This paper proposes a stable force control system to achieve the force control of industrial robots. The proposed stable force control system is designed by using an I-P force control system and a resonance ratio control system considering the required interaction with the environment. To design the resonance ratio control system considering the contact with the environment, this paper analyzes the stability of the proposed resonance ratio control and the bandwidth of a disturbance observer. This paper designs the resonance ratio control system using an instantaneous state observer considering the results of the stability analysis. In addition, a stable force control system is designed based on the resonance ratio control system using an instantaneous state observer. In this paper, the effectiveness of the proposed force control system is confirmed by performing numerical simulations and experiments using an industrial robot arm.

This paper proposes a new acceleration control method for industrial robots. To achieve high-speed and high-precision path tracking control, the motion control system must be able to control the arm acceleration. However, it is difficult to directly control the arm acceleration, because the speed reduction mechanism of industrial robots has axial torsion. To solve this problem, this paper proposes a new acceleration control method based on an inverse system. The proposed method designs an inverse transfer function system from the current command for arm acceleration. Acceleration control is achieved by the feed-forward control input designed from the inverse system. In addition, a robust feedback control system is designed to compensate for the dynamic disturbance torque and parameter variation. The effectiveness of the proposed method is confirmed by experimental results using a 2-DOF robot manipulator.

Sliding a probe over a textured surface generates a rich collection of vibrations that one can easily use to create a mental model of the surface. Haptic virtual environments attempt to mimic these real interactions, but common haptic rendering techniques typically fail to reproduce the sensations that are encountered during texture exploration. Past approaches have focused on building a representation of textures using a priori ideas about surface properties. Instead, this paper describes a process of synthesizing probe-surface interactions from data recorded from real interactions. We explain how to apply the mathematical principles of Linear Predictive Coding (LPC) to develop a discrete transfer function that represents the acceleration response under specific probe-surface interaction conditions. We then use this predictive transfer function to generate unique acceleration signals of arbitrary length. In order to move between transfer functions from different probe-surface interaction conditions, we develop a method for interpolating the variables involved in the texture synthesis process. Finally, we compare the results of this process with real recorded acceleration signals, and we show that the two correlate strongly in the frequency domain.

In the last decade, many sensor-based navigation algorithms have been proposed. In the sensor-based navigation, a robot arrives at its goal globally while avoiding neighbor obstacles locally by sensor information. In every environment, a mobile robot arrives at its goal surely. However if an environment has complicated shape, a mobile robot sometimes joins a loop and consequently runs long until its goal. In general, a loop consists of routes which a robot follows obstacles in the same direction. Nevertheless in most previous algorithms, a mobile robot follows an encountered obstacle in a constant direction. On this observation, a robot is exempted from participation of a loop by reversing a direction to follow an obstacle. The authors discuss algorithms in which the following direction is alternatively changed. This strategy has been adopted by cockroaches living in a natural environment. They then propose algorithms in which the following direction is randomly reversed. Then they compare paths generated by constant, alternative, and random selections in Bug2 and Classl under a graphics simulator for 2D complicated environments.

We discuss a bridge between model-based and sensor-based path-planning for a mobile robot in the presence of a self-positioning error. A model-based path-planning algorithm usually selects an arbitrary deadlock-free path in a computer world and then a mechanical vehicle faithfully follows the path in a real world. However if and only if the vehicle misunderstands its self-position by slipping and dead reckoning errors, it occasionally meets an unexpected obstruction since a selected path is to avoid all obstacles. As a result, the mechanical vehicle immediately falls into a deadlock in a real world. To avoid an unexpected obstacle in a real world, the vehicle obliges to use an online sensor-feedback function. However if the vehicle depends upon a careless sequence of online behaviors, it will join another deadlock, i.e., aperiodic sequence of online behaviors. To overcome this drawback, we propose three kinds of online bridges in order to generate the deadlock-free sequences of online behaviors along the path selected in an off-line path-planning algorithm.

In this paper, we propose a new type of two sensor-based path-planning algorithms running in a 2D uncertain world. In almost all previous sensor-based path-planning algorithms, the automaton comes close to the goal asymptotically when it leaves an obstructed obstacle. Consequently there is no obstacle where the automaton can hit in future at worst and then the automaton arrives at the goal finally. As a result, the proposed algorithms ensure their deadlock-free characteristics in an unknown world. As contrasted with this, the new type of two sensor-based path-planning algorithms ensures its deadlock-free characteristic by using several topological characteristics of an unknown world. Some of them are used for eliminating any local loop and alternatively the others are used for converging all global loops to the goal monotonously in an online manner. In result, even if the automaton encounters many global loops in an uncertain world, it finally catches the goal after leaving their minimum loop.>

Proposes a new sensor-based path-planning algorithm running in an uncertain 2D world. In the algorithm, the automaton basically goes straight to the goal, and if it touches some uncertain obstacles, it traces the obstacle boundary until it can go straight to the goal again. Moreover if the automaton finds some loop in the world, it enters into the goal area. As a result, several loops encountered by the automaton become smaller monotonously in the world, and then the automaton arrives at the goal after it has escaped from the minimum loop. This result is ensured in an online manner. The algorithm has several good characteristics: (1) it omits some calculations of the distance from the present point, and therefore it is faster than all previous sensor-based algorithms; (2) it is more robust than the previous sensor-based algorithms for some self-dead-reckoning error; (3) it can generate shorter deadlock-free paths in almost all 2D worlds than the previous sensor-based algorithms.