Nakju Lett Doh

Spline-Based RRT Path Planner for Non-Holonomic Robots

Planning in a cluttered environment under differential constraints is a difficult problem because the planner must satisfy the external constraints that arise from obstacles in the environment and the internal constraints due to the kinematic/dynamic limitations of the robot. This paper proposes a novel Spline-based Rapidly-exploring Random Tree (SRRT) algorithm which treats both the external and internal constraints simultaneously and efficiently. The computationally expensive numerical integration of the system dynamics is replaced by an efficient spline curve parameterization. In addition, the SRRT guarantees continuity of curvature along the path satisfying any upper-bounded curvature constraints. This paper presents the underlying theory to the SRRT algorithm and presents simulation and experiment results of a mobile robot efficiently navigating through cluttered environments.

Relative localization using path odometry information

All mobile bases suffer from localization errors. Previous approaches to accommodate for localization errors either use external sensors such as lasers or sonars, or use internal sensors like encoders. An encoder’s information is integrated to derive the robot’s position; this is called odometry. A combination of external and internal sensors will ultimately solve the localization error problem, but this paper focuses only on processing the odometry information. We solve the localization problem by forming a new odometry error model for the synchro-drive robot then use a novel procedure to accurately estimate the error parameters of the odometry error model. This new procedure drives the robot through a known path and then uses the shape of the resulting path to estimate the model parameters. Experimental results validate that the proposed method precisely estimates the error parameters and that the derived odometry error model of the synchro-drive robot is correct.

Robust discrimination method of the electrooculogram signals for human-computer interaction controlling mobile robot

Abstract In this paper, a robust algorithm that discriminates various eye motions from the ElectroOculoGram (EOG) signals is proposed. Previous researches that use the EOG only focused on saccadic motions or blinks. However, we cover all eye motions including double/triple blinks and left/right winks. Furthermore, we suggest a novel method, which removes noises of the EOG, to increase the robustness of the discrimination. The method is called “an ideal velocity shape algorithm” which compares the real velocity of the EOG with an ideal velocity designed under a noise free assumption. This algorithm significantly reduces the effects of the noises and thus enhances the robustness. Detected eye motions are used for aHuman-Computer Interaction (HCI) between a person and a mobile robot. In the HCI, the person successfully controlled the robot for a target tracking and point stabilization.

Dependable Humanoid Navigation System Based on Bipedal Locomotion

In this paper, a dependable humanoid navigation system is proposed by considering many difficulties in humanoid navigation based on bipedal locomotion in an uncertain environment. In particular, we propose a layered architecture to resolve complicated problems through a hierarchical manner. Within the proposed software architecture, a walking path planner, a walking footstep planner, and a walking pattern generator are integrated in a hierarchy to create a reliable motion that overcomes foot slippage and localization sensor noise. Each layer is designed to overcome difficulties originating from bipedal locomotion such as unstable dynamics, inclusion of a sinusoidal noise component in the localization sensor measurement, and disturbance regarding discrete footstepping. The designed navigation system is implemented on a human-sized experimental humanoid platform and is tested for the evaluation of its reliability and robustness in various tasks.

Observation Likelihood Model Design and Failure Recovery Scheme toward Reliable Localization of Mobile Robots

Although there have been many researches on mobile robot localization, it is still difficult to obtain reliable localization performance in a human co-existing real environment. Reliability of localization is highly dependent upon developers experiences because uncertainty is caused by a variety of reasons. We have developed a range sensor based integrated localization scheme for various indoor service robots. Through the experience, we found out that there are several significant experimental issues. In this paper, we provide useful solutions for following questions which are frequently faced with in practical applications: 1) How to design an observation likelihood model? 2) How to detect the localization failure? 3) How to recover from the localization failure? We present design guidelines of observation likelihood model. Localization failure detection and recovery schemes are presented by focusing on abrupt wheel slippage. Experiments were carried out in a typical office building environment. The proposed scheme to identify the localizer status is useful in practical environments. Moreover, the semi-global localization is a computationally efficient recovery scheme from localization failure. The results of experiments and analysis clearly present the usefulness of proposed solutions.

An exploration strategy using sonar sensors in corridor environments

We present a novel solution for topological exploration in corridor environments using cheap and error-prone sonar sensors. Topological exploration requires significant location detection and motion planning. To detect nodes (i.e., significant places) robustly, we propose a new measure, the eigenvalue ratio (EVR), which converts geometrical shapes in the environment into quantitative values using principal component analysis. For planning the safe motion of a robot, we propose the circle following (CF) method, which abstracts the geometry of the environment while taking the characteristics of the sonar sensors into consideration. Integrating the EVR with the CF method results in a topological exploration strategy using sonar sensors approach. The practicality of this approach is demonstrated by simulations and real experiments in corridor environments.

Minimal Grasper: A Practical Robotic Grasper With Robust Performance for Pick-and-Place Tasks

In this paper, a flexible enveloping grasper is proposed for pick-and-place tasks with low manipulation and task planning complexity for practical applications. The proposed grasper has two main characteristics: self-adaptivity and flexibility. Self-adaptivity means that the proposed grasper can grip an object in a self-adaptive way such that various process complexities (e.g., sensing, force control, and sensor–motor coordination) are significantly reduced. By flexibility, we mean that, by using a flexible material, a stable grip can be implemented to cause increased friction between the grasper and the target object as a result of increased contact area. These two properties help the proposed grasper to minimize internal forces in a passive manner and to achieve successful force distribution with self-adaptivity when performing enveloping grasping. Three sets of experiments were performed with an average success rate of 93.2% in pick-and-place tasks.

Humanoid Path Planning From HRI Perspective: A Scalable Approach via Waypoints With a Time Index

This paper proposes a path planner for a humanoid robot to enhance its performance in terms of the human-robot interaction perspective. From the human point of view, the proposed method uses the time index that can generate a path that humans feel to be natural. In terms of the robot, the proposed method yields a waypoint-based path, the simplicity of which enables accurate tracking even for humanoid robots with complex dynamics. From an environmental perspective through which interactions occur, the proposed method can be easily expanded to a wide area. Overall, the proposed method can be described as a scalable path planner via waypoints with a time index for humanoid robots. Experiments have been conducted in test beds where the robot encounters unexpected exceptional situations. Throughout these trials, the robot successfully reached the goal location while iteratively replanning the path.

A robust localization algorithm in topological maps with dynamic noises

Purpose – The papers purpose is to propose a localization algorithm for topological maps constituted by nodes and edges in a graph form. The focus is to develop a robust localization algorithm that works well even under various dynamic noises.Design/methodology/approach – For robust localization, the authors propose an algorithm which utilizes all available data such as node information, sensor measurements at the current time step (which are used in previous algorithms) and edge information, and sensor measurements at previous time steps (which have not been considered in other papers). Also, the algorithm estimates a robots location in a multi‐modal manner which increases its robustness.Findings – Findings show that the proposed algorithm works well in topological maps with various dynamics which are induced by the moving objects in the map and measurement noises from cheap sensors.Originality/value – Unlike previous approaches, the proposed algorithm has three key features: usage of edge data, inclus...

Full-DOF Calibration of a Rotating 2-D LIDAR With a Simple Plane Measurement

This paper proposes a calibration method that accurately estimates six parameters between the two centers of 2-D light detection and ranging (LIDAR) and a rotating platform. This method uses a simple plane, and to the best of our knowledge, it is the first to enable full-degree-of-freedom (DOF) estimation without additional hardware. The key concept behind this method is a decoupling property, in which the direction of a line on a plane does not contain 3-DOF translation terms. Based on this, a cost function for rotation is constructed, and 3-DOF rotation parameters are estimated. With this rotation, the remaining 3-DOF translation parameters are calculated in a manner that minimizes the cost function for translation only. In other words, an original 6-DOF problem is decoupled into two 3-DOF estimation problems. Given these cost functions, degenerate cases are mathematically analyzed for known cases (incomplete), and the robustness is numerically tested for all possible cases (complete). The performance of the method is validated by extensive simulations and experimentations, and the estimated parameters from the proposed method demonstrate better accuracy than previous methods.