In this study, we concisely investigate two phases in the hybrid A-star algorithm for non-holonomic robots: the forward search phase and analytic expansion phase. The forward search phase considers the kinematics of the robot model in order to plan continuous motion of the robot in discrete grid maps. Reeds-Shepp (RS) curve in the analytic expansion phase augments the accuracy and the speed of the algorithm. However, RS curves are often produced close to obstacles, especially at corners. Consequently, the robot may collide with obstacles through the process of movement at these corners because of the measurement errors or errors of motor controllers. Therefore, we propose an improved RS method to eventually improve the hybrid A-star algorithm’s performance in terms of safety for robots to move in indoor environments. The advantage of the proposed method is that the non-holonomic robot has multiple options of curvature or turning radius to move safer on pathways. To select a safer route among multiple routes to a goal configuration, we introduce a cost function to evaluate the cost of risk of robot collision, and the cost of movement of the robot along the route. In addition, generated paths by the forward search phase always consist of unnecessary turning points. To overcome this issue, we present a fine-tuning of motion primitive in the forward search phase to make the route smoother without using complex path smoothing techniques. In the end, the effectiveness of the improved method is verified via its performance in simulations using benchmark maps where cost of risk of collision and number of turning points are reduced by up to around 20%.

As human-robot collaboration increases tremendously in real-world applications, a fully autonomous and reliable mobile robot for the collaboration has been a central research topic and investigated extensively in a large number of studies. One of the most pressing issues in such topic is the collision-free navigation that has a moving goal and unknown obstacles under the unstructured environment. In this paper, a cognitive robotic system is proposed for the robot to navigate itself to the moving target person without obstacle collision. This cognitive robotic system consists of a cognitive agent, which is created based on Soar cognitive architecture to reason its current situation and make action decision for the robot to avoid obstacles and reach the target position, and a speed planning module, which is based on dynamic window approach to generate appropriate linear and angular velocities for driving the robot’s motors. For the implementation of the proposed system, we use a differential drive wheel robot equipped with two ultra-wideband sensors and a color depth camera as the experimental platform. Finally, to evaluate the performance of our system in actual operating conditions, we conduct experiments with a scenario that includes main tasks: avoiding consecutive unknown obstacles and turning at corner while the robot follows continuously human user along the corridor.

Humanoid robots are versatile robot platforms that can carry out intelligent tasks and services for humans, including intimate interactions. For high mobility, a robust stabilization of motion including biped walking is crucial. This paper employs and elaborates on sensory reflex control to stabilize standing motion and biped walking using basic sensors such as an inertial measurement unit (IMU) and a force-sensing resistor (FSR). Specifically, normalized zero-moment points processed from FSR data are used in the reflexive control of a simple motion of swinging the whole body while standing, and the measured inclination angle of the trunk, filtered from IMU data, is used for biped walking on a sloped floor. The proposed control scheme is validated through experiments with the commercial humanoid robot, ROBOTIS-OP.

This study aims to interpret and apply Asimov’s Three Laws of Robotics to home service robots. An agent is developed herein with the ability to focus its attention on human beings’ health, particularly the elderly and the diseased, by delivering food. The agent is developed on a cognitive agent architecture, state, operator, and result (Soar), to enable effective reasoning and decision-making skills. This study deals with basic home care services, such as food delivery and emergency response; therefore, common food care and emergency rules are newly proposed based on the priority values that correspond to a family’s circumstances and/or emergency levels. Asimov’s Three Laws are modified to aid the home service robot to follow a predetermined order in selecting a food item or recommending an alternative food item suitable for its user’s prevailing health condition. Experimental results confirm that reasoning and decision-making of the proposed agent are logically and ethically valid for a home service robot and ensure compliance with both the original and modified Asimov’s Three Laws.

Bilateration is one of the localization methods that provides the location information of the target node using two anchors’ location information and two distances between the target node and each anchor. This paper shows that bilateration with different configurations in a three-anchor positioning system gives better precision than trilateration and the same precision as the nonlinear least square (NLS) method when the target is outside the convex hull of the anchors, and also that the combinatorial use of bilaterations gives the same level of precision as the NLS method or tilateration even when the target is inside the convex hull of the anchors. For the rigorous proof of this claim, we first develop a theory that the bilateration method can be naturally modeled and understood by adopting a pair of distances as a complex variable and the mapping in the bilateration method as a complex-valued sine function with a complex variable, respectively. We explain why the proposed complexification is more intuitive and applicable to real-world problems than the real-valued bilateration method. Since the complex sine function has many nice properties and one-to-one correspondence, it can be regarded as a fundamental transformation from the measurement space to position space in a two-dimensional positioning system. Next, based on the complex-valued function theory for bilateration, we describe the error propagation and show that a nonlinear least square method can be replaced with the closed formula in the bilateration localization system. The error propagation of the bilateration has a unique property: the error in position space propagates only in the horizontal direction with anchors and is unchanged in the other direction regardless of the target’s position.