with simulation results.

The realization of reconfigurable modular microrobots could aid drug delivery and microsurgery by allowing a single system to navigate diverse environments and perform multiple tasks. So far, microrobotic systems are limited by insufficient versatility; for instance, helical shapes commonly used for magnetic swimmers cannot effectively assemble and disassemble into different size and shapes. Here by using microswimmers with simple geometries constructed of spherical particles, we show how magnetohydrodynamics can be used to assemble and disassemble modular microrobots with different physical characteristics. We develop a mechanistic physical model that we use to improve assembly strategies. Furthermore, we experimentally demonstrate the feasibility of dynamically changing the physical properties of microswimmers through assembly and disassembly in a controlled fluidic environment. Finally, we show that different configurations have different swimming properties by examining swimming speed dependence on configuration size.

Advances in microrobotics for biological applications are often limited due to their complex manufacturing processes, which often utilize cytotoxic materials, as well as limitations in the ability to manipulate these small devices wirelessly. In an effort to overcome these challenges, we investigated a facile method for generating biocompatible hydrogel based robots that are capable of being manipulated using an externally generated magnetic field. Here, we experimentally demonstrate the fabrication and autonomous control of loaded-alginate microspheres, which we term artificial cells. In order to generate these microparticles, we employed a centrifuge-based method in which microspheres were rapidly ejected from a nozzle tip. Specifically, we used two mixtures of sodium alginate; one containing iron oxide nanoparticles and the other containing mammalian cells. This mixture was loaded into a needle that was fixed on top of a microtube containing calcium chloride, and then briefly centrifuged to generate hundreds of Janus microspheres. The fabricated microparticles were then magnetically actuated with a rotating magnetic field, generated using electromagnetic coils, prompting the particles to roll across a glass substrate. Also, using vision-based feedback control, a single artificial cell was manipulated to autonomously move in a programmed pattern.