Control of hyper-redundant robot using QDSEGA

We consider a flexible autonomous system. To realize the system, we employ a hyper-redundant system (a flexible hardware system) and reinforcement learning controller &#34;QDSEGA&#34; (Q-learning with structuring exploration space based on genetic algorithm) which is a flexible software system. In this paper we apply QDSEGA for controlling of the hyper-redundant robot. To demonstrate the effectiveness, a task of acquisition of locomotion patterns is applied to a multi-legged formation and a snake-like formation, from which an effective locomotion is obtained.</p

Control of snake robots with switching constraints: trajectory tracking with moving obstacle

We propose control of a snake robot that can switch lifting parts dynamically according to kinematics. Snakes lift parts of their body and dynamically switch lifting parts during locomotion: e.g. sinus-lifting and sidewinding motions. These characteristic types of snake locomotion are used for rapid and efficient movement across a sandy surface. However, optimal motion of a robot would not necessarily be the same as that of a real snake as the features of a robot’s body are different from those of a real snake. We derived a mathematical model and designed a controller for the three-dimensional motion of a snake robot on a two-dimensional plane. Our aim was to accomplish effective locomotion by selecting parts of the body to be lifted and parts to remain in contact with the ground. We derived the kinematic model with switching constraints by introducing a discrete mode number. Next, we proposed a control strategy for trajectory tracking with switching constraints to decrease cost function, and to satisfy the conditions of static stability. In this paper, we introduced a cost function related to avoidance of the singularity and the moving obstacle. Simulations and experiments demonstrated the effectiveness of the proposed controller and switching constraints

A Study on Sinus-Lifting Motion of a Snake Robot With Sequential Optimization of a Hybrid System

In this paper, we consider “sinus-lifting motion” of a living snake, in which a snake lifts up some parts of its body from the ground, and switches the lifted parts dynamically. It is not clear whether imitating the sinus-lifting motion is the best locomotion or not for a snake like robot. The aim of this paper is to propose an appropriate motion pattern to a snake like robot considering the optimality of the sinus-lifting motion. We introduce two physical parameters, constraint forces and energy efficiency, as cost functions to optimize and propose switching strategies for generating optimal motion patterns of a snake like robot

Approximate Path-Tracking Control of Snake Robot Joints With Switching Constraints

This paper presents an approximate path-tracking control method for all joints of a snake robot, along with the verification of this method by simulations and experiments. We consider a wheeled snake robot that has passive wheels and active joints. The robot can switch the wheels that touch the ground by lifting the required parts of its body. The model of the robot becomes a kinematically redundant system if certain wheels are lifted. Using this kinematic redundancy, and selecting the appropriate lifted parts, we design a controller for approximate path tracking. Simulations and experimental results show that the proposed controller effectively reduces the path-tracking error for all joints of the snake robot

Gait Design for a Snake Robot by Connecting Curve Segments and Experimental Demonstration

This paper presents a method for designing the gait of a snake robot that moves in a complicated environment. We propose a method for expressing the target form of a snake robot by connecting curve segments whose curvature and torsion are already known. Because the characteristics of each combined shape are clear, we can design the target form intuitively and approximate a snake robot configuration to this form with low computational cost. In addition, we propose two novel gaits for the snake robot as a design example of the proposed method. The first gait is aimed at moving over a flange on a pipe, while the other is the crawler gait aimed at moving over rough terrain. We demonstrated the effectiveness of the two gaits on a pipe and rough terrain in experiments

Behavioral responses to colony-level properties affect disturbance resistance of red harvester ant colonies

Self-organizing biological systems, such as colonies of social insects, are characterized by their decentralized control and flexible responses to changing environments, often likened to swarm intelligence. Although decentralized control is well known to be a product of local interactions among agents, without the need for a bird’s-eye view, indirect knowledge of properties that indicate the current states of the entire system also helps each agent to respond to changes, thereby leading to a more adaptive system. In this study, we analyze the rules that govern workers’ behavioral responses to colony-level properties and assess whether they contribute to adaptive flexibility in social insect colonies. We focus on task allocation among red harvester ants (Pogonomyrmex barbatus) as a model system and develop an ordinary differential equation model to describe the system of task allocation among workers. We simulate 12 scenarios specifying how workers respond to changes in the colony-level properties of colony size and nutritional state. We found that when workers decrease their contact rates in response to increasing colony size, they enable achievement of a larger colony size, similar to that of P. barbatus colonies in nature, and when workers increase their foraging levels in response to decreasing colony-wide nutritional levels, they increase resilience to environmental disturbances. These negative feedback rules governing the response to colony-level properties are consistent with previous reports on ants and honeybees

Extended QDSEGA for Controlling Real Robot : Acquisition of Locomotion Patterns for Snake : like Robot

Reinforcement learning is very effective for robot learning. It is because it does not need prior knowledge and has higher capability of reactive and adaptive behaviors. In our previous works, we proposed new reinforce learning algorithm: &quot;Q-learning with dynamic structuring of exploration space based on genetic algorithm (QDSEGA)&quot;. It is designed for complicated systems with large action-state space like a robot with many redundant degrees of freedom. However the application of QDSEGA is restricted to static systems. A snake-like robot has many redundant degrees of freedom and the dynamics of the system are very important to complete the locomotion task. So application of usual reinforcement learning is very difficult. In this paper, we extend layered structure of QDSEGA so that it becomes possible to apply it to real robots that have complexities and dynamics. We apply it to acquisition of locomotion pattern of the snake-like robot and demonstrate the effectiveness and the validity of QDSEGA with the extended layered structure by simulation and experiment. </p

An Energy-Autonomous Chemical Oxygen Demand Sensor Using a Microbial Fuel Cell and Embedded Machine Learning

The current methods of water quality monitoring tend to be costly, labor-intensive, and off-site. Also, they are not energetically sustainable and often require environmentally damaging power sources such as batteries. Microbial fuel cell (MFC) technology is a promising sustainable alternative to combat these issues due to its low cost, eco-friendly energy generation, and bio-sensing features. Extensive work has been done on using MFCs as bio-sensors or sources of power separately. However, little work has been done toward using MFCs for both applications at the same time. Additionally, previous studies using MFCs for water quality measurement have been mostly limited to laboratory conditions due to the biochemical complexity of the real-world. Another limitation of MFCs is how little power they can generate, requiring the MFC-based systems to have minimal power consumption. This work addresses these challenges and presents an energy-autonomous water quality sensing unit that uses a single MFC both as its sensory input and the sole source of power for computing the chemical oxygen demand (COD). In the proposed unit, geometric features of the voltage profile of the MFC (e.g., peak heights) are used as the inputs to a machine learning algorithm (support vector regression (SVR)). The electrical power generated by the MFC is used to drive a low-power microcontroller which logs the MFC voltage and runs the machine learning algorithm. Experimental evaluation showed that the device is capable of detecting the COD of natural pond water samples accurately (coefficient of determination (R 2 )=0.94). This work is the first demonstration of energy autonomy in an MFC-based sensing unit for measuring water quality and represents a step forward in the development of energy-autonomous sensors for environmental monitoring applications

Motion control of a snake robot moving between two non-parallel planes

A control method that makes the head of a snake robot follow an arbitrary trajectory on two non-parallel planes, including coexisting sloped and flat planes, is presented. We clarify an appropriate condition of contact between the robot and planes and design a controller for the part of the robot connecting the two planes that satisfies the contact condition. Assuming that the contact condition is satisfied, we derive a simplified model of the robot and design a controller for trajectory tracking of the robot’s head. The controller uses kinematic redundancy to avoid violating the limit of the joint angle and a collision between the robot and the edge of a plane. The effectiveness of the proposed method is demonstrated in experiments using an actual robot