Details of a Researcher - ISHIGAMI Genya

Papers - ISHIGAMI Genya

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Hardware-in-the-loop Simulation for Real-time Autonomous Tracking and Landing of an Unmanned Aerial Vehicle

Aoki N., Ishigami G.

2023 IEEE/SICE International Symposium on System Integration, SII 2023 (2023 IEEE/SICE International Symposium on System Integration, SII 2023) 2023

Research paper (international conference proceedings), Joint Work, Last author, Accepted, ISSN 2474-2317

Collaborative exploration using unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has become increasingly popular in the past decade. This study addresses real-time autonomous tracking and landing of a UAV on a moving ground vehicle, which is fundamental for the UAV-UGV collaborative exploration. The method proposed in this study estimates the relative pose and velocity between a UAV and UGV, and uses model predictive control for UAV trajectory planning while considering the field of view of the camera onboard the UAV. We elaborate the hardware-in-the-loop simulator (HITL) with a physical companion computer, and confirm that the proposed method enables a UAV to land on a UGV traversing on rough terrain based on online computations in the HITL. Additionally, we present statistical analysis of the simulation results of typical and computationally demanding scenarios to elucidate the computational cost on the real machine.

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Autonomous tracking and landing of an unmanned aerial vehicle on a ground vehicle in rough terrain

Aoki N., Ishigami G.

Advanced Robotics (Advanced Robotics) 37 ( 5 ) 344 - 355 2023

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 01691864

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.

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LSTM-based Prediction of Excavating Resistive Force Using Bucket Trajectory Shape

Hashizume Kosuke, Aizawa Masanori, Ooi Takeshi, Ishigami Genya

Journal of the Robotics Society of Japan (The Robotics Society of Japan) 41 ( 1 ) 102 - 105 2023

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 02891824

Unmanned construction machine has become a promising technology in the civil engineering field. The machine is still less efficient, especially in excavating soil, than the manned construction because the unmanned machine works carefully to avoid potential hazards due to unpredicted soil deformation as well as excavation force. This research aims to predict time-series resistive force of bucket during soil excavation based on the Recurrent Neural Network. First, we experimentally obtain excavating resistive force for various excavation trajectories. In the experiment, a bucket follows pre-determined trajectories with four different elliptical arcs, and the resistive force acting on the bucket is measured. The prediction model of the resistive force is then elaborated by indexing the bucket trajectories. The model validation confirms that the aspect ratio of the ellipse of the trajectories is an effective index for accurately predicting the excavating resistive force.

Uncertainty-Aware Trajectory Planning: Using Uncertainty Quantification and Propagation in Traversability Prediction of Planetary Rovers

Reiya Takemura, Genya Ishigami

IEEE Robotics & Automation Magazine (Institute of Electrical and Electronics Engineers (IEEE)) 2 - 12 2023

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 10709932

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Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains

Endo M., Taniai T., Yonetani R., Ishigami G.

Proceedings - IEEE International Conference on Robotics and Automation (Proceedings - IEEE International Conference on Robotics and Automation) 11852 - 11858 2023

Research paper (international conference proceedings), Joint Work, Last author, Accepted, ISSN 10504729

Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors. Especially for heterogeneous terrains where the geological features vary from place to place, erroneous traversability prediction can become more apparent, increasing the risk of unrecoverable rover's wheel slip and immobilization. In this work, we propose a new path planning algorithm that explicitly accounts for such erroneous prediction. The key idea is the probabilistic fusion of distinctive ML models for terrain type classification and slip prediction into a single distribution. This gives us a multimodal slip distribution accounting for heterogeneous terrains and further allows statistical risk assessment to be applied to derive risk-aware traversing costs for path planning. Extensive simulation experiments have demonstrated that the proposed method is able to generate more feasible paths on heterogeneous terrains compared to existing methods.

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Experimental Validation of Traction Control of Compaction Roller Towed by Small Mobile Robot

Sasaki Yuto, Ishigami Genya

Journal of the Robotics Society of Japan (The Robotics Society of Japan) 41 ( 8 ) 732 - 735 2023

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 02891824

Constructing lunar infrastructures including landing pad and habitable base is essential for enhancing lunar exploration and utilization in the future. The lunar surface regolith, fine-grained loose soil, needs to be compacted and consolidated enough for securely constructing the infrastructures. In this paper, we present a traction control of compaction roller towed by a small mobile robot. The control method aims to minimize the relative drawbar pull between the small mobile robot and the compaction roller. We also manufactured two different shapes of compaction rollers and verified the relationship between the roller shape and control performance. The experimental results show that the proposed traction control effectively minimizes the relative drawbar pull for the spiral-shaped roller while it becomes unstable for the cylindrical-shaped roller because of the roller excites soil bulldozing or excavation during its rolling.

Computationally efficient and sub-optimal trajectory planning framework based on trajectory-quality growth rate analysis

Takemura R., Ishigami G.

Frontiers in Robotics and AI (Frontiers in Robotics and AI) 9 994437 (15 pages) 2022.10

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 2296-9144

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.

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Active Traversability Learning via Risk-Aware Information Gathering for Planetary Exploration Rovers

Endo M., Ishigami G.

IEEE Robotics and Automation Letters (IEEE Robotics and Automation Letters) 7 ( 4 ) 11855 - 11862 2022.10

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 2377-3766

Traversability prediction enables safe and efficient autonomous rover operation on deformable planetary surfaces. Revealing spatial distribution from terrain geometry to rover slip behavior is key to assessing prospective traversability, but is hindered by insufficient in situ measurements on hazardous states due to conservative rover traverses. To achieve a more accurate prediction, this letter proposes a framework that actively learns latent traversability by exploring informative terrain under the constraints of stochastic rover slip. With a Gaussian process (GP) modeling the spatial distribution, we devise an iterative two-stage framework that gradually refines the model estimation, combining risk-aware informative path planning and GP updates by taking in situ measurements. The path planning stage employs our designed sampling-based algorithm to generate informative trajectories with fault-tolerant risk inference, while the GP is cautiously updated with traverse data to avoid rover immobilization. Chance constraint formulation is exploited in the framework to infer the stochastic reachability of informative regions. Through GP estimates reducing uncertainty, the algorithm incrementally reaches informative yet hazardous states along feasible trajectories. Simulation studies in rough terrain environments demonstrate that the proposed framework gathers informative traverse data while averting rover stuck situations to estimate the latent traversability model.

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Active Traversability Learning via Risk-aware Information Gathering for Planetary Exploration Rovers

Masafumi Endo and Genya Ishigami

Proceedings of 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) pp. 11855-11862 2022.09

Research paper (international conference proceedings), Joint Work, Last author, Accepted

Human-Following Control in Furrow For Agricultural Support Robot

Yorozu A., Ishigami G., Takahashi M.

Proceedings of the 16th International Conference on Intelligent Autonomous Systems (Lecture Notes in Networks and Systems) 2022

Research paper (international conference proceedings), Joint Work, Accepted, ISSN 23673370

In Japan, the reduction in agricultural work population and increase in abandoned cultivated land are major social issues. To overcome these issues, we have proposed a small agricultural robot that supports the transportation of harvested crops while it follows the user. To move in the furrowed field, ridges and furrows where the robot can move must be detected using robot-mounted sensors and follow the ridge while avoiding the ridges and crop rows. However, when the robot follows the user ahead of it, it has difficulty detecting the ridges because of occlusion by the user. In addition, the robot requires to follow the user and move in and out of furrows of different widths in the farm field. To achieve smooth and safe harvesting support in the field with different width furrows, we propose a method for ridge detection and tracking by measuring the furrow width and interpolation using an RGB-D camera and a method for human-following control while avoiding ridges according to the ridge observation state using fuzzy set theory. To verify the proposed methods, experiments in the farm are carried out.

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Energy-Efficient Path Planning for UAV Using Spatiotemporal Wind Model

Aoki Nobuaki, Ishigami Genya

Journal of the Robotics Society of Japan (The Robotics Society of Japan) 40 ( 3 ) 255 - 258 2022

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 0289-1824

Unmanned aerial vehicles (UAVs) have been deployed in various applications. The UAVs are limited in their flight duration owing to the payload, battery capacity, or wind effects. To address the issue, we propose an energy-efficient path planning method for UAVs that explicitly considers spatiotemporal wind speed. The proposed method exploits the wind-based energy consumption model as the cost function and also implements a variable-resolution grid in the graph search. The simulation study with 648 scenarios confirms the validity of the proposed method. Further, the statistical analysis of the simulation results summarizes the impact of wind and freight mass on UAV path plannings.

Development of Hardware-in-the-loop Simulator for Rough Terrain Mobile Robotand Its Application to Real-time Tuning of Wheel Dynamics Model

Ishikawa Sora, Ishigami Genya

Journal of the Robotics Society of Japan (The Robotics Society of Japan) 40 ( 6 ) 542 - 545 2022

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 02891824

Robotic mobility analysis in rough terrain is essential for mission success on robots deployed on planetary surfaces, construction, or disaster sites. A Hardware-in-the-loop Simulation (HILS) is known as an accurate and feasible method for dynamic analysis. The HILS is the hybrid approach that considers the contact dynamics of a robot in an actual environment and calculates the robot vehicle dynamics in a numerical simulation. We developed a HILS for wheeled mobile robot in loose terrain using a single wheel test bed. The HILS is verified under several traction load conditions. Further, the HILS is coupled with a wheel contact model based on the Dynamic Resistive Force Theory (DRFT). This HILS-DRFT can simultaneously simulate multiple wheels of the mobile robot. To improve the reliability of the DRFT, we implemented a real-time tuning process for the scaling factor used in the DRFT based on the wheel sinkage. The HILS-DRFT with the real-time tuning accurately reproduces the driving force of the actual vehicle.

Perception-aware Receding Horizon Path Planning for UAVs with LiDAR-based SLAM

Takemura R., Ishigami G.

IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems) 2022-September 2022

Research paper (international conference proceedings), Joint Work, Last author, Accepted

This paper presents a perception-aware path planning framework for unmanned aerial vehicles (UAVs) that explicitly considers perception quality of a light detection and ranging (LiDAR) sensor. The perception quality is quantified based on how scattered feature points are in LiDAR-based simultaneous localization and mapping, which can improve the accuracy of pose estimation of UAVs. In the planning step of a UAV, the proposed framework selects the best path based on the perception quality from a library of candidate paths generated by the rapidly-exploring random trees algorithm. Consequently, the UAV can autonomously fly to a destination in a receding horizon manner. Several simulation trials of the photorealistic environments confirm that our proposed path planner reduces pose estimation error by approximately 85 % on average as compared with a purely-reactive path planner.

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CNN-Based Terrain Classification with Moisture Content Using RGB-IR Images

Goto Tomoya, Ishigami Genya

Journal of Robotics and Mechatronics (Fuji Technology Press Ltd.) 33 ( 6 ) 1294 - 1302 2021.12

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 09153942

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.

Simulation Analysis on Steering Characteristics for Vehicle Design of Manned Pressurized Lunar Rover

Naho Yashiro, Genya Ishigami

Proceedings of 20th ISTVS International and 9th Americas Regional Conference 002 (11 pages) 2021.09

Research paper (international conference proceedings), Joint Work, Last author, Accepted

Spatio-temporal prediction of soil deformation in bucket excavation using machine learning

Saku Yuki, Aizawa Masanori, Ooi Takeshi, Ishigami Genya

ADVANCED ROBOTICS (Advanced Robotics) 35 ( 23 ) 1404 - 1417 2021.06

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 0169-1864

This paper proposes a prediction model for three-dimensional spatio-temporal soil deformation in bucket excavation. The prediction model for soil deformation (PMSD) consists of two machine learning processes: the long short-term memory (LSTM) and convolutional autoencoder (Conv-AE). These processes use datasets obtained from an experimental apparatus for bucket excavation developed in this work. The apparatus equips multiple depth cameras that precisely capture time-series data of soil deformation in bucket excavation. The LSTM, an extension of a recurrent neural network, successively predicts three-dimensional soil deformation. The Conv-AE is incorporated to both ends of the LSTM in order to quasi-reversibly compress and reconstruct the datasets so that the computational burden of the LSTM is relaxed. Qualitative and quantitative evaluations of the PMSD confirm the feasibility of time-series prediction of three-dimensional soil deformation. The Conv-AE shows sufficient accuracy equivalent to the measurement accuracy of the depth camera. The prediction accuracy of the PMSD is about 10 mm in most of the cases.

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Visualization and analysis of wheel camber angle effect for slope traversability using an in-wheel camera

Matsumura R, Ishigami G

Journal of Terramechanics (Journal of Terramechanics) 93 1 - 10 2021.02

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 00224898

This paper visualizes and analyzes an effect of a wheel camber angle for the slope traversability in sandy terrain. An in-wheel camera developed in this work captures the wheel-soil contact phenomenon generated beneath the wheel through a transparent section of the wheel surface. The images taken by the camera are then analyzed using the particle image velocimetry. The soil flows with various wheel camber angles are analyzed with regard to the soil failure observed on the slope surface. The analysis reveals that the slope failure and soil accumulation in front of the wheel significantly affect the wheel forces and distributions of the wheel sinkage in the wheel width direction. Further, the side force of the wheel in traversing a slope decreases as the slip ratio increases because the shear stress in the slope downward direction decreases owing to the slope failure.

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Experimental study on wheel-soil interaction mechanics using in-wheel sensor and particle image velocimetry Part I: Analysis and modeling of normal stress of lightweight wheeled vehicles

Tsubaki H, Ishigami G

Journal of Terramechanics (Journal of Terramechanics) 93 23 - 39 2021.02

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 00224898

This study aims to develop a wheel-soil interaction model for a lightweight wheeled vehicle by measuring the normal stress distribution beneath the wheel. The main contribution of this work is to clarify the wheel-soil interaction using a wheel testbed that equips multiple sensory systems. An in-wheel sensor accurately measures the normal stress distribution as well as the contact angles of the wheel. Particle image velocimetry with a standard off-the-shelf camera analyzes soil flow beneath the wheel. The proposed model for the normal stress distribution is formulated based on these experimental data and takes into account the following phenomena for the lightweight vehicles that have not been considered in the classical model: (1) the normal stress distribution takes the form of a Gaussian curve; (2) the normal stress distribution concentrates in the front region of the wheel contact patch; (3) the distribution is divided into two areas with the boundary determined by the maximum normal stress angle; and (4) the maximum normal stress exponentially decreases as the slip ratio increases. Then, the proposed model is experimentally validated. Furthermore, a simulation study for the wheel driving characteristics using the proposed model confirms the accuracy of the proposed model.

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Spatio-Temporal Prediction of Soil Deformation using Machine Learning

Saku Yuki, Aizawa Masanori, Ooi Takeshi, Ishigami Genya

Journal of the Robotics Society of Japan (The Robotics Society of Japan) 39 ( 4 ) 367 - 370 2021

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 0289-1824

Unmanned construction machine working in dangerous environments such as construction sites and disaster areas has been developed. However, it is still necessary to improve its work efficiency especially during bulldozing and excavating soil. This research aims to develop a method for predicting soil deformation using machine learning. The feasibility of the proposed method is verified in a scenario where a simple bulldozing blade excavates soil. In the experiment, soil deformation at a front part of the blade is captured by multiple stereoscopic cameras. The camera provides depth data that are then converted to height field data. This dataset is fed to machine learning using Recurrent Neural Network (RNN) because soil deformation is continuous phenomena depending on time variation. The learned model for predicting soil deformation is confirmed in varied intrusion depth of the bulldozing blade.

Energy-aware trajectory planning for planetary rovers

Sakayori G, Ishigami G

Advanced Robotics (Advanced Robotics) 35 ( 21-22 ) 1302 - 1316 2021

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 01691864

Planetary rovers are becoming indispensable for exploration activities and science missions. The rover used in such mission is often limited in its operation time owing to power and computational resources of the rover. In this paper, a trajectory planning method for a planetary rover is proposed that considers vehicle dynamics, and energy management of the rover. The vehicle dynamics is approximated from a dynamic simulation of the rover, which can estimate the power consumption in accordance with terrain traversability of the rover. The power generation of the solar array panel mounted on the rover is also taken into account. The simulation study confirmed the usefulness of the proposed method, especially in scenarios where slopes could be observed, and one result indicated that the energy margin could be improved by 4.1 kJ, 13.9 (Formula presented.) at maximum.

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