Control Scheme For Robots Research Articles

Position control for hybrid infinite-continuous hyper-redundant robot

This paper presents a new conception and analyzes a hyperredundant continuous robot (continuous style manipulator), drive system, and control strategy. The robot includes ten flexible segments and can be extended to several components as needed. The chosen hyper-redundant robot has a continuous infinite hybrid structure (HHRIC), based on hydraulic control with a rheological element. This system combines the advantage of a joint-level drive with a lightweight construction similar to the base-driven robots. It is suitable for tasks such as wiring in hard-toreach areas (caves, subaccounts, steep areas), transportation of fluids or food to areas affected by natural disasters (people buried under ruins), exploration in difficult areas (speleological research). Generally, the control algorithms for hyper-redundant robots are specific to the robots’ constructive particularities to which they have applied and the environment in which they operate. Experimental results validate the proposal robot design and control strategies in virtual reality. As a result, it is concluded that hyper-redundant robots and immersive technologies should play an essential role soon in automated and teleoperation applications.

Human-Robot Interaction Control for Robot Driven by Variable Stiffness Actuator With Force Self-Sensing

Robots driven by variable stiffness actuators (VSAs) have been an important technology as they could provide intrinsic compliance for safe human-robot interaction. The internal compliance of VSA also makes it possible for the actuator to act as a torque sensor and estimate the external force. This paper presents a physical human-robot interaction control strategy for robots driven by VSA with force self-sensing. The VSA adopted in the robotic system improves the safe performance of physical human-robot interaction. The interaction force is directly estimated by measuring the internal deformation of VSA with a stiffness region control which ensures a better resolution of force estimation and avoids transgressing the limits of deformation simultaneously. Then an online estimation method for the human motion intention is developed to generate the desired trajectory based on the estimated force. The impedance control is designed to enable the robot to actively follow the desired trajectory with compliance. Under the proposed control strategy, the robot is capable of actively tracking the “motion intention” of human with the estimated interaction force, which is demonstrated through experiment studies.

Effects of Autonomous Mobile Robots on Human Mental Workload and System Productivity in Smart Warehouses: A Preliminary Study

Safety is of critical concern in human-robot collaboration. A human-in-the-loop experiment was conducted to assess the performance of the customized robot control scheme in a smart warehouse setting, where the human participants performed order picking and assembly tasks, and the mobile robot performed simulated pallet moving tasks. Three modes of human-robot interaction were tested (no robot involved, mobile robot with the empty payload and mobile robot with the full payload). Participants’ pupillary response, subjective workload as well as task performance (completion time and errors) were recorded and compared between different modes. Preliminary results indicated a slight decrease in human productivity (completion time increased from 191.4 to 204.1 seconds, p = 0.041) was well compensated by the relatively large gain from the robotics outputs. Additionally, human mental workload was negatively impacted (pupil diameter increased from 4.0 to 4.2 mm, p = 0.012) by introducing the robot into the shared workplace.

An adaptive seamless assist-as-needed control scheme for lower extremity rehabilitation robots

Most control methods deployed in lower extremity rehabilitation robots cannot automatically adjust to different gait cycle stages and different rehabilitation training modes for different impairment subjects. This article presents a continuous seamless assist-as-needed control method based on sliding mode adaptive control. A forgetting factor is introduced, and a small trajectory deviation from reference normal gait trajectory is used to learn the rehabilitation level of a human subject in real time. The assistance torque needed to complete the reference normal gait trajectory is learned through radial basis function neural networks, so that the rehabilitation robot can adaptively provide the assistance torque according to subject’s needs. The performance and efficiency of this adaptive seamless assist-as-needed control scheme are tested and validated by 12 volunteers on a rehabilitation robot prototype. The results show that the proposed control scheme could adaptively reduce the robotic assistance according to subject’s rehabilitation level, and the robotic assistance torque depends on the forgetting factor and the active participation level of subjects.

Analysis and Review of Average Fuzzy Inference Technique and Other AI Techniques Used for Robot Control and Navigation

Scientists throughout the world are working on various AI techniques to address control strategies of robots...

Path Tracking Control for Autonomous Harvesting Robots Based on Improved Double Arc Path Planning Algorithm

Focusing on the problems of big overshoot and long convergence time due to the large initial heading error, a new path tracking control strategy for autonomous harvesting robots based on improved double arc path planning is designed. Firstly, the improved double arc path planning algorithm includes global path planning and local planning. The shortest path distance is taken as the goal, so the optimal tangent arc path from the initial point to the reference straight line can be automatically planned by the global path planning. Due to the problems such as control delay in harvesting robot, the global path cannot be effectively tracked. By analyzing the influence of the non-mutable steering angle and control delay on path tracking, a new local path planning algorithm is proposed. Then, the two preview points obtained in global planning can be optimized dynamically, and the actual reliability of the algorithm can be enhanced. Secondly, the error compensation model is designed to compensate the installation error of the positioning antenna in real time, to satisfy the simplified condition of the two-wheeled vehicle model. Finally, by combining the large angle steering control method and the pure pursuit algorithm, a new hybrid control strategy is designed to solve the steering angle, and the path tracking function of harvesting robots is realized. Experiment results demonstrate that the hybrid control strategy can reduce the oscillation of driving path, enhance the convergence, then, improve working quality and efficiency of robots.

A reward–punishment feedback control strategy based on energy information for wrist rehabilitation

Based on evidence from the previous research in rehabilitation robot control strategies, we found that the common feature of the effective control strategies to promote subjects’ engagement is creating a reward–punishment feedback mechanism. This article proposes a reward–punishment feedback control strategy based on energy information. Firstly, an engagement estimated approach based on energy information is developed to evaluate subjects’ performance. Secondly, the estimated result forms a reward–punishment term, which is introduced into a standard model-based adaptive controller. This modified adaptive controller is capable of giving the reward–punishment feedback to subjects according to their engagement. Finally, several experiments are implemented using a wrist rehabilitation robot to evaluate the proposed control strategy with 10 healthy subjects who have not cardiovascular and cerebrovascular diseases. The results of these experiments show that the mean coefficient of determination ( R 2) of the data obtained by the proposed approach and the classical approach is 0.7988, which illustrate the reliability of the engagement estimated approach based on energy information. And the results also demonstrate that the proposed controller has great potential to promote patients’ engagement for wrist rehabilitation.

Biological Plausibility of Arm Postures Influences the Controllability of Robotic Arm Teleoperation.

ObjectiveWe investigated how participants controlling a humanoid robotic arm’s 3D endpoint position by moving their own hand are influenced by the robot’s postures. We hypothesized that control would be facilitated (impeded) by biologically plausible (implausible) postures of the robot.BackgroundKinematic redundancy, whereby different arm postures achieve the same goal, is such that a robotic arm or prosthesis could theoretically be controlled with less signals than constitutive joints. However, congruency between a robot’s motion and our own is known to interfere with movement production. Hence, we expect the human-likeness of a robotic arm’s postures during endpoint teleoperation to influence controllability.MethodTwenty-two able-bodied participants performed a target-reaching task with a robotic arm whose endpoint’s 3D position was controlled by moving their own hand. They completed a two-condition experiment corresponding to the robot displaying either biologically plausible or implausible postures.ResultsUpon initial practice in the experiment’s first part, endpoint trajectories were faster and shorter when the robot displayed human-like postures. However, these effects did not persist in the second part, where performance with implausible postures appeared to have benefited from initial practice with plausible ones.ConclusionHumanoid robotic arm endpoint control is impaired by biologically implausible joint coordinations during initial familiarization but not afterwards, suggesting that the human-likeness of a robot’s postures is more critical for control in this initial period.ApplicationThese findings provide insight for the design of robotic arm teleoperation and prosthesis control schemes, in order to favor better familiarization and control from their users.

COMPUTED TORQUE CONTROL FOR A SPATIAL DISORIENTATION TRAINER

A development of a robot control system is a highly complex task due to nonlinear dynamic coupling between the robot links. Advanced robot control strategies often entail difficulties in implementation, and prospective benefits of their application need to be analyzed using simulation techniques. Computed torque control (CTC) is a feedforward control method used for tracking of robot’s time-varying trajectories in the presence of varying loads. For the implementation of CTC, the inverse dynamics model of the robot manipulator has to be developed. In this paper, the addition of CTC compensator to the feedback controller is considered for a Spatial disorientation trainer (SDT). This pilot training system is modeled as a 4DoF robot manipulator with revolute joints. For the designed mechanical structure, chosen actuators and considered motion of the SDT, CTC-based control system performance is compared with the traditional speed PI controller using the realistic simulation model. The simulation results, which showed significant improvement in the trajectory tracking for the designed SDT, can be used for the control system design purpose as well as within mechanical design verification.

An Adaptive Method for Gait Event Detection of Gait Rehabilitation Robots.

Accurate gait event detection is necessary for control strategies of gait rehabilitation robots. However, due to personal diversity between individuals, it is a challenge for robots to detect a gait event at various stride frequencies. This paper proposes a novel method for gait event detection of a gait rehabilitation robot using a single inertial sensor mounted on the thigh. A self-adaptive threshold for detecting heel strike is obtained in real time via a linear regression model. Observable thresholds for toe off detection are constant at various stride frequencies. Experiments are conducted based on 20 healthy subjects and six hemiplegic patients wearing a gait rehabilitation robot and walking at various kinds of stride frequencies. The experimental results show that the proposed method can detect heel strike and toe off gait events within an average 2% gait cycle temporal errors and never miss two-gait event detection. Compared to the conventional thresholding method, this work presents a simple and robust application for gait event detection in healthy and hemiplegic subjects by one inertial sensor. The linear regression model can be applicable to different subjects walking at various stride frequencies.

Image Transformation and CNNs: A Strategy for Encoding Human Locomotor Intent for Autonomous Wearable Robots

Wearable robots have the potential to improve the lives of countless individuals; however, challenges associated with controlling these systems must be addressed before they can reach their full potential. Modern control strategies for wearable robots are predicated on activity-specific implementations, and testing is usually limited to a single, fixed activity within the laboratory (e.g., level ground walking). To accommodate various activities in real-world scenarios, control strategies must include the ability to safely and seamlessly transition between activity-specific controllers. One potential solution to this challenge is to the infer wearer's intent using pattern recognition of locomotion sensor data. To this end, we developed an intent recognition framework implementing convolutional neural networks with image encoding (i.e. spectrogram) that enables prediction of the upcoming locomotor activity of the wearer's next step. In this letter, we describe our intent recognition system, comprised of a mel-spectrogram and subsequent neural network architecture. In addition, we analyzed the effect of sensor locations and modalities on the recognition system, and compared our proposed system to state-of-the-art locomotor intent recognition strategies. We were able to attain high classification performance (error rate: 1.1%), which was comparable or better than previous systems.

Design and control of soft rehabilitation robots actuated by pneumatic muscles: State of the art

Robot-assisted rehabilitation has become a new mainstream trend for the treatment of stroke patients with movement disability. Pneumatic muscle (PM) is one of the most promising actuators for rehabilitation robots, due to its inherent compliance and safety features. In this paper, we conduct a systematic review on the soft rehabilitation robots driven by pneumatic muscles. This review discusses up to date mechanical structures and control strategies for PMs-actuated rehabilitation robots. A variety of state-of-the-art soft rehabilitation robots are classified and reviewed according to the actuation configurations. Special attentions are paid to control strategies under different mechanical designs, with advanced control approaches to overcome PM’s highly nonlinear and time-varying behaviors and to enhance the adaptability to different patients. Finally, we analyze and highlight the current research gaps and the future directions in this field, which is potential for providing a reliable guidance on the development of advanced soft rehabilitation robots.

Field-Based Assist-as-Needed Control Schemes for Rehabilitation Robots

This article presents assistive control schemes for rehabilitation robots. The objective was to develop control strategies to follow a predefined path by maximizing the active participation of impaired subjects during training sessions. For this purpose, the path following task was defined in the velocity domain by constructing a desired velocity field around the path, and two different controllers were designed, which adjust the intervention of the robot according to the performance of the users, delivering an assist-as-needed (AAN) feature with adequate timing freedom. First, we developed an impedance controller for velocity tracking, which gains the AAN property by means of modulating the target impedance. Second, we designed a velocity tracking controller, which adjusts its contribution, in an AAN way, by monitoring the tracking error. Utilizing force sensors, the first method does not require a compliant robotic system, while the second approach does not need force sensors but provides its best AAN performance whenever the system is compliant. The controllers were featured by a force field component to avoid large position errors. The performance of the controllers was evaluated through experimental tests performed on a planar upper-limb training robot.

Design of a Hyper-Redundant Robot and Teleoperation Using Mixed Reality for Inspection Tasks.

Hyper-redundant robots are highly articulated devices that present numerous technical challenges such as their design, control or remote operation. However, they offer superior kinematic skills than traditional robots for multiple applications. This work proposes an original and custom-made design for a discrete and hyper-redundant manipulator. It is comprised of 7 sections actuated by cables and 14 degrees of freedom. It has been optimized to be very robust, accurate and capable of moving payloads with high dexterity. Furthermore, it has been efficiently controlled from the actuators to high-level strategies based on the management of its shape. However, these highly articulated systems often exhibit complex shapes that frustrate their spatial understanding. Immersive technologies emerge as a good solution to remotely and safely teleoperate the presented robot for an inspection task in a hazardous environment. Experimental results validate the proposed robot design and control strategies. As a result, it is concluded that hyper-redundant robots and immersive technologies should play an important role in the near future of automated and remote applications.

Decentralized Control for Swarm Robots That Can Effectively Execute Spatially Distributed Tasks.

A swarm robotic system is a system in which multiple robots cooperate to fulfill a macroscopic function. Many swarm robots have been developed for various purposes. This study aims to design swarm robots capable of executing spatially distributed tasks effectively, which can be potentially used for tasks such as search-and-rescue operation and gathering scattered garbage in rooms. We propose a simple decentralized control scheme for swarm robots by extending our previously proposed non-reciprocal-interaction-based model. Each robot has an internal state, called its workload. Each robot first moves randomly to find a task, and when it does, its workload increases, and then it attracts its neighboring robots to ask for their help. We demonstrate, via simulations, that the proposed control scheme enables the robots to effectively execute multiple tasks in parallel under various environments. Fault tolerance of the proposed system is also demonstrated.

Which is the best PID variant for pneumatic soft robots an experimental study

This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots. Fabricated using soft materials, soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction. However, due to structural flexibility and compliance, mathematical models for these soft robots are nonlinear with an infinite degree of freedom (DOF). Therefore, accurate position (or orientation) control and optimization of their dynamic response remains a challenging task. Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID. However, to the best of our knowledge, there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response, i.e., reduce overshoot and settling time. In this paper, we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results. Additionally, most of the existing work on modelfree control in pneumatic soft-robotic literature use manually tuned parameters, which is a time-consuming, labor-intensive task. We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically. We presented results for both manual tuning and automatic tuning using the Ziegler-Nichols method and proposed algorithm, respectively. We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy. The experiment results show that for soft robots, the PID-controller essentially reduces to the PI controller. This behavior was observed in both manual and automatic tuning experiments; we also discussed a rationale for removing the derivative term.

A development platform for behavioral flexibility in autonomous unmanned aerial systems

Autonomous unmanned aerial systems (UAS) could supplement and eventually subsume a substantial portion of the mission set currently executed by remote pilots, making UAS more robust, responsive, and numerous than can be achieved by teleoperation alone. Unfortunately, the development of robust autonomous systems is difficult, costly, and time-consuming. Furthermore, the resulting systems often make little reuse of proven software components and offer limited adaptability for new tasks. This work presents a development platform for UAS which promotes behavioral flexibility. The platform incorporates the unified behavior framework (a modular, extensible autonomy framework), the robotic operating system (a robotic software framework), and PX4 (an open-source flight controller). Simulation of UBF agents identify a combination of reactive robotic control strategies effective for small-scale navigation tasks by a UAS in the presence of obstacles. Finally, flight tests provide a partial validation of the simulated results. The development platform presented in this work offers robust and responsive behavioral flexibility for UAS agents in simulation and reality using a methodology originally proven on ground robots.

Research on assist-as-needed control strategy of wrist function-rehabilitation robot

In order to stimulate the patients' active participation in the process of robot-assisted rehabilitation training of stroke patients, the rehabilitation robots should provide assistant torque to patients according to their rehabilitation needs. This paper proposed an assist-as-needed control strategy for wrist rehabilitation robots. Firstly, the ability evaluation rules were formulated and the patient's ability was evaluated according to the rules. Then the controller was designed. Based on the evaluation results, the controller can calculate the assistant torque needed by the patient to complete the rehabilitation training task and send commands to motor. Finally, the motor is controlled to output the commanded value, which assists the patient to complete the rehabilitation training task. The control strategy was implemented to the wrist function rehabilitation robot, which could achieve the training effect of assist-as-needed and could avoid the surge of assistance torque. In addition, therapists can adjust multiple parameters in the ability evaluation rules online to customize the difficulty of tasks for patients with different rehabilitation status. The method proposed in this paper does not rely on the information from force sensor, which reduces development costs and is easy to implement.

A Novel Adaptive Controller for Robot Manipulators Based on Active Inference

More adaptive controllers for robot manipulators are needed, which can deal with large model uncertainties. This paper presents a novel active inference controller (AIC) as an adaptive control scheme for industrial robots. This scheme is easily scalable to high degrees-of-freedom, and it maintains high performance even in the presence of large unmodeled dynamics. The proposed method is based on active inference, a promising neuroscientific theory of the brain, which describes a biologically plausible algorithm for perception and action. In this work, we formulate active inference from a control perspective, deriving a model-free control law which is less sensitive to unmodeled dynamics. The performance and the adaptive properties of the algorithm are compared to a state-of-the-art model reference adaptive controller (MRAC) in an experimental setup with a real 7-DOF robot arm. The results showed that the AIC outperformed the MRAC in terms of adaptability, providing a more general control law. This confirmed the relevance of active inference for robot control.

Assessment of Human Dynamic Gait Stability With a Lower Extremity Assistive Device.

This paper focuses on assessing gait stability by metrics derived from dynamical systems theory to understand the influence of unilateral robot assistance on the human walking pattern. A motorized assistive robot is applied to the right knee joint to provide stance support. The metrics related to global stability (the maximum Floquet multiplier, max FM), local stability (short-term and long-term divergence exponents, [Formula: see text] and [Formula: see text]), and variability (median absolute deviation, MAD) are considered. These metrics are derived for bilateral hip, knee, and ankle joint angles. Additionally, a biomechanical metric, the minimum margin of stability is assessed. Experiments are conducted on 11 healthy participants with different robot controllers. The max FM and [Formula: see text] yield statistically significant results, showing that the unassisted (left) leg is more stable in right knee assistance conditions when compared to the normal walking condition due to inter-limb coordination. Moreover, MAD and [Formula: see text] show that the variability and chaotic order of walking pattern during assistance are lower than those of normal walking. The proposed control strategy (automatic impedance tuning, AIT) improves local and orbital gait stability compared to existing controllers such as finite-state machine (FSM). The assessment of dynamic gait stability presented in this paper provides insights for further improving control strategies of assistive robots to help a user reach improved gait stability while maintaining appropriate variability.