A large-scale dump truck that automatically transports earth and sand in cooperation with a human-operated backhoe is of interest to the construction industry. A human-operated dump truck generally drives slightly past the desired loading position and then backs up to it for loading the sediment. The turning and loading positions are subjectively decided according to the working posture of the backhoe and the surrounding environment, and the safety margin of cooperative works. Backhoe operators want to perform the same maneuvers for human-operated/automated dump trucks. The movements of the autonomous vehicle should be similar to those of a human-operated one. However, it is difficult to derive a human-like path that does more than minimize costs. This study proposes a path-planning method that generates a path including a turning back, according to the changing backhoe position and surrounding conditions. We modeled the positional relationship during loading between a backhoe and dump truck, determining the loading and turning positions and related parameters from operational data collected in trials with human-operated construction vehicles. The proposed method allowed the autonomous dump truck path to resemble a human-like one. The authors have retrofitted an existing large-scale six-wheeled dump truck for automatic operation. Automatic loading in cooperation with a human-operated backhoe was realized all 17 times using the retrofitted dump. The average stopping accuracy was 0.57 m and 9.7°.

Backhoe loads sediment onto the bed of dump trucks during earthmoving work. The prediction of backhoe loading time is essential for ensuring safe cooperation between the backhoe and dump trucks. However, it is difficult to predict the instant at which the backhoe is ready to load sediment, because of the similarity in motions observed during gathering sediment. Moreover, since operators have different skill levels, the prediction requires a unique model for each operator. In this study, we attempt to predict the instant at which the backhoe is ready to load sediment into the dump truck. For this purpose, the beta-process hidden Markov model (BP-HMM) is employed to build a backhoe motion model for a specific operator. Time series data of backhoe loading motions for crushed rocks and wood chips, which were measured using 6-axis inertial measurement unit (IMU) sensors equipped at the cab, boom, and arm of the backhoe, were used for modeling with the BP-HMM. Several primitive motions of the backhoe, which occur at the completion of preparation before the loading process begins, were discovered as a result of the motion modeling based on the BP-HMM. We developed the prediction of the instant using three primitive motions. At best, the proposed method could predict the instant with a probability of 67% and 100%, at 6.0 s and 0.7 s before the loading motions began, respectively. This phased prediction can be used to reduce the idle time and risk for dump trucks during earthmoving work with the backhoe.

In large industrial plants, the inspection of production lines is a heavy and costly task that puts human inspectors at high risk. In order to overcome these challenges, we have developed an autonomous plant inspection system using a mobile tracked vehicle. In this paper, we propose an autonomous navigation method for tracked vehicles based on a contact force model that enables the robot to compensate for collisions with obstacles. The model considers the influence of the contact force on the linear and angular motion of the robot. Using the model, the controllable velocity range is derived during collisions. The experimental results show that the robot is safely controlled by complying with velocity constraints. In addition, our method can generate motions such as leaving wall, L-shaped curve and crosswise locomotion in straight passage while navigation alongside the walls. The method allows the robot to smoothly follow a target path, despite colliding with obstacles.

We have developed a method to visualize search and rescue (SAR) dogs' activities from sensor data recorded by the SAR dogs' sensor vests. This paper proposes two methods for detecting continuous barking actions of SAR dogs, which locate victims by smell and then bark continuously to tell handlers where victims are located. Continuous barking action is detected from audio information and a dog's body motions. This detection method is based on dynamic time warping (DTW), which has been used successfully to analyze human audio information. Cyclic body motion was observed during dogs' barking motions. This cyclic motion can be detected by an inertial measurement unit (IMU) attached to the vest. A fast Fourier transform (FFT) is used to analyze a dog's barking motion. The proposed detection methods were evaluated using audio and IMU data recorded during actual SAR dog training sessions. The F-scores of the audio and motion-based barking detection methods were 0.95 and 0.90, respectively. As a trial, we marked victim locations on a map based on the body motion.

Hyper-flexible elements like tethers and ropes will have various possibilities of novel manipulations. In this paper, casting and winding manipulation of hyper-flexible manipulators (HFM) is considered. The HFM is modeled and analyzed as an underactuated multi-link system connected by non-elastic passive joints. An iterative revision method of parameter identification and state estimation is proposed. The method obtains highly credible parameters and state profiles from comparatively roughly visually sampled data using dynamic simulation. Succeeded and failed results of casting motion are analyzed by simulation and an energy-like function of position and velocity of HFM tip is defined. A control method to determine actuation timing to efficiently increase the energy-like function is proposed.

Non-elastic hyper-flexible elements like tethers and ropes will have various possibilities of unconventional manipulations. In this paper, casting and winding manipulation of hyperflexible manipulators (HFM) is proposed. The HFM is modeled as an underactuated multi-link system connected by non-elastic passive joints, and simulation results with dynamic parameters heuristically determined corresponding to the experimental results show the validity of the proposed non-elastic model. The simulation results also correspond to the experimental ones with actuation at the base joint. Observations on the simulation results is derived to reveal a better casting actuation to succeed in winding to an object. Then, single swing actuation for winding is proposed and verified in simulations

Manipulators with free joints are second-order nonholonomic systems whose dynamical constraints are nonintegrable. Such systems are known to be under-actuated systems which can be controlled by less actuators than the dimension of the configuration space. Previously proposed methods to control free-joint manipulators have been based on the assumption of perfectly frictionless free joints. In this paper, averaging analysis of 3R manipulators with one actuator clarifies that their frictionless models are Hamiltonian systems with conservation of an energy-like quantity. From experiments and simulations, 3R free-joint manipulators with friction at the free joints show dissipative behaviors converging to an equilibrium point. A convergence control method using the energy-like quantity to stabilize to the equilibrium point is proposed.

A manipulator with free joints is a class of under-actuated mechanisms. Control of such systems is one of major topics in robotics and control engineering. In this paper, we apply the averaging method to manipulators connected by free joints and describe their behaviors in response to periodic inputs. We analyze 2R and 3R free-joint manipulators and show that each system has invariant manifolds defined by an energy-like conserved quantity. We also develop a control method via feedback modulation of the input amplitude to reach a desired invariant manifold. The effectiveness of the method is verified by computer simulations.

The nonlinear motion of underactuated mechanisms has drawn recent interests of researchers. Underactuated mechanical systems are often subject to so-called nonholonomic constraints which are related to many both theoretical and practical issues. We first analyze the nonlinear behavior of a two-joint planar manipulator with the second joint free, from nonlinear dynamics point of view. We then discuss the simultaneous positioning of both joints. We use a time-periodic input and propose an amplitude modulation of the feedback error. The analysis via the Poincare map shows that the behavior becomes chaotic with large amplitude. The effectiveness of the proposed positioning control is verified by experiments.

Nonholonomic system is now one of the major topics in robotics. In this paper, we discuss the nonlinear behavior of a two-joint planer manipulator with the second joint free for time-periodic inputs. First, we illustrate that when the amplitude remains small, the Poincare map of the system follows an ellipse-like closed path and it becomes chaotic with large amplitude. We then propose a control method to position the both joints via amplitude modulation of the position error. The effectiveness and robustness of the control method are verified by experiments.

A free-flying space robot with a 6-DOF manipulator cannot follow an arbitrary trajectory in the 9D generalized coordinates (3 of the satellite orientation and 6 of the manipulator) with only manipulator joint control, though it was shown to be commonly controllable in the literature. In this paper, we propose a method to approximate the desired 9D path by introducing a perturbation around it. We call the approximated trajectory the "spiral motion". A computational scheme for planning the spiral motion is presented, and is followed by computer simulation that illustrates the effectiveness of the scheme.