Mechatronics最新文献

This paper presents an electromechanical height adjustment system for vehicles. The proposed design acts on the spring holder of the automotive suspension by means of a reversible ball-screw transmission. This actuator falls within the load-leveling category of suspensions, which becomes relevant in instances where the vehicle enters different terrains, driving conditions, or travel speeds. Furthermore, it could ease accessibility for passengers and goods. To provide reversibility, the actuation system relies on a ball-screw mechanism. It increases the overall efficiency of the actuator, which in turn, implies faster actuation times and lower power consumption. To avoid back-drivability when not needed, a highly integrated and compact locking clutch is engaged. This component provides a fail-safe feature to the suspension, even in the absence of electricity. The clutch is controlled by means of an electromagnet, which is also used to estimate its locking state by measuring the inductance of its magnetic circuit. The proposed solution is compared to a very similar irreversible configuration, where the ball-screw is replaced by a power screw with trapezoidal profile. Experiments demonstrate the benefits of the reversible ball-screw transmission in terms of actuation time (two times faster) and average efficiency (three times more efficient) when compared to the irreversible alternative. The functionality and state estimation of the locking clutch is also validated.

The problem of synthesizing a fixed-time depth observer based on monocular image feedback is considered in this study. The key challenge lies in the fact that the perspective dynamical system used to model visual motion is typically characterized as being weakly persistently exciting, which complicates observer synthesis. Furthermore, the key to achieving the task objective (safe obstacle avoidance, for instance) lies in the synthesis of a depth observer that achieves rapid convergence of the (static) obstacle depth estimate to the ground truth in a known fixed-time. To address these challenges, and in contrast with prior schemes that rely on a motion-restrictive persistency of excitation (PE) condition for ensuring exponential convergence, a novel adaptive observer framework is considered in this study that incorporates a concurrent learning (CL) term for ensuring fixed-time observer convergence. In particular, the use of concurrent learning allows for the synthesis of a relaxed finite-time excitation condition that relies on historical data recorded over a dynamic sliding window in the recent past that the proposed observer relies on to ensure fixed-time convergence. Thus, a continuous-time reduced-order observer formulation is presented that relies on camera motion data to achieve fixed-time convergence to a uniform ultimate bound for a suitably large choice of the observer gains. Experimental results are used to demonstrate the efficacy of the proposed scheme in the presence of significant measurement noise. A performance comparison study is also undertaken to demonstrate superior performance of the proposed scheme compared to leading alternative designs. Finally, the practical applicability of the proposed scheme is verified by incorporating the proposed scheme within a reactive navigation scheme to accomplish obstacle avoidance. By incorporating a suitably informative CL term within the observer framework, the proposed scheme eliminates the need to rely on a difficult-to-verify PE condition, thus rendering it more suitable for practical applications like visual target-tracking and visual servo control.

A fast, novel, guaranteed collision-free, sensor-based navigation technique designed using an integrated approach towards planning and control for 2D unicycle mobile robots is proposed in this work. Unlike existing works on navigation that investigate planning to initiate the design process for integration, the proposed approach commences by finding state-constraining properties of feedback control for the design of a safe navigation controller using the sensor measurements. The invariant set describing the state constraints renders an incremental planning algorithm. As a consequence, the explicit geometric structure of the state constraints is directly utilized for progressing towards the target goal point while ensuring that the system’s trajectories are confined within the free regions of the workspace. The simulation and experimental results show that the proposed integrated control and planning technique not only renders a faster sensor-based navigator guaranteeing to reach the reachable target in an unknown environment but also inherently drives the robot towards maximum clearance path without explicitly planning for the same.

Industrial manipulators have extensively collaborated with human operators to execute tasks, e.g., disassembly of end-of-use products, in intelligent remanufacturing. A safety task execution requires real-time path planning for the manipulator’s end-effector to autonomously avoid human operators. This is even more challenging when the end-effector needs to follow a planned path while avoiding the collision between the manipulator body and human operators, which is usually computationally expensive and limits real-time application. This paper proposes an efficient hybrid motion planning algorithm that consists of $A^{\ast }$ algorithm and an online manipulator reconfiguration mechanism (OMRM) to tackle such challenges in task and configuration spaces, respectively. $A^{\ast }$ algorithm is first leveraged to plan the shortest collision-free path of the end-effector in task space. When the manipulator body is risky to the human operator, our OMRM then selects an alternative joint configuration with minimum reconfiguration effort from a database to assist the manipulator to follow the planned path and avoid the human operator simultaneously. The database of manipulator reconfiguration establishes the relationship between the task and configuration space offline using forward kinematics, and is able to provide multiple reconfiguration candidates for a desired end-effector’s position. The proposed new hybrid algorithm plans safe manipulator motion during the whole task execution. Extensive numerical and experimental studies, as well as comparison studies between the proposed one and the state-of-the-art ones, have been conducted to validate the proposed motion planning algorithm.

The integrated navigation system combining the global navigation satellite system (GNSS) and inertial navigation system (INS) is a crucial method for pose estimation in the field of autonomous driving technologies. Nevertheless, the accuracy of pose estimation is severely compromised when GNSS signals are obstructed or disrupted. To address this issue, this study introduces a multi-mode pose estimation framework designed to ensure accurate pose estimation even under unstable GNSS conditions. By integrating vehicle kinematics model that considers steering characteristics (VKMSC) and the convolutional neural network-long short-term memory (CNN-LSTM) neural network (NN) model into various estimation modes, the framework enhances the robustness of the integrated navigation system against signal interference. The system dynamically selects the optimal estimation strategy based on the degree of GNSS signal disruption. The proposed method has been validated through real-vehicle experiments, which demonstrate its efficacy in providing precise pose estimation across a spectrum of interference scenarios. Under the multipath and non-line-of-sight (MP/NLOS) mode, compared to the integrated navigation system and the fusion of traditional vehicle kinematic models, the proposed method improved positional estimation accuracy by 61.8 % and 19.7 %, respectively. In GNSS outage mode, the proposed method increased the estimation accuracy by 36.5 % and 12.0 %, respectively, compared to the INS navigation system assisted by the VKMSC and CNN-LSTM network model. The proposed method effectively reduces pose estimation errors in the integrated navigation system during interference and suppresses data fluctuations, thereby enhancing the system's precision and robustness.

Many complex mechatronic systems consist of multiple interconnected dynamical subsystems, which are designed, developed, analyzed, and manufactured by multiple independent teams. To support such a design approach, a modular model framework is needed to reduce computational complexity and, at the same time, enable multiple teams to develop and analyze the subsystems in parallel. In such a modular framework, the subsystem models are typically interconnected by means of a static interconnection structure. However, many complex dynamical systems exhibit position-dependent behavior (e.g., induced by translating interfaces) which cannot be captured by such static interconnection models. In this paper, a modular model framework is proposed, which allows to construct an interconnected system model, which captures the position-dependent behavior of systems with translating interfaces, such as linear guide rails, through a position-dependent interconnection structure. Additionally, this framework allows to apply model reduction on subsystem level, enabling a more effective reduction approach, tailored to the specific requirements of each subsystem. Furthermore, we show the effectiveness of this framework on an industrial wire bonder. Here, we show that including a position-dependent model of the interconnection structure (1) enables to accurately model the dynamics of a system over the operating range of the system and, (2) modular model reduction methods can be used to obtain a computationally efficient interconnected system model with guaranteed accuracy specifications.

Motion planning and optimization are the key and challenging problems for redundant manipulators operating in cluttered environments. This paper proposes a motion planning framework based on the heuristic solution that explores the optimal solutions for path planning and kinematic solutions by estimating the cost of target configurations via dynamic programming methods. A heuristic function model based on artificial neural networks (ANN) is constructed in the path planning structure and rapidly trained through the RRT* algorithm, leveraging value iteration concepts to search the state space. This structure can utilize previous experience to guide future exploration behavior with significant improvements in path quality and algorithm efficiency. The kinematic solving structure is unified with path planning by building a global energy optimal heuristic function. K-means is employed to determine the initial policy, avoid ineffective searches in non-critical spaces, and introduce gradient concepts to explore the optimal policy rapidly. The proposed method can obtain better energy optimization results while ensuring solving efficiency. The optimal joint angles of the manipulator are determined through collision detection and posture adjustment methods. Finally, the performance of the proposed framework is simulated and experimentally verified.

Compliant movement and stress buffering of the torso are particularly important for state transition during high-speed locomotion in quadrupedal mammals. Currently, passive compliant control is commonly used in bionic torsos of quadruped robots, while active compliant control remains rare and immature. In previous research, we developed an active six-Degree-of-Freedom (DoF) bionic parallel torso. In this paper, we establish a muscle model that includes four biomechanical elements representing muscle characteristics (muscle force-fiber length and muscle velocity relationships) from the perspective of biology and physiology. We propose a musculoskeletal model that simulates the biological motion control system to control the compliant movement of each joint of the parallel mechanism. This model includes: 1) a neural equilibrium point controller that represents the transmission of motion commands, 2) activation dynamics that describe the activation of stimulated muscles, 3) contraction dynamics that emphasize the biomechanical characteristics of muscle tendons, 4) skeletal dynamics that describe bone movement. The effects of flexor and extensor stimulation on muscle activation, force, length, and velocity were analyzed. The results showed that both the flexor and extensor muscles will contract after corresponding stimulation. Furthermore, adjusting muscle stimulation through the musculoskeletal model can drive the parallel mechanism to reach the desired position. The musculoskeletal control method based on external force feedback can establish new torque balance in joints and drive the parallel torso to achieve compliant movements. Simulation and experiments have demonstrated the feasibility of the musculoskeletal control method. This method enhances the compliance and environmental adaptability of the parallel torso in practical applications.

This paper introduces the concept of underactuated vacuum gripper (UVG), which combines two strategies, that is, underactuation and vacuum grasping. The idea is to achieve shape adaptation while improving grip stability by augmenting an underactuated gripper with suction cups. A general theory to predict the contact forces for a UVG is developed and used for comparison reasons with a standard underactuated counterpart. Multibody simulations have been performed to verify the analytical model and quantitatively evaluate the performance of the system in terms of three metrics, namely, grasp stability, contact force distribution, and pull-out force. Finally, the experimental results obtained from a physical prototype of an underactuated linkage-driven gripper equipped with suction cups are illustrated, attesting to the feasibility and potential gain of the system.