This paper proposes a simple controller for bipedal locomotion that can stabilize three-axis (roll, pitch, and yaw) rotation without relying on ground reaction moment manipulation. Extra acceleration of the CoM (center-of-mass) from the nominal DCM (divergent component of motion) dynamics generates moment around the CoM. Based on this principle, the behavior of the desired DCM is modulated to carry signal for rotation stabilization. A robust walking controller is synthesized by combining the proposed rotation stabilizer with continuous step adaptation. Simulation study shows that the proposed controller is capable of robust disturbance rejection and yaw-regulated walking of a point-foot robot.

Remotely controlled hydraulically driven robots are expected to play an important role in extreme environments such as disaster sites, and force feedback is effective for improving the fidelity of the remote environment and the work efficiency. However, it is not reasonable to attach a force sensor directly to the end-point of a hydraulically driven robot. In a previous study, the authors showed that the impact forces, which are important information to improve the fidelity of the remote environment and work efficiency, can be estimated by using the information of acceleration of each link in addition to the cylinder pressures. In this paper, we investigated how many accelerometers and where those accelerometers should be attached on each link by using an index called GDOP (Geometric Dilution Of Precision) to improve the accuracy of impact force estimation. The experimental results show that although the estimation accuracy could not be improved significantly by rearranging the accelerometers, the effect of reducing the noise of the estimated load due to the sensor noise was confirmed.

This paper presents a control system for autonomous driving based on MPC in which driving style can be easily modified by changing control parameters. Each of the motion controls for the longitudinal and the lateral direction are formulated as the model predictive control problem. Finally the experimental verification by using driving simulator and a real electric vehicle is performed by implementing MPC on each platform, and it is confirmed that the proposed system can produce a large variety of driving characteristics. The implemented MPC package will also be beneficial to the developers and researchers in various fields other than control engineering field.

This paper presents cooperative adaptive cruising control of multiple cars in automated/un-automated mixed traffic. In order to take account of un-automated cars, the vehicle maneuver is expressed as a PrARX model that is a continuous approximation of hybrid dynamical system. The PrARX model describes the driver's logical decision making as well as continuous maneuver in a uniform manner. The acceleration inputs of automated vehicles are computed in model predictive control framework where the state equation includes a platoon of automated and un-automated cars coupled with PrARX driver models. For computing assisting outputs in real time, a fast computation method for nonlinear model predictive control based on the continuation technique is employed. Simulation studies of the proposed CACC system indicates that explicit prediction of un-automated cars improves the overall stability of the platoon.

This paper presents an autonomous lane tracking that reflects different driving characteristics using model predictive control (MPC). We consider that human driver minimizes a cost function depending on his/her skill, experience, and preference on driving. The cost function of MPC can be used to model personal driving characteristics. To identify the parameters of cost function, at first, we analyze the difference of driving characteristics between skilled and un-skilled drivers through experiments of driving behavior on a driving simulator (DS). Next, we introduce "meta-performance indices" that can evaluate human driving data of experiment and results of autonomous driving. These parameters are expected to express the driving characteristics as a group, i.e., not tuned to personal driver. Finally, the validity of the proposed system is verified.

This paper presents a trajectory planning method for automated parking. The proposed method constructs a state roadmap in which each node contains not only position but also orientation information of the vehicle. The roadmap is constructed by dividing the orientation space in multiple resolutions considering the non-holonomic constraints of the vehicle and the collision-avoidance constraints between the vehicle and the boundary of the parking environment. Using the state roadmap, a complex parking trajectory composed of both forward and reverse motions can be computed with small online computation cost. The proposed method is evaluated in both numerical simulations and an experiment using an electric vehicle.

This paper presents a trajectory-planning method for multibody systems. Trajectory planning of a multibody system is formulated as a constraint-solving problem on a set of variables expressing the motion of the multibody system over a unite-time interval. Constraints express the dynamics of rigid bodies, kinematic conditions of joints, various range limitations, as well as achievement of tasks, and they can be assigned different priority levels. The prioritized constraint-solving problem is then treated under the framework of lexicographical goal programming, where the local optimality of the problem is characterized in terms of Pareto efficiency condition. Based on this observation, an algorithm that iteratively updates the variables toward a locally optimal solution is derived. The proposed method is evaluated in simulation examples.

This paper presents a novel personalized driver assistance system(PDAS) based on the model predictive control(MPC) together with a continuous/discrete hybrid dynamical system model of the driving behavior. First of all, the driving behavior is identified as the piecewise ARX model. Then, it is explicitly embedded in the optimization problem for finding the optimal assisting output. Since the driving behavior includes some binary variables, the optimization problem is formulated as the mixed integer programming. Some adaptation mechanism to accommodate to the change of the situation is particularly discussed. Finally, the proposed scheme is tested by using the real vehicle wherein the real-time assisting control based on MPC is implemented.

This research develops a metric for the evaluation of an automobile driver's distraction based on a mathematical driving behavior model. Driving data was collected in a driving simulator. The primary task was to maintain a constant following distance behind a lead vehicle. The secondary task, which brings about the distraction, is to operate the in-car touch panel. A PrARX model is used to describe the vehicle-following behavior. In the PrARX model, the weighting parameter represents the driver's logical decision making and the auto-regressive exogenous models characterize the driver's continuous-time motion control behavior. By calculating the entropy of the PrARX model, the driver's distraction, which is considered a degradation of decision-making ability, is assessed in a quantitative manner.

This paper proposes a probability-weighted autoregressive exogenous (PrARX) model wherein the multiple ARX models are composed of the probabilistic weighting functions. This model can represent both the motion-control and decision-making aspects of the driving behavior. As the probabilistic weighting function, a “softmax” function is introduced. Then, the parameter estimation problem for the proposed model is formulated as a single optimization problem. The “soft” partition defined by the PrARX model can represent the decision-making characteristics of the driver with vagueness. This vagueness can be quantified by introducing the “decision entropy.” In addition, it can be easily extended to the online estimation scheme due to its small computational cost. Finally, the proposed model is applied to the modeling of the vehicle-following task, and the usefulness of the model is verified and discussed.

This research develops a new graph-map method for autonomous car-like mobile robots based on variable-resolution division of space. Unlike conventional roadmaps, which include position information only, the proposed graph-map also includes orientation information of the car-like robot. In this manner, the robot is able to plan a path in detail. The orientation information of each node is not a fixed value but a range. The range is constructed by dividing the orientation space using variable-resolution, which is obtained from the surrounding situation of links that connect to the node. Finally, the proposed method is evaluated through simulations.

This research addresses a method for mobile robots that simultaneously performs map building and path-planning on line. A graph representation of a workspace with variable resolutions is constructed using measurement data obtained by omni-directional distance sensors. At the same time, a real-time search for a feasible path to the goal is executed on the constructed graph-map. The proposed method is evaluated through experiments using an omni-directional mobile robot equipped with laser range finders.

This paper presents a decentralized planning method for generating dynamic whole body motions of multilink robots including humanoids. First, a robotic system will be modeled as a general multi-body dynamical system. The planning problem of a multi-body system will then be formulated as a constraint resolution problem. The problem will be solved by means of an extended Gauss-Seidel method, which is capable of handling multiple constraint groups with different priorities. The method will be demonstrated in whole-body motion generation tasks of a humanoid, both in numerical simulations and in experiments using a real humanoid robot.