A Vision-based Scheme for Kinematic Model Construction of Re-configurable Modular Robots

Re-configurable modular robotic (RMR) systems are advantageous for their reconfigurability and versatility. A new modular robot can be built for a specific task by using modules as building blocks. However, constructing a kinematic model for a newly conceived robot requires significant work. Due to the finite size of module-types, models of all module-types can be built individually and stored in a database beforehand. With this priori knowledge, the model construction process can be automated by detecting the modules and their corresponding interconnections. Previous literature proposed theoretical frameworks for constructing kinematic models of modular robots, assuming that such information was known a priori. While well-devised mechanisms and built-in sensors can be employed to detect these parameters automatically, they significantly complicate the module design and thus are expensive. In this paper, we propose a vision-based method to identify kinematic chains and automatically construct robot models for modular robots. Each module is affixed with augmented reality (AR) tags that are encoded with unique IDs. An image of a modular robot is taken and the detected modules are recognized by querying a database that maintains all module information. The poses of detected modules are used to compute: (i) the connection between modules and (ii) joint angles of joint-modules. Finally, the robot serial-link chain is identified and the kinematic model constructed and visualized. Our experimental results validate the effectiveness of our approach. While implementation with only our RMR is shown, our method can be applied to other RMRs where self-identification is not possible

Improving robotic machining accuracy through experimental error investigation and modular compensation

Machining using industrial robots is currently limited to applications with low geometrical accuracies and soft materials. This paper analyzes the sources of errors in robotic machining and characterizes them in amplitude and frequency. Experiments under different conditions represent a typical set of industrial applications and allow a qualified evaluation. Based on this analysis, a modular approach is proposed to overcome these obstacles, applied both during program generation (offline) and execution (online). Predictive offline compensation of machining errors is achieved by means of an innovative programming system, based on kinematic and dynamic robot models. Real-time adaptive machining error compensation is also provided by sensing the real robot positions with an innovative tracking system and corrective feedback to both the robot and an additional high-dynamic compensation mechanism on piezo-actuator basis

Automatic Modeling for Modular Reconfigurable Robotic Systems: Theory and Practice

A modular reconfigurable robot consists of a collection of individual link and joint components that can be assembled into a number of different robot ge-ometries. Compared to a conventional industrial robot with fixed geometry, such a system can provide flexibility to the user to cope with a wide spectru

On adaptive robot systems for manufacturing applications

System adaptability is very important to current manufacturing practices due to frequent changes in the customer needs. Two basic concepts that can be employed to achieve system adaptability are flexible systems and modular systems. Flexible systems are fixed integral systems with some adjustable components. Adjustable components have limited ranges of parameter changes that can be made, thus restricting the adaptability of systems. Modular systems are composed of a set of pre-existing modules. Usually, the parameters of modules in modular systems are fixed, and thus increased system adaptability is realized only by increasing the number of modules. Increasing the number of modules could result in higher costs, poor positioning accuracy, and low system stiffness in the context of manufacturing applications. In this thesis, a new idea was formulated: a combination of the flexible system and modular system concepts. Systems developed based on this new idea are called adaptive systems. This thesis is focused on adaptive robot systems. An adaptive robot system is such that adaptive components or adjustable parameters are introduced upon the modular architecture of a robot system. This implies that there are two levels to achieve system adaptability: the level where a set of modules is appropriately assembled and the level where adjustable components or parameters are specified. Four main contributions were developed in this thesis study. First, a General Architecture of Modular Robots (GAMR) was developed. The starting point was to define the architecture of adaptive robot systems to have as many configuration variations as possible. A novel application of the Axiomatic Design Theory (ADT) was applied to GAMR development. It was found that GAMR was the one with the most coverage, and with a judicious definition of adjustable parameters. Second, a system called Automatic Kinematic and Dynamic Analysis (AKDA) was developed. This system was a foundation for synthesis of adaptive robot configurations. In comparison with the existing approach, the proposed approach has achieved systemization, generality, flexibility, and completeness. Third, this thesis research has developed a finding that in modular system design, simultaneous consideration of both kinematic and dynamic behaviors is a necessary step, owing to a strong coupling between design variables and system behaviors. Based on this finding, a method for simultaneous consideration of type synthesis, number synthesis, and dimension synthesis was developed. Fourth, an adaptive modular Parallel Kinematic Machine (PKM) was developed to demonstrate the benefits of adaptive robot systems in parallel kinematic machines, which have found many applications in machine tool industries. In this architecture, actuators and limbs were modularized, while the platforms were adjustable in such a way that both the joint positions and orientations on the platforms can be changed

Self-sufficiency of an autonomous self-reconfigurable modular robotic organism

In recent years, getting inspiration from simple but complex biological organisms, several advances have been seen in autonomous systems to mimic different behaviors that emerge from the interactions of a large group of simple individuals with each other and with the environment. Among several open issues a significantly important issue, not addressed so far, is the self-sufficiency, or in other words, the energetic autonomy of a modular robotic organism. This feature plays a pivotal role in maintaining a robotic organism\u27s autonomy for a longer period of time. To address the challenges of self-sufficiency, a novel dynamic power management system (PMS) with fault tolerant energy sharing is proposed, realized in the form of hardware and software, and tested. The innate fault tolerant feature of the proposed PMS ensures power sharing in an organism despite docked faulty robotic modules. Due to the unavailability of sufficient number of real robotic modules a simulation framework called Replicator Power Flow Simulator is devised for the implementation of application software layer power management components. The simulation framework was especially devised because at the time of writing this work no simulation tool was available that could be used to perform power sharing and fault tolerance experiments at an organism level. The simulation experiments showed that the proposed application software layer dynamic power sharing policies in combination with the distributed fault tolerance feature in addition to self-sufficiency are expected to enhance the robustness and stability of a real modular robotic organism under varying conditions.Inspiriert von einfachen aber komplexen biologischen Organismen wurden in den letzten Jahren verschiedenste autonome Systeme entwickelt, welche die Verhaltensweisen einer großen Gruppe einfacher Individuen nachahmen. Das zentrale und bis heute ungelöste Problem dieser Organismen ist deren autonome Energieversorgung. Zur Sicherstellung der Energieversorgung eines aus mehreren Robotern zusammengesetzten Organismus wurde in dieser Arbeit ein neuartiges Power-Management-System (PMS) konzipiert, aufgebaut und an einzelnen Robotermodulen und einem Roboterorganismus getestet. Die Hardware eines bestehenden Roboters wurde um ein neues Konzept erweitert, das auch bei fehlerhaften Robotermodulen einen Energieaustausch sicherstellt und so zu einer erhöhten Robustheit des PMS führen soll. Das entwickelte PMS wurde in modulare Roboter integriert und beispielhaft anhand eines Roboterorganismus getestet. In Ermangelung einer ausreichenden Anzahl von Robotermodulen wurde eine Simulationsumgebung entwickelt und die Software des PMS im Simulationsprogramm, anstatt im Roboter, implementiert. Dieses Simulationswerkzeug ist momentan das Einzige, das unter Berücksichtigung des Bewegungsmodells des Organismus den Energietransport im Roboterorganismus visuell darstellt und das Verhalten in verschiedenen Fehlerfällen simulieren kann. Die Simulationen und Messungen zeigen, dass das entwickelte PMS geeignet ist, die Energieversorgung von Roboterorganismen auch in Fehlerfällen sicherzustellen und so die Stabilität und Robustheit zu erhöhen

Graphical modelling of modular machines

This research is aimed at advancing machine design through specifying and implementing (in "proof of concept" form) a set of tools which graphically model modular machines. The tools allow mechanical building elements (or machine modules) to be selected and configured together in a highly flexible manner so that operation of the chosen configuration can be simulated and performance properties evaluated. Implementation of the tools has involved an extension in capability of a proprietary robot simulation system. This research has resulted in a general approach to graphically modelling manufacturing machines built from modular elements. A focus of study has been on a decomposition of machine functionality leading to the establishment of a library of modular machine primitives. This provides a useful source of commonly required machine building elements for use by machine designers. Study has also focussed on the generation of machine configuration tools which facilitate the construction of a simulation model and ultimately the physical machine itself. Simulation aspects of machine control are also considered which depict methods of manipulating a machine model in the simulation phase. In addition methods of achieving machine programming have been considered which specify the machine and its operational tasks. Means of adopting common information data structures are also considered which can facilitate interfacing with other systems, including the physical machine system constructed as an issue of the simulation phase. Each of these study areas is addressed in its own context, but collectively they provide a means of creating a complete modular machine design environment which can provide significant assistance to machine designers. Part of the methodology employed in the study is based on the use of the discrete event simulation technique. To easily and effectively describe a modular machine and its activity in a simulation model, a hierarchical ring and tree data structure has been designed and implemented. The modularity and reconfigurability are accommodated by the data structure, and homogeneous transformations are adopted to determine the spatial location and orientation of each of the machine elements. A three-level machine task programming approach is used to describe the machine's activities. A common data format method is used to interface the machine design environment with the physical machine and other building blocks of manufacturing systems (such as CAD systems) where systems integration approaches can lead to enhanced product realisation. The study concludes that a modular machine design environment can be created by employing the graphical simulation approach together with a set of comprehensive configuration. tools. A generic framework has been derived which outlines the way in which machine design environments can be constructed and suggestions are made as to how the proof of concept design environment implemented in this study can be advanced

Navigating in 3D Uneven Environments through Supervoxels and Nonlinear MPC

Navigating uneven and rough terrains presents difficulties, including stability, traversability, sensing, and robustness, making autonomous navigation in these terrains a challenging task. This study introduces a new approach for mobile robots to navigate uneven terrains. The method uses a compact graph of traversable regions on point cloud maps, created through the utilization of supervoxel representation of point clouds. By using this supervoxel graph, the method navigates the robot to any reachable goal pose by utilizing a navigation function and Nonlinear Model Predictive Controller (NMPC). The NMPC ensures kinodynamically feasible and collision-free motion plans, while the supervoxel-based geometric planning generates near-optimal plans by exploiting the terrain information. We conducted extensive navigation experiments in real and simulated 3D uneven terrains and found that the approach performs reliably. Additionally, we compared resulting motion plans to some state-of-the-art sampling-based motion planners in which our method outperformed them in terms of execution time and resulting path lengths. The method can also be adapted to meet specific behavior, like the shortest route or the path with the least slope route. The source code is available in a GitHub repository