Published papers

This paper presents an energy and perception aware framework for path planning and navigation of unmanned aerial vehicles (UAVs) in GNSS-denied and spatiotemporal wind environments. The proposed framework mainly consists of the global and local path planning methods that respectively consider the energy consumption of an UAV and perception quality of a light detection and ranging (LiDAR) sensor mounted on the UAV. The energy consumption is estimated based on the aerodynamic model that calculates drag and lift forces on the UAV. The global planner then uses the total energy consumption in the spatiotemporal wind as the cost function to find an energy-efficient path as a set of waypoints. The local path planning navigates the UAV between the waypoints with maintaining the perception quality. The perception quality is quantified based on how well the LiDAR sensor scans feature points around the UAV that highly correlates with the navigation accuracy. Numerical simulation study for each of the global and local path planners validates their usefulness. Further, the overall framework is entirely verified in a long-range flight scenario of the photorealistic environments developed in the Gazebo simulation.

テレセントリックレンズを用いたビジュアルオドメトリ（移動速度推定）カメラを開発し，その有用性を走行実験によって検証した．

© 2018, Fuji Technology Press. All rights reserved. This paper proposes a computationally efficient method for generating a three-dimensional environment map and estimating robot position. The proposed method assumes that a laser range finder mounted on a mobile robot can be used to provide a set of point cloud data of an environment around the mobile robot. The proposed method then extracts typical feature points from the point cloud data using an intensity image taken by the laser range finder. Subsequently, feature points extracted from two or more different sets of point cloud data are correlated by the iterative closest point algorithm that matches the points between the sets, creating a large map of the environment as well as estimating robot location in the map. The proposed method maintains an accuracy of the mapping while reducing the computational cost by downsampling the points used for the iterative closest point. An experimental demonstration using a mobile robot test bed confirms the usefulness of the proposed method.

A planar travel distance sensor (two-dimensional sen-sor) was developed for a mobile robot in sandy terrain. The sensor system uses an optical flow device integrated into a small module with a simple configuration. The system achieves a high sampling rate on the order of milliseconds as well as precise measurement on a sub-millimeter order. Its performance was evaluated experimentally for measurement accuracy and repeatability, velocity response, robustness at varied heights with respect to terrain, and terrain surface characteristics. The experimental results confirm that the two-dimensional sensor system is accurate, having an error of distance traveled of less than a few percent, and that it possesses a wide dynamic range for the robot’s traveling velocity. This paper also discusses the applicability of the two-dimensional sensor for practical scenarios on the basis of the experimental findings.

Planetary rovers need high mobility on a rough terrain such as sandy soil, because such a terrain often impedes the rover mobility and causes significant wheel slip. Therefore, the accurate estimation of wheel soil interaction characteristics is an important issue. Recent studies related to wheel soil interaction mechanics have revealed that the classical wheel model has not adequately addressed the actual interaction characteristics observed through experiments. This article proposes an in-wheel sensor system equipped with two sensory devices on the wheel surface: force sensors that directly measure the force distribution between the wheel and soil and light sensors that accurately detect the wheel soil surface boundary line. This sensor design enables the accurate measurement of wheel terrain interaction characteristics such as wheel force distribution, wheel soil contact angles, and wheel sinkage when the powered wheel runs on loose sand. In this article, the development of the in-wheel sensor system is introduced along with its system diagram and sensor modules. The usefulness of the in-wheel sensor system is then experimentally evaluated via a single wheel test bench. The experimental results confirm that explicit differences can be observed between the classical wheel model and practical data measured by the in-wheel sensor system. (C) 2015 ISTVS. Published by Elsevier Ltd. All rights reserved.

Omnidirectional vehicles have been widely applied in several areas, but most of them are designed for the case of motion on flat, smooth terrain, and are not feasible for outdoor usage. This paper presents the design and development of an omnidirectional mobile robot that possesses high mobility in rough terrain. The omnidirectional robot consists of a main body with four sets of mobility modules, called an active split offset caster (ASOC). The ASOC module has independently driven dual wheels that produce arbitrary planar translational velocity, enabling the robot to achieve its omnidirectional motion. Each module is connected to the main body via a parallel link with shock absorbers, allowing the robot to conform to uneven terrain. In this paper, the design and development of the ASOC-driven omnidirectional mobile robot for rough terrain are described. A control scheme that considers the kinematics of the omnidirectional mobile robot is presented. The mobility of the robot is also evaluated experimentally based on a metric called the ASOC mobility index. The mobility evaluation test clarifies a design tradeoff between terrain adaptability and omnidirectional mobility due to the shock absorbers. In addition, an odometry improvement technique that can reduce position estimation error due to wheel slippage is proposed. Experimental odometry tests confirmed that the proposed technique is able to improve the odometry accuracy for sharp-turning maneuvers.

Unmanned mobile robots in rough terrains are a key technology for achieving smart agriculture and smart construction. The mobility performance of robots highly depends on the moisture content of soil, and past few studies have focused on terrain classification using moisture content. In this study, we demonstrate a convolutional neural network-based terrain classification method using RGB-infrared (IR) images. The method first classifies soil types and then categorizes the moisture content of the terrain. A three-step image preprocessing for RGB-IR images is also integrated into the method that is applicable to an actual environment. An experimental study of the terrain classification confirmed that the proposed method achieved an accuracy of more than 99% in classifying the soil type. Furthermore, the classification accuracy of the moisture content was approximately 69% for pumice and 100% for dark soil. The proposed method can be useful for different scenarios, such as small-scale agriculture with mobile robots, smart agriculture for monitoring the moisture content, and earthworks in small areas.

Collaborative exploration using unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has become increasingly popular in academia over the past decade. This study addresses the task of autonomously landing a UAV on a ground vehicle in rough terrain, which is fundamental, yet challenging, for UAV-UGV collaborative exploration. The method proposed in this paper estimates the relative pose and velocity between UAV and UGV and uses model predictive control for UAV trajectory planning while considering the field of view of camera onboard the UAV. Through a simulation study, we confirmed that the proposed method enables a UAV to accurately track the UGV traversing in rough terrain, and to land on it with 100% success rate at the UGV velocity of 1.0 m/s and still 50% at 3.0 m/s.

A planetary exploration rover has been used for scientific missions or as a precursor for a future manned mission. The rover’s autonomous system is managed by a space-qualified, radiation-hardened onboard computer; hence, the processing performance for such a computer is strictly limited, owing to the limitation to power supply. Generally, a computationally efficient algorithm in the autonomous system is favorable. This study, therefore, presents a computationally efficient and sub-optimal trajectory planning framework for the rover. The framework exploits an incremental search algorithm, which can generate more optimal solutions as the number of iterations increases. Such an incremental search is subjected to the trade-off between trajectory optimality and computational burden. Therefore, we introduce the trajectory-quality growth rate (TQGR) to statistically analyze the relationship between trajectory optimality and computational cost. This analysis is conducted in several types of terrain, and the planning stop criterion is estimated. Furthermore, the relation between terrain features and the stop criterion is modeled offline by a machine learning technique. Then, using the criterion predicted by the model, the proposed framework appropriately interrupts the incremental search in online motion planning, resulting in a sub-optimal trajectory with less computational burden. Trajectory planning simulation in various real terrain data validates that the proposed framework can, on average, reduce the computational cost by 47.6% while maintaining 63.8% of trajectory optimality. Furthermore, the simulation result shows the proposed framework still performs well even though the planning stop criterion is not adequately predicted.

Soft robotic systems have applications in industrial, medical, and security applications. Many applications require these robots to be small and lightweight. One challenge in developing a soft robotic system is to drive multiple degrees-of-freedom (DOF) with few actuators, thereby reducing system size and weight. This paper presents the analysis and design of an inchworm-like mobile robot that consists of multiple, independent thermally activated joints but is driven by a single actuator. To realize control of this under-actuated system, a solder-based locking mechanism has been developed to selectively activate individual joints without requiring additional actuators. The design and performance analysis of a prototype mobile robot that is capable of inchworm-like translational and steering motion is described. The design of novel "feet" with anisotropic friction properties is also described.

Omnidirectional vehicles have been widely applied in several areas, but most of them are designed for the case of motion on flat, smooth terrain, and are not feasible for outdoor usage. This paper presents an omnidirectional mobile robot that possesses highmobility in rough terrain. The omnidirectional robot employs four sets of mobility modules, called active split offset caster (ASOC). The ASOCmodule has two independently-driven wheels that produce arbitrary planar translational velocity, enabling the robot to achieve its omnidirectional mobility. Each module is connected to the main body of the robot via a parallel link with shock absorbers. In this paper, a design and development of the ASOC-driven omnidirectional mobile robot for rough terrain are described. Also, a control scheme that considers a kinematics of the omnidirectional mobile robot is presented. The omnidirectional mobility of the robot regardless of ifs heading direction is experimentally evaluated based on ametric called omnidirectional mobility index. © Springer-Verlag Berlin Heidelberg 2014.