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As a possible extension of a drone application, transportation of a cable-suspended load is expected. The model of a drone with a suspended load is a nonlinear underactuated system that is known to be difficult to analyze and control. This paper applies the linearization method, known as hierarchical linearization, to the system. We observed that, via the hierarchical linearization scheme, the system can be linearized exactly and the controller can be designed simultaneously. There are two features of this approach. First, the controller exactly considers the system nonlinearity, and the feedback controller is based on the linear control theory. Second, it is possible to derive the analytical solution of the closed-loop system. We have demonstrated these features via numerical simulations.

Object tracking is widely utilized and becomes indispensable in automation technology. In environments containing many objects, however, occlusion and false recognition frequently occur. To alleviate these issues, in this paper, we propose a novel object tracking method based on moving horizon estimation incorporating probabilistic data association (MHE-PDA) through a probabilistic data association filter (PDAF). Since moving horizon estimation (MHE) is accomplished through numerical optimization, we can ensure that the estimation is consistent with physical constraints and robust to outliers. The robustness of the proposed method against occlusion and false recognition is verified by comparison with PDAF through simulations of a cluttered environment.

In this paper, a formation control for quadcopters connected by cables is considered. The quadcopters need to flight by avoiding the crush among each other and by keeping the distance constraint due to the cable. Moreover, the avoidance of contact against walls should be taken into account since the system is assumed to flight an indoor environment. To hold these several constraints, the model predictive based controller is proposed, and the effectiveness of the controller is verified via numerical simulation.

Leg/wheel mobile robots have a hybrid mechanism combining legs and wheels. Those robots are expected as a transporter in an extraterrestrial environment because leg/wheel mechanisms have the potential to move on rough terrain efficiently. A controller is required adapting to the complex geometry of the ground. This paper presents a leg configuration control based on model predictive control considering load distribution using the state equation which includes steering. Numerical simulations are conducted to verify the advantage of the proposed method. The robot successfully moves on the slope with a bank angle while improving the load distribution.

In this paper, moving obstacle avoidance in narrow environment is studied. Recently, electric wheelchairs are widely used, which has independent driving wheels modeled as a two-wheeled nonholonomic vehicle model. One of the challenges on obstacle avoidance in real environment is that the obstacle position observed by sensors is usually fluctuated due to noise; it is difficult to pass through the obstacles safely. To overcome this difficulty, we apply time-state control form to divide the system into two subsystems: path dynamics and velocity dynamics so that we can separate the path planning from velocity control, which enables us to transform each subsystem into linear ones. The path tracking is achieved by linear control synthesis design, while running velocity can be tuned depending on the distance to the obstacles. Then we apply model predictive control (MPC) for each subsystem in order to realize obstacle avoidance and optimal velocity control. We impose constraints on MPC representing prohibited area for obstacle avoidance on path following control. In this paper, we show effectiveness of the proposed method by numerical simulations assuming a real electric wheelchair. In the neighborhood of obstacles, the vehicle decelerated in accordance with the distance to the obstacle. It is shown that the effectiveness of the proposed method is supported by numerical simulations.

In this paper, we propose an attitude control law for underactuated two-wheel spacecraft under non-zero total angular momentum. Attitude control with non-zero total angular momentum is complicated ...

Vision sensors are widely used not only for detection and recognition, but also measurement of the pose of the moving objects. But in the crowded environment, occlusion often disrupts the measurement, and wrong data association due to misrecognition deteriorates the tracking performance. Probabilistic data association filter (PDAF) is known as useful to address such issues, in which observed features are weighted by probability to deal with multiple observations as well as to cope with occlusion and false recognition. This paper presents a novel object tracking method in which PDAF is incorporated into moving horizon estimation (MHE) framework to deal with multiple frame tracking and physical constraints. The performance of the proposed method is evaluated by comparing with the PDAF.

Recently, wide-area and long-term observation of the planet surface using unmanned exploration rovers have been actively conducted. Exploration rovers require autonomous control in a severe environment. In particular, since exploration rovers operate in an environment with few power sources, appropriate power management is important. In this study, we model the electric energy balance of exploration rovers. Then, exploration rovers achieve trajectory tracking control considering the electric energy balance by evaluating power balance and tracking performance based on economic model predictive control. We verify the effectiveness of the proposed method through numerical simulations in which we assume that the exploration rover moves on the lunar terrain. As a result, it is shown that the exploration rover achieves the behavior considering electric energy balance. Thus, we realize economic model predictive control considering electric energy balance for exploration rovers by the proposed method.

Optical motion capture system can measure the position and the orientation of an object equipped with retroreflective markers. When some markers are not observed due to occlusion, the system often fails to localize the object. In our past study, moving horizon estimation (MHE) is applied to improve the estimation robustness, where states are determined so that the sum of error magnitude of dynamics and observation is minimized under constraints. In general, marker association is crucial for high accuracy estimation. In this paper, we propose a marker association method for MHE where the proposed association method exploits a potential function and optimization so that the association is robust against marker occlusion and fake images similar to the marker. In the simulation, for the environment where the previous method fails due to missing markers and misrecognition, we confirm that the proposed method can successfully realize the estimation.

In this paper, we propose the coverage control for multi-copter type Unmanned Aerial Vehicle (UAV) with avoidance of local optimum and collision. As an effect of coverage control, the optimal deployment of agents according to arbitrary distribution could be calculated. However, there are problems with coverage control. One of them is that fallen into the local optimum solution owing to biased initial position or target. And the other one is collision since coverage control did not consider the size of agent. In this paper, we attempt to solve the local optimum by gradually changing the distribution density map which represents target deployment. And Voronoi diagram with void which considers the size of agents could solve the collision. This paper verifies the validity of proposed method with numerical simulation, comparing random walk and simulated annealing for local optimal problem, and the artificial potential method for collision.

In this paper, we propose an obstacle avoidance method considering both the shape and dynamics of robot and the motion of obstacles, using model predictive control (MPC) and fuzzy potential method (FPM). FPM can deal with the shape of robot, but it cannot treat the dynamics of robot. On the other hand, MPC can deal with constraints including the dynamics of both robot and obstacles. The predicted movable range of obstacles is used as a prohibited region for MPC. Thus, combining FPM and MPC, the proposed method achieves moving obstacle avoidance. The effectiveness of the proposed method is verified through numerical simulations.

For planetary rovers, a path planning with reduced risk is critical to realize safety and efficient locomotion on uneven terrain. To realize such locomotion, it is important to combine the local information observed by on-board sensors and global geometry obtained from, for example, satellites. In this research, we focus on a motion planning integrating these geometric information with two different levels of resolutions. In this research, we interpolate DEM data using low-order function and generate the cost map which indicates the error between interpolation function and grid data. The rover is navigated to avoid the area with large error where the elevation changes drastically. We present an avoidance method based on the dissipation function related to the map error. The rover can avoid the hazardous areas and move economically using the optimal calculation and the cost map. Through numerical simulations, we compare the proposed method with two generally known search algorithms in order to verify the superiority of the proposed method.

Localization is important to achieve safety motion of autonomous mobile robots. In this paper, we propose a model predictive control which prevents vehicles from falling into a singular localization environment with less features. We assume to use a laser range finder (LRF) as a sensor to obtain two dimensional point-cloud data of the surrounding environment and apply map-matching method for localization. We can calculate a covariance matrix of localization error using Fisher information matrix. The maximum eigenvalue of this covariance matrix is used as an index of uncertainty of localization. Then, we extract regions having large uncertainty for localization to construct prohibited regions. The robot avoids singular environments and regions with significantly low estimation accuracy by considering the prohibited regions represented as inequality constraints for model predictive control (MPC). We show the effectiveness of the proposed method through numerical simulations which simulate singular environments indicating a corridor or a large floor where only degenerated information is available.

This paper proposes a formation control method for multi quadcopters considering collision avoidance using model predictive control (MPC) in three dimensional space. To achieve the formation control, the term of consensus is included in MPC. Collision avoidance among control objects is considered by adding the term of repulsive force to index function of MPC. Thus this paper achieves formation control by combining consensus based control with collision avoidance control in MPC. In general, the repulsive force is described as a nonlinear function. It is difficult to add the nonlinear function to the term of index function because the computation time can increase. Hence it's not appropriate to implement to practical system. Thus the term of repulsive force is approximated as a linear function to achieve real-time control. In addition, the model of control object is assumed as linear system. The validity of proposed method is verified via experiment with three quadcopters in three dimensional space.

In this paper, a novel linearization based nonlinear controller is developed for a dynamical model of a quadcopter including a rotor dynamics. As the main contribution, it is revealed that the 17-dimensional quadcopter system is exactly linearizable by hierarchical linearization which was introduced by the author, although the quadcopter system isn't feedback linearizable in the sense of ordinary input-output linearization. The proposed linearization method first linearizes altitude dynamics and designs a feedback controller for the linearized subsystem. By fixing the structure of a controller for linearized subsystem, it's revealed that the remaining dynamics becomes fully linearizable. As the result, the system is fully linearized without any approximation by static feedback transformation, and the stabilization controller is designed for the linearized system. The validity of the proposed controller is verified via a numerical simulation.

Leg/wheel robots has a hybrid movement mechanism which combines leg and wheel mechanism. In this paper, we propose a posture control for three-dimensional leg/wheel robots in which the distance between zero moment point and center of gravity are minimized using model predictive control. It is expected to prevent overturning of the robots based on prediction and considering zero moment point. The proposed method can be applied to the robots with redundant degrees of freedom. We show the superiority of the proposed method through simulations of multiple body dynamics.

When leg/wheel mobile robots are controlled by model predictive control (MPC), the optimization is too complex for an embedded CPU. Then, we proposed a method which reduces computational complexity by sequential optimization for the models partitioned into each leg. However, robustness of the proposed method was not evaluated enough in the disturbing environment. In this paper, we conduct simulations on the inclined plane to evaluate the robustness. Through the simulations, the robustness of the proposed method is improved by changing the control cycle and the predictive length.

Localization with laser range finder (LRF) needs an environment map. In some environments, the localization using the environment map prepared in advance often fails. In this study, we achieve localization using both the environment map and the feature of supplemented objects as the estimated map. Moreover, to suppress the influence of the observation noises of LRF, we estimate the feature of supplemented objects using the extended Kalman filter (EKF). In this paper, we verify the influence of the estimated map on estimation accuracy through the simulation and verify the effectiveness of this localization method through the experiment conducted in a gymnasium where is larger than the scanning range of LRF.

This paper combines coverage control, random-walk and artificial potential method to deploy multi-copter type Unmanned Aerial Vehicles (UAVs). With the coverage control, it is able to deploy agents in the coverage area with arbitrary distribution based on the purpose of control. However, when initial position of agents concentrates on one location, it could be fallen into the local optimal solution (local optimal problem). While the local optimal problem, agents could not achieve the distribution based on the purpose of control. Meanwhile, random-walk takes vectorial angle randomly and move constant distance. By using this movement, it is possible to disperse the initial position of UAVs. Therefore, the local optimal problem could be solved by combining random-walk to coverage control. Moreover, using the artificial potential method as a repulsive force to avoid the collision between agents. This paper verifies the validity of proposed method by numerical simulation.

In this paper, inter-vehicular distance control method is applied to the small vehicles such as automatic guided vehicles (AGVs). Path tracking and attitude angular error will turn up when vehicles take a detour to avoid obstacles, however, this phenomenon has not been explicitly considered in conventional methods. In this paper, we propose target velocity modification considering path tracking and attitude angular error and assess the effectiveness of it quantitatively by numerical simulation in a situation of obstacle avoidance.

In this paper, we apply the hierarchical model predictive tracking control for real robot and verify the travelling performance. We deal with the independent four-wheel driving/steering vehicle (IFWDS). This vehicle is equipped with the four coaxial steering mechanisms (CSM). It is composed of two steering joints which have different movable range and rotational speed. In our previous study, we achieved the improvement of tracking performance by optimal allocation of steering angle for CSM in simulation. In this study, we verify that we can obtain the same result in experiment.

Motion capture systems are widely used for measuring the position and the orientation of objects. It detects the markers attached to the objects, but they are sometimes lost sight by occlusion and dead angle of sensor. It becomes a problem that markers to determine the coordinates of the object are not measured. Thus, it is necessary to estimate state suppressing the influence of missing measurement. In this study, we propose a Moving Horizon Estimation for state estimation to tackle this issue. If any markers are not measured because of occlusion, MHE can estimate position of the robot uniquely because it optimizes the evaluation considering the error of available measurement and motion dynamics of the vehicle. In our previous study, we applied MHE for estimation of the position and heading angle of the vehicle robot with markers. In this paper, we will show the application of it to the visual feedback control for the robot using estimation value at real time. we will prove that MHE is effective because it can suppress the influence of occlusion and rapid change of input.

A safety technology about unmanned aerial vehicles(UAVs) attracts a lot of attention. This paper presents a robust formation control method for multi agent system (MAS) to suppress the influence of disturbance and maintain the formation robustly by sharing the disturbance information. This paper estimates the disturbance by the disturbance observer. UAVs share these disturbance information using network of MAS. The inputs are calculated by model predictive control(MPC) that predicts the future motion of each UAV considering a shared disturbance information. UAVs share the disturbance information and the future motion of UAVs predicted by MPC using network of MAS. The proposed method achieves the robust formation control to consider these future information in MPC. This paper verifies the validity of proposed method via simulation.

In our previous study, we proposed a localization method based on moving horizon estimation (MHE) for known environments. However, in the method the environment map is required for achieving the localization. So in this study, to achieve the localization in unknown environments, we propose a localization method integrating scan-matching and odometry based on MHE; the vehicle pose is estimated by minimizing both errors of scan-matching and odometry, simultaneously. The localization is achieved even in the environment that the pose cannot be localized uniquely by only the scan-matching method since the information for the estimation is complemented by the odometry. We verify the effectiveness of the proposed method through the off-line localizations using the experimental data. The superiority of the proposed method is shown in a comparison with an extended Kalman filter based method.

There is a demand for autonomous driving control in front-wheel steering vehicles because it is expected to make driving safer and easier and also to reduce the driving workload. In order to perform safe driving with autonomous driving control, it is necessary to consider unexpected disturbances when the vehicle is moving and that tire forces have limitations. We propose autonomous driving control combining Model Predictive Control (MPC) and Sliding Mode Control (SMC). In this paper, we employ MPC in order to consider the maximum tire forces. SMC is employed to deal with unexpected disturbances that the model has not anticipated. Furthermore, we confirmed that path following control is possible by practical inspection using a small front-wheel steering vehicle that is susceptible to unexpected disturbances.

In this study, we apply a hierarchical model predictive control to omni-directional mobile vehicle, and improve the tracking performance. We deal with an independent four-wheel driving/steering vehicle (IFWDS) equipped with four coaxial steering mechanisms (CSM). The coaxial steering mechanism is a special one composed of two steering joints on the same axis. In our previous study with respect to IFWDS with ideal steering, we proposed a model predictive tracking control. However, this method did not consider constraints of the coaxial steering mechanism which causes delay of steering. We also proposed a model predictive steering control considering constraints of this mechanism. In this study, we propose a hierarchical system combining above two control methods for IFWDS. An upper controller, which deals with vehicle kinematics, runs a model predictive tracking control, and a lower controller, which considers constraints of coaxial steering mechanism, runs a model predictive steering control which tracks the predicted steering angle optimized an upper controller. We verify the superiority of this method by comparing this method with the previous method.