CPG Control for Biped Hopping Robot in Unpredictable Environment

A CPG control mechanism is proposed for hopping motion control of biped robot in unpredictable environment.Based on analysis of robot motion and biological observation of animal's control mechanism,the motion control task is divided into two simple parts:motion sequence control and output force control.Inspired by a two-level CPG model,a two-level CPG control mechanism is constructed to coordinate the drivers of robot joint,while various feedback information are introduced into the control mechanism.Interneurons within the control mechanism are modeled to generate motion rhythm and pattern promptly for motion sequence control; motoneurons are modeled to control output forces of joint drivers in real time according to feedbacks.The control system can perceive changes caused by unknown perturbations and environment changes according to feedback information,and adapt to unpredictable environment by adjusting outputs of neurons.The control mechanism is applied to a biped hopping robot in unpredictable environment on simulation platform,and stable adaptive motions are obtained.     

Fly-like visuomotor responses of a robot using aVLSI motion-sensitive chips

 We explore the use of continuous-time analog very-large-scale-integrated (aVLSI) neuromorphic visual preprocessors together with a robotic platform in generating bio-inspired behaviors. Both the aVLSI motion sensors and the robot behaviors described in this work are inspired by the motion computation in the fly visual system and two different fly behaviors. In most robotic systems, the visual information comes from serially scanned imagers. This restricts the form of computation of the visual image and slows down the input rate to the controller system of the robot, hence increasing the reaction time of the robot. These aVLSI neuromorphic sensors reduce the computational load and power consumption of the robot, thus making it possible to explore continuous-time visuomotor control systems that react in real-time to the environment. The motion sensor provides two outputs: one for the preferred direction and the other for the null direction. These motion outputs are created from the aggregation of six elementary motion detectors that implement a variant of Reichardt's correlation algorithm. The four analog continuous-time outputs from the motion chips go to the control system on the robot which generates a mixture of two behaviors – course stabilization and fixation – from the outputs of these sensors. Since there are only four outputs, the amount of information transmitted to the controller is reduced (as compared to using a CCD sensor), and the reaction time of the robot is greatly decreased. In this work, the robot samples the motion sensors every 3.3 ms during the behavioral experiments. Received: 4 October 1999 / Accepted in revised form: 26 April 2001     

Simultaneous Optimization of Robot Structure and Control System Using Evolutionary Algorithm

The simultaneous optimization of a robot structure and control system to realize effective mobility in an outdoor environment is investigated. Recently, various wheeled mechanisms with passive and/or active linkages for outdoor environments have been developed and evaluated. We developed a mobile robot having six active wheels and passive linkage mechanisms, and experimentally verified its maneuverability in an indoor environment. However, there are various obstacles in outdoor environment and the travel ability of a robot thus depends on its mechanical structure and control system.We proposed a method of simultaneously optimizing mobile robot structure and control system using an evolutionary algorithm. Here, a gene expresses the parameters of the structure and control system. A simulated mobile robot and controller are based on these parameters and the behavior of the mobile robot is evaluated for three typical obstacles. From the evaluation results, new genes are created and evaluated repeatedly. The evaluation items are travel distance, travel time, energy consumption, control accuracy, and attitude of the robot.Effective outdoor travel is achieved around the 80th generation, after which, other parameters are optimized until the 300th generation. The optimized gene is able to pass through the three obstacles with low energy consumption, accurate control, and stable attitude.     

Information flow principles for plasticity in foraging robot swarms

An important characteristic of a robot swarm that must operate in the real world is the ability to cope with changeable environments by exhibiting behavioural plasticity at the collective level. For example, a swarm of foraging robots should be able to repeatedly reorganise in order to exploit resource deposits that appear intermittently in different locations throughout their environment. In this paper, we report on simulation experiments with homogeneous foraging robot teams and show that analysing swarm behaviour in terms of information flow can help us to identify whether a particular behavioural strategy is likely to exhibit useful swarm plasticity in response to dynamic environments. While it is beneficial to maximise the rate at which robots share information when they make collective decisions in a static environment, plastic swarm behaviour in changeable environments requires regulated information transfer in order to achieve a balance between the exploitation of existing information and exploration leading to acquisition of new information. We give examples of how information flow analysis can help designers to decide on robot control strategies with relevance to a number of applications explored in the swarm robotics literature.     

A memory-based approach to navigation

A memory-based system for autonomous indoor navigation is presented. The system was implemented as a follow-midline reflex on a robot that moves along the corridors of our institute. The robot estimates its position in the environment by comparing the visual input with images contained in its memory. Spatial positions are represented by classes. Memories are formed during a learning phase by encoding labeled images. The output of the system is the a posteriori probability distribution of the classes, given an input image. During performance, an image is assigned to the class that maximizes the probability. This work shows that extensive use of memory can reduce information processing to a simple and flexible procedure, without the need of complicated and specific preprocessing. The system is shown to be reliable, with good generalization capability. With learning limited to a small part of a corridor, the robot navigates along the entire corridor. Furthermore, it is able to move in other corridors of different shape, with different illumination conditions.     

Insect–machine hybrid system for understanding and evaluating sensory-motor control by sex pheromone in Bombyx mori

To elucidate the dynamic information processing in a brain underlying adaptive behavior, it is necessary to understand the behavior and corresponding neural activities. This requires animals which have clear relationships between behavior and corresponding neural activities. Insects are precisely such animals and one of the adaptive behaviors of insects is high-accuracy odor source orientation. The most direct way to know the relationships between neural activity and behavior is by recording neural activities in a brain from freely behaving insects. There is also a method to give stimuli mimicking the natural environment to tethered insects allowing insects to walk or fly at the same position. In addition to these methods an ‘insect–machine hybrid system’ is proposed, which is another experimental system meeting the conditions necessary for approaching the dynamic processing in the brain of insects for generating adaptive behavior. This insect–machine hybrid system is an experimental system which has a mobile robot as its body. The robot is controlled by the insect through its behavior or the neural activities recorded from the brain. As we can arbitrarily control the motor output of the robot, we can intervene at the relationship between the insect and the environmental conditions.     

Optic flow estimation on trajectories generated by bio-inspired closed-loop flight

We generated panoramic imagery by simulating a fly-like robot carrying an imaging sensor, moving in free flight through a virtual arena bounded by walls, and containing obstructions. Flight was conducted under closed-loop control by a bio-inspired algorithm for visual guidance with feedback signals corresponding to the true optic flow that would be induced on an imager (computed by known kinematics and position of the robot relative to the environment). The robot had dynamics representative of a housefly-sized organism, although simplified to two-degree-of-freedom flight to generate uniaxial (azimuthal) optic flow on the retina in the plane of travel. Surfaces in the environment contained images of natural and man-made scenes that were captured by the moving sensor. Two bio-inspired motion detection algorithms and two computational optic flow estimation algorithms were applied to sequences of image data, and their performance as optic flow estimators was evaluated by estimating the mutual information between outputs and true optic flow in an equatorial section of the visual field. Mutual information for individual estimators at particular locations within the visual field was surprisingly low (less than 1 bit in all cases) and considerably poorer for the bio-inspired algorithms that the man-made computational algorithms. However, mutual information between weighted sums of these signals and comparable sums of the true optic flow showed significant increases for the bio-inspired algorithms, whereas such improvement did not occur for the computational algorithms. Such summation is representative of the spatial integration performed by wide-field motion-sensitive neurons in the third optic ganglia of flies.     

Visually guided gait modifications for stepping over an obstacle: a bio-inspired approach

There is an increasing interest in conceiving robotic systems that are able to move and act in an unstructured and not predefined environment, for which autonomy and adaptability are crucial features. In nature, animals are autonomous biological systems, which often serve as bio-inspiration models, not only for their physical and mechanical properties, but also their control structures that enable adaptability and autonomy—for which learning is (at least) partially responsible. This work proposes a system which seeks to enable a quadruped robot to online learn to detect and to avoid stumbling on an obstacle in its path. The detection relies in a forward internal model that estimates the robot’s perceptive information by exploring the locomotion repetitive nature. The system adapts the locomotion in order to place the robot optimally before attempting to step over the obstacle, avoiding any stumbling. Locomotion adaptation is achieved by changing control parameters of a central pattern generator (CPG)-based locomotion controller. The mechanism learns the necessary alterations to the stride length in order to adapt the locomotion by changing the required CPG parameter. Both learning tasks occur online and together define a sensorimotor map, which enables the robot to learn to step over the obstacle in its path. Simulation results show the feasibility of the proposed approach.     

Using insects to drive mobile robots — hybrid robots bridge the gap between biological and artificial systems

The use of mobile robots is an effective method of validating sensory–motor models of animals in a real environment. The well-identified insect sensory–motor systems have been the major targets for modeling. Furthermore, mobile robots implemented with such insect models attract engineers who aim to avail advantages from organisms. However, directly comparing the robots with real insects is still difficult, even if we successfully model the biological systems, because of the physical differences between them. We developed a hybrid robot to bridge the gap. This hybrid robot is an insect-controlled robot, in which a tethered male silkmoth (Bombyx mori) drives the robot in order to localize an odor source. This robot has the following three advantages: 1) from a biomimetic perspective, the robot enables us to evaluate the potential performance of future insect-mimetic robots; 2) from a biological perspective, the robot enables us to manipulate the closed-loop of an onboard insect for further understanding of its sensory–motor system; and 3) the robot enables comparison with insect models as a reference biological system. In this paper, we review the recent works regarding insect-controlled robots and discuss the significance for both engineering and biology.     

Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N²), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.     

A Hybrid Search Algorithm for Swarm Robots Searching in an Unknown Environment

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency.     

A Switching Controller System for a Wheeled Mobile Robot

A wheeled mobile mechanism with a passive and/or active linkage mechanism for rough terrain environment is developed and evaluated. The wheeled mobile mechanism which has high mobility in rough terrain needs sophisticated system to adapt various environments.We focus on the development of a switching controller system for wheeled mobile robots in rough terrain. This system consists of two sub-systems: an environment recognition system using link angles and an adaptive control system. In the environment recognition system, we introduce a Self-Organizing Map (SOM) for clustering link angles. In the adaptive controllers, we introduce neural networks to calculate the inverse model of the wheeled mobile robot.The environment recognition system can recognize the environment in which the robot travels, and the adjustable controllers are tuned by experimental results for each environment. The dual sub-system switching controller system is experimentally evaluated. The system recognizes its environment and adapts by switching the adjustable controllers. This system demonstrates superior performance to a well-tuned single PID controller.     

An information-theory model of cellular survival, mutation and transformation and the relevance of this model to chemical carcinogenesis

A model of cellular survival, mutation and transformation is presented in accordance with information theory. A cellular system is considered to be stable with respect to its environment when the vital information the cell expresses at least equals the information requirements of the environment. Environmental agents, such as mutagens, perturb the cell's expression of information such that an imbalance occurs between the cell's information requirement and the cell's ability to express vital information. This imbalance, which is interpreted as the intrinsic entropy of the cell, serves as a measure of biological cell death. If the cell compensates for the altered ability to express information by adapting to a less restricted set of information requirements, then one may view the cell as having undergone a "transformation" to a less restricted phenotype. This paper will elucidate the mathematical inter-relationships of cellular survival, mutation and transformation and will relate these mathematical concepts to chemical carcinogenesis.     

Information-driven self-organization: the dynamical system approach to autonomous robot behavior

In recent years, information theory has come into the focus of researchers interested in the sensorimotor dynamics of both robots and living beings. One root for these approaches is the idea that living beings are information processing systems and that the optimization of these processes should be an evolutionary advantage. Apart from these more fundamental questions, there is much interest recently in the question how a robot can be equipped with an internal drive for innovation or curiosity that may serve as a drive for an open-ended, self-determined development of the robot. The success of these approaches depends essentially on the choice of a convenient measure for the information. This article studies in some detail the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process. The PI of a process quantifies the total information of past experience that can be used for predicting future events. However, the application of information theoretic measures in robotics mostly is restricted to the case of a finite, discrete state-action space. This article aims at applying the PI in the dynamical systems approach to robot control. We study linear systems as a first step and derive exact results for the PI together with explicit learning rules for the parameters of the controller. Interestingly, these learning rules are of Hebbian nature and local in the sense that the synaptic update is given by the product of activities available directly at the pertinent synaptic ports. The general findings are exemplified by a number of case studies. In particular, in a two-dimensional system, designed at mimicking embodied systems with latent oscillatory locomotion patterns, it is shown that maximizing the PI means to recognize and amplify the latent modes of the robotic system. This and many other examples show that the learning rules derived from the maximum PI principle are a versatile tool for the self-organization of behavior in complex robotic systems.     

View from the boundary

Re-implementing biological mechanisms on robots not only has technological application but can provide a unique perspective on the nature of sensory processing in animals. To make a robot work, we need to understand the function as part of an embodied, behaving system. I argue that this perspective suggests that the terms "representation" and "information processing" can be misleading when we seek to understand how neurobiological mechanisms carry out perceptual processes. This argument is presented here with reference to a robot model of cricket behavior, which has demonstrated competence comparable to that of the insect, but utilizes surprisingly simple central processing. Instead it depends on sensory interfaces that are well matched to the task, and on the link between environment, action, and perception.     

A brain-inspired compact cognitive mapping system

In many simultaneous localization and mapping (SLAM) systems, the map of the environment grows over time as the robot explores the environment. The ever-growing map prevents long-term mapping, especially in large-scale environments. In this paper, we develop a compact cognitive mapping approach inspired by neurobiological experiments. Mimicking the firing activities of neighborhood cells, neighborhood fields determined by movement information, i.e. translation and rotation, are modeled to describe one of the distinct segments of the explored environment. The vertices with low neighborhood field activities are avoided to be added into the cognitive map. The optimization of the cognitive map is formulated as a robust non-linear least squares problem constrained by the transitions between vertices, and is numerically solved efficiently. According to the cognitive decision-making of place familiarity, loop closure edges are clustered depending on time intervals, and then batch global optimization of the cognitive map is performed to satisfy the combined constraint of the whole cluster. After the loop closure process, scene integration is performed, in which revisited vertices are removed subsequently to further reduce the size of the cognitive map. The compact cognitive mapping approach is tested on a monocular visual SLAM system in a naturalistic maze for a biomimetic animated robot. Our results demonstrate that the proposed method largely restricts the growth of the size of the cognitive map over time, and meanwhile, the compact cognitive map correctly represents the overall layout of the environment. The compact cognitive mapping method is well suitable for the representation of large-scale environments to achieve long-term robot navigation. Electronic supplementary materialThe online version of this article (10.1007/s11571-020-09621-6) contains supplementary material, which is available to authorized users.     

Research on the Kinematic Properties of a Sperm-Like Swimming Micro Robot

Nowadays, it has been one of the hottest topics for scientists to research the interventional micro robots operating in human lumen. In this paper, a novel sperm-like interventional swimming robot with single tail is presented. The kinematic models of the sperm-like helical swimming modes are built, and the motion principles are analyzed numerically. Positions and orientations are displayed graphically during the single-tail micro robot swims in liquid. Also, the displacements and the swimming velocities of the robot in x, y, z directions are plotted. It is shown that, when the single flexible tail screws in liquid environment, it generates both axial and radial propulsion forces, thus to cause the axial and the radial movements. In order to make the swimming micro robot more controllable, an improved sperm-like swimming intervention micro robot with four flexible tails is fabricated and characterized in pipes filled with silicone oil. Experimental results show that the sperm-like micro robot can swim efficiently. With different combinations of the tails' rotation directions, the robot can gain excellent controlled performance.     

Comparison of human and humanoid robot control of upright stance

There is considerable recent interest in developing humanoid robots. An important substrate for many motor actions in both humans and biped robots is the ability to maintain a statically or dynamically stable posture. Given the success of the human design, one would expect there are lessons to be learned in formulating a postural control mechanism for robots. In this study we limit ourselves to considering the problem of maintaining upright stance. Human stance control is compared to a suggested method for robot stance control called zero moment point (ZMP) compensation. Results from experimental and modeling studies suggest there are two important subsystems that account for the low- and mid-frequency (DC to 1 Hz) dynamic characteristics of human stance control. These subsystems are (1) a “sensory integration” mechanism whereby orientation information from multiple sensory systems encoding body kinematics (i.e. position, velocity) is flexibly combined to provide an overall estimate of body orientation while allowing adjustments (sensory re-weighting) that compensate for changing environmental conditions and (2) an “effort control” mechanism that uses kinetic-related (i.e., force-related) sensory information to reduce the mean deviation of body orientation from upright. Functionally, ZMP compensation is directly analogous to how humans appear to use kinetic feedback to modify the main sensory integration feedback loop controlling body orientation. However, a flexible sensory integration mechanism is missing from robot control leaving the robot vulnerable to instability in conditions where humans are able to maintain stance. We suggest the addition of a simple form of sensory integration to improve robot stance control. We also investigate how the biological constraint of feedback time delay influences the human stance control design. The human system may serve as a guide for improved robot control, but should not be directly copied because the constraints on robot and human control are different.     

The emergence of multiple particle tracking in intracellular trafficking of nanomedicines

A growing number of nanoparticle systems, termed “nanomedicines”, are being developed for diagnostic and therapeutic applications. Nanoparticles can employ various cellular entry pathways and trafficking mechanisms to effectively deliver drugs, biomolecules, and imaging agents to precise sub-cellular locations. However, the dynamic transport of nanoparticles through the complex intracellular environment is not well understood, having been primarily studied with static or bulk averaged methods in the past. Such techniques do not provide detailed information regarding the transport mechanism and rates of individual nanoparticles, where understanding of the interaction of nanoparticles with the cellular environment remains incomplete. Recent advances in live-cell fluorescence microscopy and real-time multiple particle tracking (MPT) have facilitated an improved understanding of cell trafficking pathways. Understanding the dynamic transport of nanoparticles as they are delivered into complex cellular components may lead to rational improvements in the design of nanomedicines. This review discusses different cellular uptake and trafficking pathways of nanomedicines, briefly highlights current fluorescence microscopy tools, and provides examples from the recent literature on the use of MPT and its applications.