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.

In this paper, we develop a new type of snake-like robot using screw-drive units that are connected by active joints. The screw-drive units enable the robot to generate propulsion on any side of the body in contact with environments. Another feature of this robot is the omnidirectional mobility by combinations of screws' angular velocities. We also derive a kinematic model and apply it to trajectory tracking control. Furthermore, we design a front-unit-following controller, which is suitable for manual operations. In this control system, operators are required to command only one unit in the front; then, commands for the rest of the units are automatically calculated to track the path of the preceding units. Asymptotic convergence of the tracking error of the front-unit-following controller is analyzed based on a Lyapunov approach for the case of constant curvature. The effectiveness of the control method is demonstrated by numerical examples and 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 body. 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 wheels are slowed down by properly changing the target pitch angle of the body. Stability analysis is carried out by taking into account the transient response of the body angle to the target value, which is neglected in existing work. The effectiveness of the proposed method is demonstrated by experiments.

To achieve a balanced walking for a humanoid, it is necessary to estimate the dynamic stability of the system. However, in a small size humanoid with restricted system resource, it is hard to satisfy the performance level desired by dynamics analysis. Therefore, in this paper, we propose the feasible methods to generate gait pattern and stabilize walking based on coupled oscillators which have a clear correlation between oscillator parameters and system dynamics without a real time ZMP calculation. The proposed method was tested on the open humanoid platform DARwIn-OP for the evaluation, and the result showed that a real time gait pattern generation and stabilization were realized.

This paper presents a path-tracking control method for a snake-like robot using screw drive mechanism. The operators are required to command only one unit in the head, then commands for the rest of the units are automatically calculated to track the path of the preceding units. Although the velocity commands for exact path tracking can be calculated using past command signals due to the omni-directional property of the robot, a simpler control law without using past signals is adopted from the implementation viewpoint. Asymptotic tracking error is investigated based on a Lyapunov approach for the case of a constant curvature. Furthermore, the effectiveness of the proposed method is investigated by computer simulations and laboratory experiments.

In this paper, we develop a prototype of a new snake-like robot using the screw drive mechanism, and design a control system design for trajectory tracking. First, we explain the outline of the snake-like robot using the screw drive units and joint units, as well as the principle of the screw drive mechanism. Next, in order to achieve the trajectory tracking of the robot, we derive a kinematic model. Finally, the validity of the derived model and the effectiveness of the proposed control system are demonstrated by simulations and experiments.

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.

For heavy material handling, we first propose an agricultural robot which consists of a 4-DOF underactuated manipulator, a crawler vehicle, and a passive hand in the first step. Then, we discuss the use of LQ control for high speed manipulation. We design a control system based on the characteristics of agricultural operations and the manipulator. The robust stability is analyzed under the structured uncertainty. The robotic harvesting experiment was done in a watermelon field and the effectiveness of LQ control was confirmed.