Design of a modular assembly of four-footed robots with multiple functions

Modern industries use many types of robots. In addition to general robotic arms, bipedal, tripedal, and quadrupedal robots, which were originally developed as toys, are gradually being used for multiple applications in manufacturing processes. This research begins with establishing the platform for four-footed robots with multiple functions, high sensitivity, and modular assembly and this is how a fundamental model of the industrial robots is constructed. Under additional loads, the four feet of the quadrupedal robot reinforce its carrying ability and reliability compared to bipedal or tripedal robots, which helps it to carry more objects and enhances functionality. Based on different requirements and demands from the manufacturing processes, the highly sensitive four-footed robot provides an expandable interface to add different sensing components. In addition, when combined with a wireless communication module or independent 1.2 GHz radio frequency CCD wireless image transmission system, the user can control the robot remotely and instantly. The design helps the four-footed robot to expand its applications. By assembling and disassembling modules and changing the sensing components, the highly sensitive four-footed robot can be used for different tasks. Moreover, the remote control function of the robot will increase interaction with human beings, so it can become highly become involved in people's lives. The platform of the four-footed robot will become a design reference for the commercialization of different industrial robots, and it will provide the design of industrial robots with more options and useful applications.     

A rescue robot system for collecting information designed for ease of use — a proposal of a rescue systems concept

A remote controlled robot for collecting information in disasters, e.g. earthquakes, is one of most effective applications of robots, because it is very dangerous for human beings to locate survivors in collapsed buildings and, in addition, small robots can move into narrow spaces to find survivors. However, previous rescue systems that use robots have a significant problem — a shortage of operators. In catastrophic disasters, in order to save victims, we must explore wide areas within a limited time. Thus, many rescue robots should be employed simultaneously. However, human interfaces of previous rescue robots were complicated, so that well-trained professional operators were needed to operate the robots and, thus, to use many rescue robots, many professional operators were required. However, in such catastrophic disasters it is difficult to get many professional operators together within a short time. In this paper we address the problem and propose a concept of rescue team organization in which professional rescue staff and volunteer staff work together for handling a catastrophic disaster. We point out the necessity for rescue robots which can be operated easily by non-professional volunteer staff. To realize a rescue robot which can be operated easily, we propose a rescue robot system which has a human interface seen in typical, everyday vehicles and a snake-like robot which has mechanical intelligence. We have demonstrated the validity and the effectiveness of the proposed concept by developing a prototype system.     

Self-modeling in humanoid soccer robots

In this paper we discuss the applicability, potential benefits, open problems and expected contributions that an emerging set of self-modeling techniques might bring on the development of humanoid soccer robots. The idea is that robots might continuously generate, validate and adjust physical models of their sensorimotor interaction with the world. These models are exploited for adapting behavior in simulation, enhancing the learning skills of a robot with the regular transference of controllers developed in simulation to reality. Moreover, these simulations can be used to aid the execution of complex sensorimotor tasks, speed up adaptation and enhance task planning. We present experiments on the generation of behaviors for humanoid soccer robots using the Back-to-Reality algorithm. General motivations are presented, alternative algorithms are discussed and, most importantly, directions of research are proposed.     

Hybrid autonomous control for multi mobile robots

Reinforcement learning can be an adaptive and flexible control method for autonomous system. It does not need a priori knowledge; behaviors to accomplish given tasks are obtained automatically by repeating trial and error. However, with increasing complexity of the system, the learning costs are increased exponentially. Thus, application to complex systems, like a many redundant d.o.f. robot and multi-agent system, is very difficult. In the previous works in this field, applications were restricted to simple robots and small multi-agent systems, and because of restricted functions of the simple systems that have less redundancy, effectiveness of reinforcement learning is restricted. In our previous works, we had taken these problems into consideration and had proposed new reinforcement learning algorithm, 'Q-learning with dynamic structuring of exploration space based on GA (QDSEGA)'. Effectiveness of QDSEGA for redundant robots has been demonstrated using a 12-legged robot and a 50-link manipulator. However, previous works on QDSEGA were restricted to redundant robots and it was impossible to apply it to multi mobile robots. In this paper, we extend our previous work on QDSEGA by combining a rule-based distributed control and propose a hybrid autonomous control method for multi mobile robots. To demonstrate the effectiveness of the proposed method, simulations of a transportation task by 10 mobile robots are carried out. As a result, effective behaviors have been obtained.     

Perceptive whole‐body planning for multilegged robots in confined spaces

Legged robots are exceedingly versatile and have the potential to navigate complex, confined spaces due to their many degrees of freedom. As a result of the computational complexity, there exist no online planners for perceptive whole‐body locomotion of robots in tight spaces. In this paper, we present a new method for perceptive planning for multilegged robots, which generates body poses, footholds, and swing trajectories for collision avoidance. Measurements from an onboard depth camera are used to create a three‐dimensional map of the terrain around the robot. We randomly sample body poses then smooth the resulting trajectory while satisfying several constraints, such as robot kinematics and collision avoidance. Footholds and swing trajectories are computed based on the terrain, and the robot body pose is optimized to ensure stable locomotion while not colliding with the environment. Our method is designed to run online on a real robot and generate trajectories several meters long. We first tested our algorithm in several simulations with varied confined spaces using the quadrupedal robot ANYmal. We also simulated experiments with the hexapod robot Weaver to demonstrate applicability to different legged robot configurations. Then, we demonstrated our whole‐body planner in several online experiments both indoors and in realistic scenarios at an emergency rescue training facility. ANYmal, which has a nominal standing height of 80 cm and a width of 59 cm, navigated through several representative disaster areas with openings as small as 60 cm. Three‐meter trajectories were replanned with 500 ms update times.     

Learning cooperative grasping with the graph representation of a state-action space

In this paper we present a method for two robot manipulators to learn cooperative tasks. If a single robot is unable to grasp an object in a certain orientation, it can only continue with the help of other robots. The grasping can be realized by a sequence of cooperative operations that re-orient the object. Several sequences are needed to handle the different situations in which an object is not graspable for the robot. It is shown that a distributed learning method based on a Markov decision process is able to learn the sequences for the involved robots, a master robot that needs to grasp and a helping robot that supports him with the re-orientation. A novel state-action graph is used to store the reinforcement values of the learning process. Further an example of aggregate assembly shows the generality of this approach.     

Demonstration and Analysis of Quadrupedal Passive Dynamic Walking

Animals, including human beings, can travel in a variety of environments adaptively. Legged locomotion makes this possible. However, legged locomotion is temporarily unstable and finding out the principle of walking is an important matter for optimum locomotion strategy or engineering applications. As one of the challenges, passive dynamic walking has been studied on this. Passive dynamic walking is a walking phenomenon in which a biped walking robot with no actuator walks down a gentle slope. The gait is very smooth (like a human) and much research has been conducted on this. Passive dynamic walking is mainly about bipedalism. Considering that there are more quadruped animals than bipeds and a four-legged robot is easier to control than a two-legged robot, quadrupedal passive dynamic walking must exist. Based on the above, we studied saggital plane quadrupedal passive dynamic walking simulation. However, it was not enough to attribute the result to the existence of quadrupedal passive dynamic walking. In this research, quadrupedal passive dynamic walking is experimentally demonstrated by the four-legged walking robot 'Quartet 4'. Furthermore, changing the type of body joint, slope angle, leg length and variety of gaits (characteristics in four-legged animals) was observed passively. Experimental data could not have enough walking time and could not change parameters continuously. Then, each gait was analyzed quantitatively by the experiment and three-dimensional simulation.     

A new simulation based robot command library applied to three robots

A new type of high‐level robot command library is presented and demonstrated. Three robot programming languages have been analyzed and new robot command libraries created for three types of robot. The programming of three robots using the new high‐level robot command library demonstrated that it was possible to program robots with different kinematic configurations without the programmer having any knowledge of the physical structure of the robots. The library commands contained simulations of the abilities of the robots as well as having the ability to control the physical robots. This paper shows how simulation can be incorporated into a high‐level robot command library and how the command library can be used for the programming of three industrial robots. ©1999 John Wiley & Sons, Inc.     

Altruistic coordination for multi-robot cooperative pathfinding

When multiple robots perform tasks in a shared workspace, they might be confronted with the risk of blocking each other’s ways, which will lead to conflicts or interference among them. Planning collision-free paths for all the robots is a challenge for a multi-robot system, which is also known as the multi-robot cooperative pathfinding problem in which each robot has to navigate from its starting location to the destination while keeping avoiding stationary obstacles as well as the other robots. In this paper, we present a novel fully decentralized approach to this problem. Our approach allows robots to make real-time responses to dynamic environments and can resolve a set of benchmark deadlock situations subject to complex spatial constraints in a shared workspace by means of altruistic coordination. Specifically, when confronted with congested situations, each robot can employ waiting, moving-forwards, dodging, retreating and turning-head strategies to make local adjustments. Most importantly, each robot only needs to coordinate and communicate with the others that are located within its coordinated network in our approach, which can reduce communication overhead in fully decentralized multi-robot systems. In addition, experimental results also show that our proposed approach provides an efficient and competitive solution to this problem.     

Probabilistic network formation through coverage and freeze-tag

We address the problem of propagating a piece of information among robots scattered in an environment. Initially, a single robot has the information. This robot searches for other robots to pass it along. When a robot is discovered, it can participate in the process by searching for other robots. Since our motivation for studying this problem is to form an ad hoc network, we call it the Network Formation Problem. In this paper, we study the case where the environment is a rectangle and the robots’ locations are unknown but chosen uniformly at random. We present an efficient network formation algorithm, Stripes, and show that its expected performance is within a logarithmic factor of the optimal performance. We also compare Stripes with an intuitive network formation algorithm in simulations. The feasibility of Stripes is demonstrated with a proof-of-concept implementation.     

Building Terrain-Covering Ant Robots: A Feasibility Study

Robotics researchers have studied robots that can follow trails laid by other robots. We, on the other hand, study robots that leave trails in the terrain to cover closed terrain repeatedly. How to design such ant robots has so far been studied only theoretically for gross robot simplifications. In this article, we describe for the first time how to build physical ant robots that cover terrain and test their design both in realistic simulation environments and on a Pebbles III robot. We show that the coverage behavior of our ant robots can be modeled with a modified version of node counting, a real-time search method. We then report on first experiments that we performed to understand their efficiency and robustness in situations where some ant robots fail, they are moved without realizing this, the trails are of uneven quality, and some trails are destroyed. Finally, we report the results of a large-scale simulation experiment where ten ant robots covered a factory floor of 25 by 25 meters repeatedly over 85 hours without getting stuck.     

What robots can teach us about intimacy: The reassuring effects of robot responsiveness to human disclosure

Perceiving another person as responsive to one’s needs is inherent to the formation of attachment bonds and is the foundation for safe-haven and secure-base processes. Two studies examined whether such processes also apply to interactions with robots. In both studies, participants had one-at-a-time sessions, in which they disclosed a personal event to a non-humanoid robot that responded either responsively or unresponsively across two modalities (gestures, text). Study 1 showed that a robot’s responsiveness increased perceptions of its appealing traits, approach behaviors towards the robot, and the willingness to use it as a companion in stressful situations. Study 2 found that in addition to producing similar reactions in a different context, interacting with a responsive robot improved self-perceptions during a subsequent stress-generating task. These findings suggest that humans not only utilize responsiveness cues to ascribe social intentions to robots, but can actually use them as a source of consolation and security.     

Reliable Monte Carlo localization for mobile robots

Reliability is a key factor for realizing safety guarantee of fully autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick relocalization, simultaneously. The presented method can be implemented using a similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables a robot to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick relocalization from a failure state can be realized while mitigating noisy influence of global localization. We conduct three types of experiments using wheeled mobile robots equipped with a two-dimensional LiDAR. Results show that reliable MCL that performs robust localization, self-failure detection, and quick failure recovery can be realized.     

Mechanics of bipedal and quadrupedal locomotion on dry and wet granular media

The effectiveness of exploration robots is contingent upon their capability and efficiency to locomote on contorted and multifaceted terrains. Traditional wheeled robots do not suffice at overcoming such terrains and complications they introduce. The proposed work explores the performance of WhegRunner, a whegged (i.e., wheel-legged) robotic platform used to examine the mechanics of running on granular media at different saturation levels. In particular, the effect of bipedal/quadrupedal gait, saturation level, stride length, and stride frequency on robot's forward body velocity and cost of transport (COT) was studied. To increment nominal stride length, the robot was fitted with different number of spoked whegs from 3 to 7. In addition, eight evenly spaced motor speeds ranging from 2.33 to 7.43 Hz were used to observe the effect of stride frequency. Similar trends were observed between the two gaits, with the quadrupedal gait showing better overall performance. On dry sand, wider strides were attained at reduced motor speeds with lower spoked whegs. However, on wet sand (15% and 30% saturation), wider strides were achieved with higher motor speeds and lower spoked whegs. As a result, lower spoked whegs accomplished higher body forward velocity and lower COT as saturation increased. The acquired knowledge elucidates underexplored mechanics of locomotion on granular media at different saturation levels. The WhegRunner can be used as a platform to obtain a greater insight on how to develop more proficient exploration robots, ones which can face complex and deformable terrains and mediums.     

A hierarchical reinforcement learning approach for optimal path tracking of wheeled mobile robots

Robust motion control is fundamental to autonomous mobile robots. In the past few years, reinforcement learning (RL) has attracted considerable attention in the feedback control of wheeled mobile robot. However, it is still difficult for RL to solve problems with large or continuous state spaces, which is common in robotics. To improve the generalization ability of RL, this paper presents a novel hierarchical RL approach for optimal path tracking of wheeled mobile robots. In the proposed approach, a graph Laplacian-based hierarchical approximate policy iteration (GHAPI) algorithm is developed, in which the basis functions are constructed automatically using the graph Laplacian operator. In GHAPI, the state space of an Markov decision process is divided into several subspaces and approximate policy iteration is carried out on each subspace. Then, a near-optimal path-tracking control strategy can be obtained by GHAPI combined with proportional-derivative (PD) control. The performance of the proposed approach is evaluated by using a P3-AT wheeled mobile robot. It is demonstrated that the GHAPI-based PD control can obtain better near-optimal control policies than previous approaches.     

CONRO: Towards Deployable Robots with Inter-Robots Metamorphic Capabilities

Metamorphic robots are modular robots that can reconfigure their shape. Such capability is desirable in tasks such as earthquake search and rescue and battlefield surveillance and scouting, where robots must go through unexpected situations and obstacles and perform tasks that are difficult for fixed-shape robots. The capabilities of the robots are determined by the design specification of their modules. In this paper, we present the design specification of a CONRO module, a small, self-sufficient and relatively homogeneous module that can be connected to other modules to form complex robots. These robots have not only the capability of changing their shape (intra-robot metamorphing) but also can split into smaller robots or merge with other robots to create a single larger robot (inter-robot metamorphing), i.e., CONRO robots can alter their shape and their size. Thus, heterogeneous robot teams can be built with homogeneous components. Furthermore, the CONRO robots can separate the reconfiguration stage from the locomotion stage, allowing the selection of configuration-dependent gaits. The locomotion and automatic inter-module docking capabilities of such robots were tested using tethered prototypes that can be reconfigured manually. We conclude the paper discussing the future work needed to fully realize the construction of these robots.     

Hard material small-batch industrial machining robot

Hard materials can be cost effectively machined with standard industrial robots by enhancing current state-of-the-art technologies. It is demonstrated that even hard metals with specific robotics-optimised novel hard-metal tools can be machined by standard industrial robots with an improved position-control approach and enhanced compliance-control functions. It also shows that the novel strategies to compensate for elastic robot errors, based on models and advanced control, as well as the utilisation of new affordable sensors and human-machine interfaces, can considerably improve the robot performance and applicability of robots in machining tasks. In conjunction with the development of safe robots for human-robot collaboration and cooperation, the results of this paper provide a solid background for establishing industrial robots for industrial-machining applications in both small- and medium-size enterprises and large industry. The planned short-term and long-term exploitation of the results should significantly increase the future robot usage in the machining operations.     

Conflict free coordinated path planning for multiple robots using a dynamic path modification sequence

In this paper, a practically viable approach for conflict free, coordinated motion planning of multiple robots is proposed. The presented approach is a two phase decoupled method that can provide the desired coordination among the participating robots in offline mode. In the first phase, the collision free path with respect to stationary obstacles for each robot is obtained by employing an A* algorithm. In the second phase, the coordination among multiple robots is achieved by resolving conflicts based on a path modification approach. The paths of conflicting robots are modified based on their position in a dynamically computed path modification sequence (PMS). To assess the effectiveness of the developed methodology, the coordination among robots is also achieved by different strategies such as fixed priority sequence allotment for motion of each robot, reduction in the velocities of joints of the robot, and introduction of delay in starting of each robot. The performance is assessed in terms of the length of path traversed by each robot, time taken by the robot to realize the task and computational time. The effectiveness of the proposed approach for multi-robot motion planning is demonstrated with two case studies that considered the tasks with three and four robots. The results obtained from realistic simulation of multi-robot environment demonstrate that the proposed approach assures rapid, concurrent and conflict free coordinated path planning for multiple robots.     

Accelerating Robot Development Through Integral Analysis of Human–Robot Interaction

The development of interactive robots is a complicated process, involving a plethora of psychological, technical, and contextual influences. To design a robot capable of operating ldquointelligentlyrdquo in everyday situations, one needs a profound understanding of human-robot interaction (HRI). We propose an approach based on integral analysis of multimodal data to pursue this understanding and support interdisciplinary research and development in the field of robotics. To adopt this approach, a software tool named interaction debugger was developed that features user-friendly navigation, browsing, searching, viewing, and annotation of data; it enables fine-grained inspection of the HRI. In four case studies, we demonstrated how our analysis approach aids the development process of interactive robots.     

Integrating human observer inferences into robot motion planning

Our goal is to enable robots to produce motion that is suitable for human–robot collaboration and co-existence. Most motion in robotics is purely functional, ideal when the robot is performing a task in isolation. In collaboration, however, the robot’s motion has an observer, watching and interpreting the motion. In this work, we move beyond functional motion, and introduce the notion of an observer into motion planning, so that robots can generate motion that is mindful of how it will be interpreted by a human collaborator. We formalize predictability and legibility as properties of motion that naturally arise from the inferences in opposing directions that the observer makes, drawing on action interpretation theory in psychology. We propose models for these inferences based on the principle of rational action, and derive constrained functional trajectory optimization techniques for planning motion that is predictable or legible. Finally, we present experiments that test our work on novice users, and discuss the remaining challenges in enabling robots to generate such motion online in complex situations.