Details of a Researcher - ISHIGAMI Genya

Papers - ISHIGAMI Genya

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Path Planning Method for Soil Investigation using Mobile Robot

Arai Kohei, Ishigami Genya

Journal of the Robotics Society of Japan (The Robotics Society of Japan) 41 ( 9 ) 813 - 816 2023.11

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

A lunar landing base is a key infrastructure for mass transportation from Earth to the Moon and for sustainable activities on the Moon in the future. Unmanned robots are expected to survey the soil stiffness of multiple points for securely constructing the landing infrastructure. There is a trade-off between the time or energy required for the survey, the number of survey points, and the completeness of the survey. This paper proposes a path planning for robotic soil investigation that determines the location and the number of additional survey points. The proposed method aims to obtain completeness of information related to the soil stiffness as well as to reduce the survey time. The simulation study reveals that the method achieves obtaining a certain level of completeness while it reduces the time required for the survey.

Experimental analysis on jumping mechanism using snap-through buckling of an elastic strip

Shuhei Shimizu, Takara Abe, Tomohiko G. Sano, Genya Ishigami

IUTAM Symposium on Nonlinear dynamics for design of mechanical systems across different length/time scales PS08, (2 pages) 2023.09

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

Jumping mechanism of elastic shells on rigid or granular substrates

Takara Abe, Shuhei Shimizu, Genya Ishigami, Tomohiko G. Sano

IUTAM Symposium on Nonlinear dynamics for design of mechanical systems across different length/time scales PS07, (2 pages) 2023.09

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

UV laser etching process: enhancing bending performance in ionic polymer-metal composites (IPMC)

N. Furusawa, K. Soda, N. Matsuhisa, G. Ishigami, J. Ogawa, M. Asai and H. Takahashi

Proc. of the 14th Japan-China-Korea Joint Conference on MEMS/NEMS 2023 2023-P-022 (2 pages) 2023.09

Research paper (international conference proceedings), Joint Work

Path Planning with Cable-obstacles Avoidance for a Tethered Mobile Robot in Unstructured Environments

Ryuki Shimada and Genya Ishigami

Proceedings of the 34th International Symposium on Space Technology and Science (ISTS 2023) 2023-k-04, (8 pages) 2023.08

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

Energy-and-perception-aware planning and navigation framework for unmanned aerial vehicles

Takemura R., Aoki N., Ishigami G.

Advances in Mechanical Engineering (Advances in Mechanical Engineering) 15 ( 4 ) 1 - 17 2023.04

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 16878132

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.

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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.

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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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)) 31 ( 2 ) 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) 2023-May 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

Cr doping-induced ferromagnetism in the spin-glass Cd1-xMnxTe studied by x-ray magnetic circular dichroism

V. K. Verma, S. Sakamoto, K. Ishikawa, V. R. Singh, K. Ishigami, G. Shibata, T. Kadono, T. Koide, S. Kuroda, A. Fujimori

2022.04

The prototypical diluted magnetic semiconductor Cd1-xMnxTe is a spin glass (x<0.6) or an antiferromagnet (x>0.6), but becomes ferromagnetic upon doping with a small amount of Cr atoms substituting for Mn. In order to investigate the origin of the ferromagnetism in Cd1-x-yMnxCryTe, we have studied its element specific magnetic properties by x-ray absorption spectroscopy (XAS) and x-ray magnetic circular dichroism (XMCD) at the Cr and Mn L2,3 edges. Thin films were grown by molecular beam epitaxy with a fixed Mn content of x = 0.2 and varying Cr content in the range of y = 0 - 0.04. Measured XAS and XMCD spectra indicate that both Cr and Mn atoms are divalent and that the ferromagnetic or superparamagnetic components of Cr and Mn are aligned in the same directions. The magnetization of Mn increases with increasing Cr content. These results can be explained if ferromagnetic interaction exists between neighboring Mn and Cr ions although interaction between Mn atoms is largely antiferromagnetic. We conclude that each ferromagnetic or superparamagnetic cluster consists of ferromagnetically coupled several Cr and a much larger number of Mn ions.

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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.

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) 412 LNNS 155 - 164 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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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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