Intuitive control of mobile robots: an architecture for autonomous adaptive dynamic behaviour integration.

Simultaneous control of many MEMS microrobots through a common, global, control signal is one of the grand unsolved challenges of microrobotics. At present, most mobile, aqueous, or aerial microrobots, are actuated and maneuvered using global externally supplied fields. The differentiation of the microrobot motion within a larger swarm is achieved through differences in their design that lead to different responses to these global fields. Such differentiation imposes a limit on the number of independently controllable microrobots due to the finite number of significant different levels (e.g. voltages) of these applied fields (or signals). In this paper, we present a new paradigm for control of large numbers of MEMS microrobots through a global control signal by introducing a mechanism call physical finite state machines (PFSM). PFSM can causes a change in the motion of the robot based on a temporal sequence in the global control and power delivery signal. We show that a PFSM can be implemented using stress-engineered MEMS microrobots, and show that the previously presented sub-linear control voltage bandwidth of O/n is a direct consequence of the application of an on-board two-state PFSM. We further show that the PFSM concept can be extended to on-board multistate FSM, and can, in theory further reduce the control voltage bandwith to O(c), a constant bound.

Coordinating mobile robots are widely used in commercial and industrial settings to fulfill various tasks. However, to program the coordination among mobile robots is challenging. A coordination framework is needed to shield the programmer from handling low-level details of robot control and communication, while supporting flexible and cost-effective coordination at the same time. The coordination framework should also be able to well coexist with the underlying robot control. To this end, we propose the Coordination-enabled Behavior-Based Robotics (CBBR) framework. CBBR employs Distributed Shared Memory (DSM) to support coordination. The shared memory illusion built by the DSM greatly simplifies the coordination logic. Moreover, the flexible access patterns of the DSM and the rich consistency semantics of the DSM reads and writes enable flexible and cost-effective coordination. With the coordination support from the DSM, CBBR naturally extends the classical Behavior-Based Robotics (BBR) for robot control. From the perspective of robot control using BBR, the shared variables in the DSM act as the logical sensors capturing the status of coordination. The coordination algorithms are encapsulated into coordination behaviors. Thus, the physical environment status and the coordination status may trigger the physical and the coordination behaviors. The scheduling of both types of behaviors integrates coordination into robot control. We conduct a case study to demonstrate the use of CBBR. The performance measurements show the cost-effectiveness of coordinating mobile robots based on CBBR, in terms of time, space, and energy consumption.

Fully autonomous robot systems, though highly desired, face substantial theoretical and practical challenges when being deployed into a dynamic environment where humans co-exist. To tackle this challenge, this thesis investigates the concept of human-in-the-loop (HITL) systems, which incorporate human input to enhance robot functionality. HITL systems offer a pragmatic alternative, combining human versatility with robotic precision. This research aims to address critical questions in one particular HITL architecture which prioritizes the dominant role of the human within the system, positioning the robot primarily in an assistive capacity that adheres to human commands to facilitate the achievement of a shared goal. It explores two primary paradigms of shared assistance methods-Shared Control (SC) and Shared Autonomy (SA)-and discusses the system designs as well as specific algorithms to implement the three critical components in a HITL system: human intention estimation, modulation of human inputs and robot autonomy, and the human-robot communication channel. Due to the variety of use cases and their unique challenges, four distinct HITL systems are developed and analyzed to illustrate how shared assistance methods could be employed to assist human operators: an assistive wheelchair for indoor navigation, a human-centered robot system design for industrial tasks, a mobile bi-manual robot for tele-manipulation, and a virtual reality (VR)-based customizable shared control system for fine teleoperation. Although each system represents a comprehensive robotic solution, the research contributions for each work vary. In the assistive wheelchair navigation system, the focus was on human intent estimation via a low-throughput interface utilizing a recursive Bayesian filter, with significant efforts dedicated to developing a real-time user interface serving as the communication channel. In the human-robot collaboration system for industrial setting, the emphasis was on human state estimation through camera-based posture tracking and exploring the interplay between robot behavior and human ergonomics. For the two teleoperation systems, the primary focus was on the real-time modulation of human inputs and robot autonomy to aid in achieving dexterous manipulation tasks. A novel VR-based user interface was developed to enable users to customize the robot autonomy level. Each system was validated through a pilot study involving 10-20 human subjects, accompanied by extensive data analysis to provide insights into designing HITL systems for various applications. In conclusion, this thesis contributes to a deeper understanding of HITL systems, highlighting their potential to enhance human productivity, ergonomics, and quality of life in varied applications through concrete examples. The integration of human intent estimation and real-time shared control methods into robotic systems demonstrates the feasibility and benefits of HITL approaches. Our extensive experimental analysis underscores the critical role of human feedback in designing practical HITL systems that can be deployed in real-world scenarios. --Author's abstract

The European Community's strategic research initiative in information technology (ESPRIT) has been in place for nearly five years. An early example of the pan-European collaborative projects being conducted under this initiative is 'SKIDS': Signal and Knowledge Integration with Decisional Control for Multisensory Systems. This four year project, which is approaching completion, aims to build a real-time multisensor perception machine. This machine will be capable of performing data fusion, interpretation, situation assessment, and resource allocation tasks, under the constraints of both time and resource availability, and in the presence of uncertain data. Of the many possible applications, the surveillance and monitoring of a semi-automated 'factory environment' has been chosen as a challenging and representative test scenario. This paper presents an overview of the goals and objectives of the project, the makeup of the consortium, and roles of the members within it, and the main technical achievements to data. In particular, the following are discussed: relevant application domains, and the generic requirements that can be inferred from them; sensor configuration, including choice, placement, etc.; control paradigms, including the possible trade-offs between centralized, hierarchical, and decentralized approaches; the corresponding hardware architectural choices, including the need for parallel processing; and the appropriate software architecture and infra-structure required to support the chosen task oriented approach. Specific attention is paid to the functional decomposition of the system and how the requirements for control impact the organization of the identified interpretation tasks. Future work and outstanding problems are considered in some concluding remarks. By virtue of limited space, this paper is descriptive rather than explanatory.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Transferring motion instructions from a user enables robots to perform new and unforeseen operations. Robot collectives, in particular, offer greater adaptability to changing tasks and environments. However, effectively transferring motion instructions becomes challenging as the collective's shape and size evolve. These changes often require additional system constraints to maintain robust motion control, which typically depends on pre-programmed knowledge of new tasks, ultimately limiting the collective's adaptability. To overcome the above challenges, we propose a physical and computational platform for user-guided control of self-reconfigurable modular robots. This platform consists of an optimization scheme for online processing of user commands, which prevents any modular robot actions that would violate system or environment constraints. The second component consists of Joint-space Joysticks, which match the robot's morphology, enabling the user to control diverse and dynamically changing modular robot structures through direct physical interaction. We present a platform that enables users to safely control modular, shape-changing robots through a physical interface. We demonstrate the platform's efficacy and generalizability across a diverse set of modular robot morphologies using two independent robotic systems - Mori3 and Roombots - performing a range of tasks including pick-and-place, human assistance, legged locomotion, and workspace expansion.

We introduce a novel control mode for Scanning Tunneling Microscope (STM) that leverages di/dz feedback. By superimposing a high-frequency sinusoidal modulation on the control signal, we extract the amplitude of the resulting tunneling current to obtain a di/dz measurement as the tip is scanned over the surface. A feedback control loop is then closed to maintain a constant di/dz, enhancing the sensitivity of the tip to subtle surface variations throughout a scan. This approach offers distinct advantages over conventional constant-current imaging. We demonstrate the effectiveness of this technique through high-resolution imaging and lithographic experiments on several Si(100)-2 × 1:H surfaces. Our findings, validated across multiple STM systems and imaging conditions, pave the way for a new paradigm in STM control, imaging, and lithography.

In this paper, a brain-computer interface (BCI) system based on steady-state visually evoked EEG potentials (SSVEP) has been presented to steer a NAO humanoid robot, in which a novel robot control paradigm with two-layer-interface was designed and implemented. The scene captured from robot camera was divided into different target regions and displayed to the human subjects with extra visual stimulus superposed on it to reduce the influence of dynamic background scene and the side-effect of gaze shift. The visually evoked potentials was captured from the subjects' EEG signals to accomplish the directions selection mission and to control the NAO robot's movements towards the target region. All the subjects could complete the mission using our BCI paradigm in the real-world environment. The results showed the feasibility of our BCI paradigm in brain-actuated robot control.

This paper presents a two-part study investigating the use of forearm surface electromyographic (EMG) signals for real-time control of a robotic arm. In the first part of the study, we explore and extend current classification-based paradigms for myoelectric control to obtain high accuracy (92-98%) on an eight-class offline classification problem, with up to 16 classifications/s. This offline study suggested that a high degree of control could be achieved with very little training time (under 10 min). The second part of this paper describes the design of an online control system for a robotic arm with 4 degrees of freedom. We evaluated the performance of the EMG-based real-time control system by comparing it with a keyboard-control baseline in a three-subject study for a variety of complex tasks.

TRANSCRIPTIONAL CONTROL OF CELL CYCLE PROGRESSION: THE HISTONE GENE IS A PARADIGM FOR THE G1/S PHASE AND PROLIFERATION/DIFFERENTIATION TRANSITIONS

This paper proposes an approach to representing robot morphology and control, using a two-level description linked to two different physical axes of development. The bioinspired encoding produces robots with animal-like bilateral limbed morphology with co-evolved control parameters using a central pattern generator-based modular artificial neural network. Experiments are performed on optimizing a simple simulated locomotion problem, using multi-objective evolution with two secondary objectives. The results show that the representation is capable of producing a variety of viable designs even with a relatively restricted set of parameters and a very simple control system. Furthermore, the utility of a cumulative encoding over a non-cumulative approach is demonstrated. We also show that the representation is viable for real-life reproduction by automatically generating CAD files, 3D printing the limbs, and attaching off-the-shelf servomotors.

The paper studies the formed body after completing its autonomous formation task and then proposes a self-healing method that flexibly scales with the scale of the loss in the situation that a large-scale swarm robot is locally missing due to external disturbances. With the stratified mechanism that includes layer-by-layer separation, layer-by-layer filling and iteration loop, the macro-level behavior of a swarm robot is transformed to the local action of an individual robot within the edge layers of the current aggregate. Based on the existing formation technique, driven by the shape genes of the formed body and the changes in the scale of population, the self-healing goal of the large scale of population is transformed into the similar scale of the formed body after scaling. Through the self-healing movement control method centered on individual behavior rules, the bottom-up self-healing configuration is presented on a scaled scale. The paper verifies the feasibility and scalability of this novel method through simulations. The maximum number of robots can reach tens of thousands. Finally, the self-healing method is used to implement the Cilibot robot, a hardware system developed by our laboratory.

Bidirectional Human-Robot Learning: Imitation and Skill Improvement

A biomimetic reach and grasp approach for mechanical hands

The majority of industrial robots come with position/velocity control architectures which allow precise positioning and accurate path-following, but they fail when performing contact tasks for finishing stages. To address this issue, impedance and force control have been proposed as an alternative to the position/velocity control paradigm. However, while the former requires torque control and hence cannot be applied to most of the position/velocity-based industrial robots, the latter comes with issues such as limited bandwidth and instability. A more recent approach consists of mounting an active pneumatic tool on the robot end-effector with the aim of regulating the contact force independently from the robot control architecture. Nevertheless, such active tools are bulky and hence require robots with high payload. In this paper we present a prototype of a novel agonist-antagonist (A-A) cable-driven active tool that is light-weight and it has been conceived to control the impedance at the contact interface. In order to estimate and analyze the bandwidth, the A-A system will reproduce the behavior of elastic band which have a perfect bandwidth with a known stiffness. Preliminary experiments and results are presented in order to evaluate the goodness of the rendered impedance.

In this paper, a fuzzy controller is developed for of an autonomous nonholonomic mobile robot, which was successfully built with behavior-based artificial intelligence that is implemented by several levels of competences and behaviors. The Lyapunov's direct method is used to formulate a class of control laws that guarantee the convergence of the steering errors to zero. Certain constraints for the control laws are presented for the selection of a suitable rule base for the fuzzy controller, which makes the system asymptotically stable. The stability of the proposed fuzzy controller is proved theoretically and demonstrated by simulation studies. Experiments are also conducted to investigate the performance of the developed fuzzy controller.