Published papers

This paper proposes a method that can estimate and classify the magnitude of wheel slippage for a mobile robot in sandy terrains. The proposed method exploits a sensor suite, called an in-wheel sensor, which measures the normal force and contact angle at the wheel-sand interaction boundary. An experimental test using the in-wheel sensor reveals that the maximum normal force and exit angle of the wheel explicitly vary with the magnitude of the wheel slippage. These characteristics are then fed into a machine learning algorithm, which classifies the wheel slippage into three categories: non-stuck wheel, quasi-stuck wheel, and stuck wheel. The usefulness of the proposed method for slip classification is experimentally evaluated using a four-wheel-drive test bed rover.

This paper presents a wide-range routing method for multiple rovers deployed for a lunar polar exploration mission. In the mission scenario considered in this paper, multiple exploration points are divided into several sets of points to be assigned to multiple rovers. Subsequently, routes between the exploration points are generated for each rover. This assignment and routing problems are well known as the multiple Traveling Salesman Problem (mTSP). The scenario aims to minimize two objective functions: the sum and standard deviation of operation time among the multiple rovers. This problem is then subject to the multi-objective mTSP (MOmTSP). Therefore, the proposed method is composed of a three-step procedure for solving the MOmTSP. First, the k-means algorithm divides an exploration area into several regions for the number of rovers. Subsequently, a routing algorithm with the space-filling curve and 2-opt algorithm determines the order of exploration points in each region. A local path planner then generates a feasible path between two exploration points. The simulation results using the proposed method with 80 cases are statistically analyzed. We confirm that the proposed method solves the multiple optimization problems by finding the routes that can equalize the rover's operation time and minimize their deviations.

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.

レポート番号: G-10

Soft actuators are constrained in their deformation when subjected to loads, so their performance needs to be evaluated similarly to that of conventional motors. In this study, we propose a seesaw-type force sensor consisting of a beam with multiple load points having different spring constants and a laser displacement meter. The relationship between force and displacement is evaluated by applying force from a soft actuator and measuring the displacement. The load points of the fabricated seesaw-type beams had spring constants ranging from 1.2 N/m to 15.9 N/m. The soft actuators were ionic polymer-metal composite (IPMC) actuators with a size of 5 mm × 25 mm and a thickness of 190 μm. In the experiment with the IPMC actuator, the force was high and the displacement was small at 5.3 mN and 0.3 mm where the spring constant was large, and the opposite trend was observed at 2.3 mN and 1.9 mm where the spring constant was small.

This paper describes a swimming robot that imitates a flatworm. The model was made with a length of 20 cm, which is about twice the size of a flatworm. As for the folds, they were reproduced by flat wing waves that mimicked the flatworm using a single motor and a phase-shifting slider-crank mechanism. In this way, the phase speed of the folds can be freely changed by changing the phase difference of the crank. The performance of the fabricated robot was evaluated using a high-speed camera and tracking software. For the flapping amplitude, the ornithopter showed no significant errors other than misalignment of parts.

This paper describes a swimming robot that imitates a flatworm. The model was made with a length of 20 cm, which is about twice the size of a flatworm. As for the folds, they were reproduced by flat wing waves that mimicked the flatworm using a single motor and a phase-shifting slider-crank mechanism. In this way, the phase speed of the folds can be freely changed by changing the phase difference of the crank. The performance of the fabricated robot was evaluated using a high-speed camera and tracking software. For the flapping amplitude, the ornithopter showed no significant errors other than misalignment of parts.

The construction of lunar infrastructure is crucial for future human activities. Unmanned vehicles, such as rovers, serve as key mobility systems for construction tasks but face the risk of becoming trapped by sand and immobilized on uneven terrains. This paper focuses on a scenario where a rover being stuck is rescued by another rover via a robotic manipulator. We design and prototype a two-link robotic manipulator and conduct dynamic simulation along with experimental demonstrations. The experimental result are generally consistent with simulation results, particularly in terms of identifying the energy-efficient configurations of the manipulator for effective rescue operations.

In recent years, the effectiveness of autonomous robots on planet surfaces and in crisis situations have been recognized. During planetary exploration missions, it is crucial to plan a route where the planetary rovers can reach its destination safely. However, when these rovers are traversing on the trail, the estimated driving force does not always match that in the real world. This can result in the rovers deviating from its planned course. This paper proposes a new cost function that takes the rover deviation into account and evaluates this method using multi-point path planning simulations. A grid-based path planning method is used with Dijkstra 's algorithm as it 's foundation.

Sampling-based search algorithms such as Rapidly-Exploring Random Trees (RRT) have been utilized for mobile robot path planning and motion planning in high dimensional continuous spaces. This paper presents a path planning method for a planetary exploration rover in rough terrain. The proposed method exploits the framework of a sampling-based search, the optimal RRT (RRT∗) algorithm. The terrain geometry used for planning is composed of point cloud data close to continuous space captured by a light detection and ranging (LIDAR) sensor. During the path planning phase, the proposed RRT∗ algorithm directly samples a point (node) from the LI-DAR point cloud data. The path planner then considers the rough terrain traversability of the rover during the tree expansion process of RRT∗. This process improves conventional RRT∗ in that the generated path is safe and feasible for the rover in rough terrain. In this paper, simulation study on the proposed path planning algorithm in various real terrain data confirms its usefulness.

© 2020 ISTVS This paper reports a vision-based technique of measuring the spatio-temporal deformation of excavated soil for estimating the bucket resistive force. The proposed measurement technique uses two depth cameras to determine three-dimensional soil-surface displacement. The technique consists of the following two processes: the first is related to image correlation between the two cameras, and the second involves data filtering and smoothing for generating soil deformation as a continuously curved surface. The proposed technique delivers measurement accuracy to the nearest centimeter. Typical experimental results of the three-dimensional measurement of soil deformation using the proposed technique are presented in the paper. Further, this study updates an interaction model for the resistive-force estimation while a bucket excavates soil. The model introduces a correction variable that changes with the bucket wrist angle by exploiting the experimental measurement of soil deformation. The model estimates the resistive force with an error of less than one quarter of the maximum force. These updates also exhibit the effectiveness of the proposed technique.

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.

Newly proposed missions to the Moon/Mars often require vehicles to operate at high speed for time-efficient surface explorations. Among wheel-terrain interaction models in the past being applicable for quasi-static locomotion, a granular Resistive Force Theory (RFT) is increasingly employed alternatively for its simplicity and applicability. The RFT has also been extended to the Dynamic RFT (DRFT), which considers the rate-dependent effects often seen in the high-speed locomotion. In this research, first, we validate the DRFT through single-wheel experiments. The results suggest that the original DRFT underestimates the influence of wheel rut and that the granular flow beneath the wheel may decrease the wheel traction force. We then propose an empirically modified DRFT (m-DRFT), which considers an appropriate evaluation of the wheel rut depth and a simplified model for the granular flow. The comparison with the experimental results confirms that the m-DRFT accurately predicts the wheel drawbar pull in relatively low slip conditions while the original DRFT overestimates it. Moreover, the m-DRFT reproduces the temporal oscillations of the drawbar pull generated by the wheel grousers. These results imply that the m-DRFT is applicable for transient locomotion state or even fast-rotating wheel where estimating the sequential changes of wheel forces becomes essential.

This paper presents a framework for trajectory planning that explicitly considers robotic traversability based on a quasi-dynamic vehicle model of a mobile robot in loose soil. The quasi-dynamic model estimates the slip effect due to wheel-terrain interaction forces regardless of solving complicated multibody dynamics. Therefore, our proposed model is computationally efficient for quantifying how the robot safely traverses each trajectory segment generated by a planning algorithm. The trajectory planning in our framework exploits a sampling-based incremental search algorithm, i.e., Closed-Loop Rapidly-Exploring Random Trees (CL-RRT). In the tree extension process of the CL-RRT, the traversability assessment based on the quasi-dynamic vehicle model excludes the trajectory segment associated with a hazardous wheel slip ratio. As a result, a trajectory generated from the proposed framework is safely traversable for the robot even in high slip terrain. Simulation results show that the proposed vehicle model can run 57K times faster than the dynamic model and predict the robot motion 3 times more accurately than the kinematic model. Multiple trials of the trajectory planning simulation show that our proposed framework incorporated with the quasi-dynamic model reduces a wheel slip ratio by about 40 % as compared with the kinematic model.

In this paper, a trafficability analysis for the exploration rover is described. A rover traveling on loose terrain often experiences its slippages (wheel slips or vehicle sideslip), and in particular, these slips become larger when the rover traverses on sandy slopes. The authors have investigated traction mechanics of a rigid wheel of the rover on loose terrain with paying attention to slipping behaviors of the wheel. In this paper, based on our previous works regarding the wheel-terrain mechanics, we propose a Thrust-Cornering Characteristic Diagram for trafficability analyses of the rover. The thrust-cornering characteristic diagram consists of various characteristics curves of wheel forces, namely thrust and cornering forces, with various wheel slippage conditions. This diagram provides quantitative criteria for slope traversing capability of the rover on arbitrary angles of slope. The usefulness of the proposed diagram for the trafficability analysis is confirmed through slope traversal experiments using a four-wheel test bed. Further, a steering maneuver control for slope traversing situation is discussed based on the diagram. ©2008 IEEE.

For a mobile robot it is critical to detect and compensate for slippage, especially when driving in rough terrain environments. Due to its highly unpredictable nature, drift largely affects the accuracy of localization and control systems, even leading, in extreme cases, to the danger of vehicle entrapment with consequent mission failure. This paper presents a novel method for lateral slip estimation based on visually observing the trace produced by the wheels of the robot, during traverse of soft, deformable terrain, as that expected for lunar and planetary rovers. The proposed algorithm uses a robust Hough transform enhanced by fuzzy reasoning to estimate the angle of inclination of the wheel trace with respect to the vehicle reference frame. Any deviation of the wheel trace from the planned path of the robot suggests occurrence of sideslip that can be detected, and more interestingly, measured. This allows one to estimate the actual heading angle of the robot, usually referred to as the slip angle. The details of the various steps of the visual algorithm are presented and the results of experimental tests performed in the field with an allterrain rover are shown, proving the method to be effective and robust. ©2008 IEEE.

In an extreme environment, such as Mars or a volcanic area, mobile robots have been used in scientific missions, or as precursors for a future manned mission. The robot called a planetary exploration rover is managed by a space-qualified, radiation-hardened, and low-clock onboard computer, and autonomously travels over challenging terrain.