Teleoperation system using past image records (SPIR) is a system that provides the third-person view image to the operator for the robot operation. The image is visually generated by overlaying the CG model of the robot on the background image taken by the robot-mounted camera at a past time. In this paper, we proposed a new user interface based on SPIR for the teleoperation of a mobile manipulator. By using the proposed user interface, the operator can control the end effector's position and orientation intuitively while looking the end effector from the third-person viewpoint.

In this paper, we propose a new method of controlling a group of mobile robots based on formation abstraction. The shape of a formation is represented by a deformable polygon, which is constructed by bending a rectangle, to go through narrow spaces without colliding with obstacles. If the trajectory of the front end point, as well as the width and the length of the formation, are given, the formation automatically reshapes itself to fit the area through which the front part of the group has already safely passed. Furthermore, the robots continuously try to optimize their positions to decrease the risk of collisions by integrating a decentralized locational optimization algorithm into the formation control. We show that the objective function, taking into account the distance between robots, does not decrease for fixed and nonconvex polygonal formation shapes if the zero-order hold control is applied for a sufficiently short sampling period. We also analyze the influence of the decentralized locational optimization algorithm on the objective function in the case of variable formations. The effectiveness of the proposed method is demonstrated in both simulations and real robot experiments.

This paper presents a shape transformation control method of a mobile robot with wheel-arms. The proposed method aims at transformation from a four-wheeled mode for high-speed mobility to an inverted pendulum mode, which has advantages of high viewing position and small turning radius. The transformation starts with lifting up the wheel-arms to raise the center of gravity of the whole robot including the main body and arms. From such initial states, the body is lifted up and controlled to the target angle by partial linearization, while returning the arms to the initial angle. Then, the robot position is controlled by manipulating the target body angle. Unlike existing methods, we take into account the effects of the body angular velocity and the tracking error of the body angle by constructing a model set, which is composed of a single nominal model and its polytopic uncertainty for the system matrices. In order to derive the model set, we assume that the target body angle is constrained to a prescribed range. Therefore, the target body angle is manipulated using a model predictive control method, such that the closed-loop system is asymptotically stabilized, while the given constraint is satisfied, for all systems in the model set. The effectiveness of the proposed method is demonstrated in both simulations and real robot experiments.

This paper presents a model predictive control (MPC) approach for multivehicle formation taking into account collision avoidance and velocity limitation with reduced computational burden. The first part of the paper constructs a formation control law using feedback linearization with MPC in order to reduce the optimal control problem to a mixed-integer quadratic programming problem for a group of unicycles. The second part constructs a new branch-and-bound (B& B) -based algorithm for collision-avoidance problems. Numerical examples and experiments show that the proposed method significantly reduces computation time.

Recently various autonomous mobile robots are developed for practical use. For coexistence with the robots and human in the real environment, the consideration of safety is very important. We should consider a region with a limitation of a maximum velocity of a mobile robot for the safety. In this paper, we propose path planning and trajectory generation methods for a mobile robot which moves in the environment with predetermined velocity constraints. In order to demonstrate the validity of the proposed methods, numerical simulations and experiments have been carried out.

This paper presents a new branch-and-bound (B&B) method to reduce the computational burden of online optimization in multi-vehicle formation control with collision avoidance that is formulated by model predictive control. One of the possible reasons for extremely heavy computation time of the standard B&B algorithm is that so many subproblems are generated since integer constraints are rarely satisfied in “relaxed” problems. The purpose of the proposed method is to decrease subproblems based on a new branching rule taking into account the inherent properties of collision avoidance problems. Numerical examples and experiments show that the proposed method drastically reduces computation time of online optimization for multi-vehicle formation control.