Desired Velocity Research Articles

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer.

A Passivity-Based Framework for Safe Physical Human–Robot Interaction

In this paper, the problem of making a safe compliant contact between a human and an assistive robot is considered. Users with disabilities have a need to utilize their assistive robots for physical human–robot interaction (PHRI) during certain activities of daily living (ADLs). Specifically, we propose a hybrid force/velocity/attitude control for a PHRI system based on measurements from a six-axis force/torque sensor mounted on the robot wrist. While automatically aligning the end-effector surface with the unknown environmental (human) surface, a desired commanded force is applied in the normal direction while following desired velocity commands in the tangential directions. A Lyapunov-based stability analysis is provided to prove both the convergence as well as passivity of the interaction to ensure both performance and safety. Simulation as well as experimental results verify the performance and robustness of the proposed hybrid controller in the presence of dynamic uncertainties as well as safe physical human–robot interactions for a kinematically redundant robotic manipulator.

A real-time collision-free maneuver generation algorithm for autonomous driving

In this paper we propose a real-time maneuver generation algorithm for Autonomous Vehicles (AVs). Given a planar road geometry with static and moving obstacles along it, we are interested in finding collision-free maneuvers that satisfied the AV’s dynamics and subject to physical and comfort limits. Based on longitudinal and transverse coordinates, we propose a novel collision avoidance constraint and formulate a suitable maneuver regulation optimal control problem. Maneuver regulation has intrinsic robustness with respect to standard trajectory tracking that stems from the requirement of following a desired path with a desired velocity profile assigned on it. The optimization problem is solved by using a nonlinear optimal control technique that generates (local) optimal trajectories. We demonstrate the efficacy of the proposed algorithm by providing numerical computations on two different scenarios. Finally, experimental results are presented to demonstrate the efficiency of the proposed algorithm both in terms of computational effort and dynamic features captured.

A Self-Coordinating Controller with Balance-Guiding Ability for Lower-Limb Rehabilitation Exoskeleton Robot.

The restricted posture and unrestricted compliance brought by the controller during human-exoskeleton interaction (HEI) can cause patients to lose balance or even fall. In this article, a self-coordinated velocity vector (SCVV) double-layer controller with balance-guiding ability was developed for a lower-limb rehabilitation exoskeleton robot (LLRER). In the outer loop, an adaptive trajectory generator that follows the gait cycle was devised to generate a harmonious hip-knee reference trajectory on the non-time-varying (NTV) phase space. In the inner loop, velocity control was adopted. By searching the minimum L2 norm between the reference phase trajectory and the current configuration, the desired velocity vectors in which encouraged and corrected effects can be self-coordinated according to the L2 norm were obtained. In addition, the controller was simulated using an electromechanical coupling model, and relevant experiments were carried out with a self-developed exoskeleton device. Both simulations and experiments validated the effectiveness of the controller.

A ship evacuation model considering the interaction between pedestrians based on cellular automata

This paper proposes the CAIE (cellular automata with interaction effect) model to evaluate ship passenger evacuation capability. The CAIE model simulates the interaction between passengers by establishing the friction and repulsive force model between passengers, derives the formula of the desired velocity's effect on the movement ability, reflects the influence of a high-density environment on the movement velocity of personnel, and defines push-crossing behavior rules to simulate opposite-flow movement. The proposed CAIE model is verified by passenger evacuation simulation experiments in single-exit cabins under the condition of opposite passenger traffic. The results demonstrate that the CAIE model has similar passenger movement rules as the social force model in terms of the arching and faster-is-slower phenomena. The results also show that the CAIE model meets the test requirements for the MSC.1/Circ.1533 communication of the advanced evacuation simulation model. Finally, the evacuation simulation analysis is performed on a small polar cruise ship using the CAIE model, Pathfinder software, and Maritime Exodus software. The comparison results indicate that the simulation results of the three models and their distributions of evacuation time are consistent. Therefore, the proposed CAIE model can be used to evaluate the evacuation ability of ships.

Optimizing trajectories for highway driving with offline reinforcement learning

Achieving feasible, smooth and efficient trajectories for autonomous vehicles which appropriately take into account the long-term future while planning, has been a long-standing challenge. Several approaches have been considered, roughly falling under two categories: rule-based and learning-based approaches. The rule-based approaches, while guaranteeing safety and feasibility, fall short when it comes to long-term planning and generalization. The learning-based approaches are able to account for long-term planning and generalization to unseen situations, but may fail to achieve smoothness, safety and the feasibility which rule-based approaches ensure. Hence, combining the two approaches is an evident step towards yielding the best compromise out of both. We propose a Reinforcement Learning-based approach, which learns target trajectory parameters for fully autonomous driving on highways. The trained agent outputs continuous trajectory parameters based on which a feasible polynomial-based trajectory is generated and executed. We compare the performance of our agent against four other highway driving agents. The experiments are conducted in the Sumo simulator, taking into consideration various realistic, dynamically changing highway scenarios, including surrounding vehicles with different driver behaviors. We demonstrate that our offline trained agent, with randomly collected data, learns to drive smoothly, achieving velocities as close as possible to the desired velocity, while outperforming the other agents.

A humanoid robot teleoperation approach based on waist–arm coordination

PurposeIn the current teleoperation system of humanoid robots, the control between arms and the control between the waist and arms are individual and lack coordinated motion. This paper aims to solve the above problem and proposes a teleoperation control approach for a humanoid robot based on waist–arm coordination (WAC).Design/methodology/approachThe teleoperation approach based on WAC comprises dual-arm coordination (DAC) and WAC. The DAC method realizes the coordinated motion of both arms through one hand by establishing a mapping relationship between a single hand controller and the manipulated object; the WAC method realizes the coordinated motion of both arms and waist by calculating the inverse kinematic input of robotic arms based on the desired velocity of the waist and the end of both arms. An integrated teleoperation control framework provides interfaces for the above methods, and users can switch control modes online to adapt to different tasks.FindingsAfter conducting experiments on the dual-arm humanoid robot through the teleoperation control framework, it was found that the DAC method can save 27.2% of the operation time and reduce 99.9% of the posture change of the manipulated object compared with the commonly used individual control. The WAC method can accomplish a task that cannot be done by individual control. The experiments proved the improvement of both methods in terms of operation efficiency, operation stability and operation capability compared with individual control.Originality/valueThe DAC method better maintains the constraints of both arms and the manipulated object. The WAC method better maintains the constraints of the manipulated object itself. Meanwhile, the teleoperation framework integrates the proposed methods and enriches the teleoperation modes and control means.

TeCVP: A Time-Efficient Control Method for a Hexapod Wheel-Legged Robot Based on Velocity Planning.

Addressing the problem that control methods of wheel-legged robots for future Mars exploration missions are too complex, a time-efficient control method based on velocity planning for a hexapod wheel-legged robot is proposed in this paper, which is named time-efficient control based on velocity planning (TeCVP). When the foot end or wheel at knee comes into contact with the ground, the desired velocity of the foot end or knee is transformed according to the velocity transformation of the rigid body from the desired velocity of the torso which is obtained by the deviation of torso position and posture. Furthermore, the torques of joints can be obtained by impedance control. When suspended, the leg is regarded as a system consisting of a virtual spring and a virtual damper to realize control of legs in the swing phase. In addition, leg sequences of switching motion between wheeled configuration and legged configuration are planned. According to a complexity analysis, velocity planning control has lower time complexity and less times of multiplication and addition compared with virtual model control. In addition, simulations show that velocity planning control can realize stable periodic gait motion, wheel-leg switching motion and wheeled motion and the operation time of velocity planning control is about 33.89% less than that of virtual model control, which promises a great prospect for velocity planning control in future planetary exploration missions.

Cooperative predictive control for arbitrarily mixed vehicle platoons with guaranteed global optimality

AbstractThis paper studies the cooperative control problem of a mixed vehicle platoon, which is composed of connected autonomous vehicles (CAVs) and human‐driven vehicles (HDVs) in an arbitrary order. An alternating direction method of multipliers (ADMM) based distributed model predictive control (DMPC) algorithm is proposed for CAVs to lead the mixed vehicle platoon travelling in a string with anticipated inter‐vehicle spacing and a desired velocity. First, the mixed vehicle platoon is divided into multiple interrelated sub‐platoons with any two adjacent sub‐platoons having a common CAV, and then a generic model is constructed for each sub‐platoon based on the intelligent driver model for HDV and the kinematic model for CAV, respectively. Second, a local MPC controller is designed for each sub‐platoon to optimize the control inputs of CAVs with the objective of minimizing the position and velocity errors of all vehicles in the sub‐platoon, and then the ADMM is utilized to obtain the global optimal solution among local MPC controllers of all sub‐platoons. Finally, numerical simulations and experiments are carried out to verify the effectiveness of the proposed DMPC algorithm, the results of which reveal that it can reduce the computation cost significantly and ensure the control performance comparable to the centralized MPC algorithm.

Finite-time event-triggered sliding mode predictive control of unmanned underwater vehicles without velocity measurements

In this paper, a novel dual closed-loop trajectory tracking method based on model predictive control (MPC) and sliding mode control (SMC) is proposed for unmanned underwater vehicle (UUV) in three-dimensional underwater environment. On the basis of characteristics of MPC rolling optimization, the trajectory tracking problem is converted to the optimization problem of the outer loop kinematic controller to obtain the desired velocity for the inner loop, and the solution frequency is reduced through the event triggering mechanism. With the constraints of desired velocity, a finite time velocity observer which can handle unknown velocity constraints is designed. Concurrently, a dynamic controller based on recursive integral sliding mode surface is presented to eliminate the reaching phase improve the convergence rate. In addition, to eliminate overestimation of the upper bound of disturbance, a nested adaptive law is developed to design switching control law according to disturbance change. Finally, comparative simulations are conducted to illustrate the feasibility and superiority of the proposed control scheme.

A new divergence-free synthetic eddy method for generating homogeneous isotropic turbulence with a prescribed energy spectrum

A new divergence-free synthetic eddy method (SEM) for the generation of homogeneous isotropic turbulence (HIT) with a prescribed energy spectrum is presented. In the proposed method, the divergence-free consistent synthetic velocity field is generated from the curl of a vector potential field following conventional SEM methodology. A carefully designed formula is constructed for the computation of the local vector potential field induced by each synthetic eddy, with which a desired velocity spectrum tensor can be satisfied for the synthetic incompressible turbulence exactly. The proposed method is characterized by a multi-scale feature in which the length scales of synthetic eddies are determined by a dimensionless random variable satisfying a particular probability distribution function (PDF). The mathematical relation, concerning the energy spectrum of the synthetic turbulence, the shape function of the eddies and the PDF of the eddy-scale-related random variable, is derived. This assists in determining either the shape function or the PDF. The performance of the proposed method is examined by synthesizing HIT satisfying a theoretical energy spectrum model and HIT identified in a wind-tunnel experiment.

Effect of electrical stimulation amplitude on dynamic behavior of mice during evacuation

Profound examination of the dynamic behavior of pedestrians during evacuation can significantly reduce the number of associated accidents. Conducting experiments on animals can help obtain deep insight into the dynamic behavior of pedestrians. Previous experiments using insects, such as ants and woodlice, showed large differences between the dynamic behaviors of insects and humans. However, systematic studies on the behavioral characteristics (e.g., velocity) of mice under electrical stimulation conditions have not been reported. Therefore, this study was conducted to investigate changes in the dynamic behavior of mice during evacuation caused by electric shock. Electrical stimulation was supplied through their feet during evacuation. The average velocity, desired velocity (maximum instantaneous velocity), average velocity in the congestion zone, and escape time were measured and analyzed. According to the results, the desired velocity and escape time increased in proportion to the amplitude of the electrical stimulation; however, the average velocity decreased. Consequently, the level of emergency of mice is affected by both the amplitude of electrical stimulation and the number density in congestion area as in human experiments.

Bearing-Only Formation Maneuvering Control of Multi-agent Systems with Time-Varying Leaders’ Velocity

This paper studies bearing-only formation maneuver control of multi-agent systems modeled as double-integrators. The developed distributed control makes the multi-agent system form a desired geometric pattern in which the target formation is bearing-constrained. The controlled agents are supposed to sense the bearing and bearing rate with respect to several neighboring agents, and the interaction among agents is characterized by a fixed undirected graph. Different from existing control approaches where the target formation or the leaders move at a constant velocity, this paper investigates the case where the leaders’ velocity is time-varying. For each follower agent, a finite-time velocity estimator is used to estimate its desired velocity. Then, a bearing-only control law is designed for the follower using a signum function, which renders the tracking error with respect to the leaders with time-varying velocity to converge to zero. The effectiveness of the control laws is illustrated by simulations.

On the driver's stochastic nature in car-following behavior: Modeling and stabilizing based on the V2I environment

<abstract> <p>The driver's stochastic nature is one of the important causes of traffic oscillation. To better describe the impact of the driver's stochastic characteristics on car-following behavior, we propose a stochastic full velocity difference model (SFVDM) considering the stochastic variation of the desired velocity. In order to mitigate traffic oscillation caused by driving stochasticity, we further propose a stable speed guidance model (S-SFVDM) by leveraging vehicle-to-infrastructure communication. Stochastic linear stability conditions are derived to demonstrate the prominent influence of the driver's stochasticity on the stability of traffic flow and the improvement of traffic flow stability by the proposed guidance strategy, respectively. We present numerical tests to demonstrate the effectiveness of the proposed models. The results show that the SFVDM can capture the traffic oscillation caused by the driver's stochastic desired velocity and reproduce the same disturbance growth pattern as in the field experiment. The results also indicate that the S-SFVDM can significantly expand the stable area of traffic flow to decrease the negative impact on traffic flow stability caused by the driver's stochastic nature.</p> </abstract>

Switching formation control of multi-lane autonomous vehicle platoons robust to relative position measurement noises

This paper studies formation control of multi-lane platoons of vehicles, endowed with local positioning capabilities, under the influence of noises and inconsistencies of inter-vehicle relative position measurements. We propose a distributed two-level control framework that consists of a high-level relative position based distributed formation control scheme and low-level individual dynamic controllers of the platoon vehicles. The proposed formation control scheme utilizes deadzone based switching for robustness against sensor noises and adaptive longitudinal controllers for enabling the platoon of heterogeneous vehicles to track the desired platoon velocity.

Motion Planning with Specified Terminal Velocity Direction of Fixed-Wing UAVs in 3D*

This paper focuses on the motion planning problem of fixed-wing UAVs in 3D whose velocities are restricted by the nonholonomic constraints. Different from common planning which only requires the target position, we additionally bring in the velocity direction in the sense that the fixed-wing UAV should reach the desired position with the specified velocity direction at the terminal time. Besides, the fixed-wing UAV is modelled as a rigid body with six degrees of freedom rather than a particle agent. The main idea of the proposed motion planning algorithm is to design a guiding vector field over the workspace. By following the vector field, the fixed-wing can be navigated to the destination with the desired velocity direction. Simulations are conducted to verify effectiveness of the theoretical results.

Constrained Control Allocation Improving Fault Tolerance of a Four Wheel Independently Driven Articulated Vehicle

In articulated vehicles steering is accomplished by adjusting the articulation angle. Commonly a steering actuator is used to pivot the two vehicle sections. This actuator can be dispensed with if the pivoting is controlled by selective distribution of the drive torques to the individually driven wheels instead. Since steering is a safety-critical function, it must be ensured even if one of the drive motors fails. For this purpose, we propose a control method for a fault-tolerant four wheel independently driven articulated vehicle. The control method was developed and tested in a simulation environment and validated on a 1:5 scale demonstrator vehicle. The proposed method with constrained control allocation maintains the desired velocity and articulation angle by distributing the driving torques to the four wheels considering the current actuator limits. Both the simulation and the vehicle tests show that the control method meets the control objectives even when a sudden actuator limit occurs during critical driving scenarios like cornering. Thus, the vehicle can keep its maneuverability in the event of a detected failure of a drive motor. Together with reliable failure detection, the proposed approach provides a basis for further development towards innovative fault-tolerant electric articulated vehicles that meet the requirements of functional safety.

Cooperative Adaptive Cruise Control in a Mixed-Autonomy Traffic System: A Hybrid Stochastic Predictive Approach Incorporating Lane Change

This paper presents a stochastic and predictive control design approach for connected and automated vehicles (CAVs) in a mixed-autonomy traffic environment, where CAVs are able to react properly to uncertain maneuvers of human-driven vehicles (HVs). The proposed fully-automated cooperative adaptive cruise control (CACC) design leverages a discrete hybrid stochastic model predictive controller that automatically determines the vehicle's operating mode based on onboard sensors data and information received through vehicle-to-vehicle (V2V) communication. Operating modes include free following, warning, danger, emergency braking, and lane change. Although the controller mainly focuses on maintaining the desired velocity and distance among CAVs, it also allows HVs to perform lane-change maneuvers and merge into the platoon's lane when needed. In response to an HV's position in the lane and its probabilistic behavior, the controller may switch the CAV's operating mode to react accordingly. Considering free-following and emergency-braking modes leads to efficient and safe autonomous driving. Switching between warning, danger, and lane-change modes along with adjusting the steering angle to perform a lane-change maneuver, when needed, robustifies the platoon's performance against unexpected human-driven vehicle maneuvers. Simulation studies are conducted to validate the efficacy of the proposed control design approach. The performance of the proposed control design approach is also compared to a switching control using simulation studies.

Predefined-Time Bipartite Consensus of Networked Euler-Lagrange Systems via Sliding-Mode Control

This brief investigates the predefined-time bipartite consensus of multiple Euler-Lagrange systems with a dynamic leader. A new distributed predefined-time observer is added to estimate the desired velocity of each follower. Then, a new sliding surface and a distributed protocol are constructed to guarantee the networked systems achieve the bipartite consensus within a predefined time. It should be pointed out that, all the observing time, sliding time and reaching time can be preset in advance according to the task requirements. One numerical example with two-link robot arms is proposed to verify the effectiveness of the new designs.

A Convolutional Dynamic-Jerk-Planning Algorithm for Impedance Control of Variable-Stiffness Cable-Driven Manipulators.

Cable-driven manipulators, characterized by slender arms, dexterous motion, and controllable stiffness, have great prospects for application to capture on-orbit satellites. However, it is difficult to achieve effective motion planning and stiffness control of cable-driven manipulators because of the coupled relationships between cable lengths, joint angles, and reaction forces. Therefore, a convolutional dynamic-jerk-planning algorithm is devised for impedance control of variable-stiffness cable-driven manipulators. First, a variable-stiffness cable-driven manipulator with universal modules and rotary quick-change modules is designed to overcome difficulties related to disassembly, installation, and maintenance. Second, a convolutional dynamic-jerk-planning algorithm is devised to overcome the discontinuity and shock problems of the manipulator's velocity during intermittent control processes. The algorithm can also make acceleration smooth by setting jerk dynamically, reducing acceleration shock and ensuring the stable movement of the cable-driven manipulator. Third, the stiffness of the cable-driven manipulator is further optimized by compensating for the position and velocity of drive cables by employing position-based impedance control. Finally, the prototype of the variable-stiffness cable-driven manipulator is developed and tested. The convolutional dynamic-jerk-planning algorithm is used to plan the desired velocity curves for velocity control experiments of the cable-driven manipulator. The results verify that the algorithm can improve the acceleration smoothness, thereby making movement smooth and reducing vibrations. Furthermore, stiffness control experiments verify that the cable-driven manipulator has ideal variable stiffness capabilities.