In this article, we demonstrate the value of using decoupled neural oscillators in conjunction with appropriate mechanical designs for snake-like robots engaging in field locomotion. Even though our typical wheeled snake robot with decoupled neural oscillators incorporating angle joint feedback can generate consistent lateral undulation, it lacks robustness. A purely decoupled actuation strategy is not practical for field locomotion. However, the application of one-way wheels and elasticity between the modules improves robustness, allowing our robot to traverse outdoor open terrain. On the other hand, the physical form of the undulatory motion must be varied to pass through intricate narrow-field environments. The lack of neural constraints between the modules passively enables this variability and also facilitates each module independently gaining propulsion (i.e., by means of reflexive thrust via sensory feedback to the neural oscillator and one-way traction generated by one-way side rollers). The designed robot is able to engage in fast creeping motion through an environment containing randomly distributed logs. The results of the field experiments show that the specially designed mechanisms in conjunction with decoupled oscillators can enhance robustness, variability, and independent local propulsion.

Snakes are very unique animals that have distinguished motor function adaptable to the most diverse environments in terrestrial animals regardless of their simple cord-shaped body. Revealing the mechanism underlying this distinct locomotion pattern, which is fundamentally different from walking, is significant not only in biological field but also for applications in engineering field. However, it has been difficult to clarify this adaptive function, emerging from dynamic interaction between body, brain and environment, by previous scientific methodologies based on reductionism, where understanding of the total system is approached by analyzing specific individual elements. In this research, we aim at revealing the mechanisms underlying this adaptability by the use of the constructive methodology, in which biological system models reflecting biological knowledge is used as a tool for analysis of the total system. In this report, we present development of system models, i.e. a dynamics simulator and a robot, constructed based on biological data obtained from dissection, CT-imaging, kinematics measurements and EMG measurements. By constructing mechanical models based on anatomical studies and applying control input designed according to EMG and kinematics measurements into the models, locomotion similar to living snakes was achieved and remaining problems in the models were clarified.

In this paper, development of a CPG-based controllers for meandering locomotion of a snake-like robot that can adapt to changing friction is presented. The controllers are composed of two kinds of CPG models and receives environmental information from friction force sensors attached on the bottom of the robot. Adaptive CPG parameters are obtained using genetic algorithm with environments with different friction conditions.

Several insights gained from observing quadruped robots in omnidirectional static walking experiments at high speed on a slope are presented here. In order to allow a robot to move as fast as possible, the height of center of gravity (COG) and three rotating axes, i.e., roll, pitch, and yaw, were used to discuss the COG with the corresponding optimal body posture (COBP). The COBP is the posture in which the motion velocity is maximized according to the height of COG, stability, degree of slope, and direction. Successive gait transition with a minimum number of steps is achievable with the use of a common foot position before and after a gait transition. The time required to change gaits may be reduced by designing the foot positions during crawling and rotating while limiting reachable region of the foot on a slope. The robot, thus, walks in any direction fast and statically with COBP by dynamically changing the height of COG and body posture during gait transitions

In this paper, we discuss the optimal body postures of a quadruped robot to perform omni-directional static walking on a slope. The optimal body posture is the posture with the maximum possible moving speed w. r. t. slope and moving direction. The proposed method based on dynamically changing body posture during gait-transitions, is used to maintain high robot motion velocity on slope. The timing of changing body posture is designed by considering the stability during gait-transition. Using the proposed method, the robot can walk into any direction with the fastest moving speed on a slope. Through walking experiments by computer simulation, the validity of the proposed method has been verified.

In this paper, we propose a control architecture for meandering locomotion of snake-like robots based on neural oscillator network (NON). The proposed architecture is composed of a network of central pattern generators (CPGs) to realize propagation of purposive oscillation with specific phase shift. By implementing this architecture to a simulator with consideration of mechanical dynamics of a real snake-like robot, we present realization of meandering motion and preliminary policies about parameter settings of the NON. Moreover, we present that real robot can successfully exhibit meandering movement by using controller output of the proposed architecture.

Snake-like robots that behaves biological snakes' characteristics have possibility to make them supremely adapted for environment. In this paper, we firstly formulate the kinematics and the dynamics of 3-dimensional snake robots and then analyze the sinus-lifting creeping motion as an example. In this study, the sinus-lifting motion was imaged as the creeping motion in 2-dimensional plane with different distribution of normal reaction forces for simplicity. Comparison has been made between the creeping locomotion with uniform normal reaction forces, and the sinus-lifting creeping motion where the normal reaction forces are distributed by the weight with respect to the body curvature. As a result, we know that the snake-like robot in the sinus-lifting creeping motion mode with curvature-weighted normal reaction forces moves at higher speed than that in the creeping motion mode with uniform normal reaction forces.

Biological snakes' diverse locomotion modes and physiology make them supremely adapted for environment. The special structure of snakes and their unique movement offer them peculiar ability of climbing and moving even in some ill-conditioned environments such as on the marshland or in narrow tubes. In this paper, we first discuss the controllability of a 3-dimensional snake-like robot, and then show the controllability of the robot by computer simulation.

Biological snakes' diverse locomotion modes and physiology make them supremely adapted for environment. To realize these snakes' noticeable features, we have developed a snake-like robot that has no any forward direction driving force. To enlarge the environment-adaptable ability of our robot, in this study we discuss the creeping locomotion of our snake-like robot on a slope. A computer simulator is presented for analysis of the creeping locomotion of our snake-like robot on a slope, and the environment-adaptable body shape for the creeping locomotion of the snake-like robot on slope is also derived through this simulator.

A transport module for constructing an efficient and flexible material handling system are proposed in the paper: an AGV (automated guided vehicle) module loaded a conveyer in which it is possible to hand-over operation between two modules. The operation can solve the deadlock problem among AGVs in the process of material handling. Prototype modules are created and hand-over experiments are made to measure motion characteristics of the modules. Transport simulations are made for a model plant layout by using values from former experiments. The results show the effectiveness of the proposed module from the viewpoint of transport efficiency, especially for cellular-type or process-type plant layouts.

We propose an architecture to accelerate reinforcement learning by a mobile robot. In order to solve the problem of explosion of learning time in former reinforcement learning methods, we introduce a mechanism to acquire and utilize a set of widely applicable generalized rules into a reinforcement learning system. The mechanism extracts these rules by a statistical analysis of experienced data from the learning process. By applying these rules, the learning process can be accelerated by reducing search space. Simulation results indicate the effectiveness of the proposed method.