Haptic Feedback Remote Control System for Electric Mechanical Assembly Vehicle Developed to Avoid Obstacles

Kowol, P.; Nowak, Paweł; Banas, W; Bagier, Przemysław; Sciuto, Grazia Lo

doi:10.1007/s10846-023-01824-3

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…This study is presents a new analog joystick with stepper motors to control the driving movement of an electric vehicle equipped with Mecanum 4 omnidirectional holonomic wheels for the obstacle detection scenario proposed in [23,24]. The joystick is equipped with stepper motors, and uses a Raspberry Pi as the main controller for the electronic components and for connecting and communicating by WiFi.…”

Section: Contributionsmentioning

confidence: 99%

“…This research involves and electrical vehicle equipped by Swedish omnidirectional wheels that communicates with the joystick by WiFi and has a battery managed by a PLC controller [23,24]. The LiDAR (RPLiDAR A1M8 360 Degree Laser Scanner) uses the Raspberry Pi 4 minicomputer to identify the space of obstacles and send data to the PLC via WiFi.…”

Section: Description Of the Joystick System And Electric Vehiclementioning

confidence: 99%

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Linear Actuators in a Haptic Feedback Joystick System for Electric Vehicles

Daniel,

Kowol,

Lo Sciuto

2024

Computers

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Several strategies for navigation in unfamiliar environments have been explored, notably leveraging advanced sensors and control algorithms for obstacle recognition in autonomous vehicles. This study introduces a novel approach featuring a redesigned joystick equipped with stepper motors and linear drives, facilitating WiFi communication with a four-wheel omnidirectional electric vehicle. The system’s drive units integrated into the joystick and the encompassing control algorithms are thoroughly examined, including analysis of stick deflection measurement and inter-component communication within the joystick assembly. Unlike conventional setups in which the joystick is tilted by the operator, two independent linear drives are employed to generate ample tensile force, effectively “overpowering” the operator’s input. Running on a Raspberry Pi, the software utilizes Python programming to enable joystick tilt control and to transmit orientation and axis deflection data to an Arduino unit. A fundamental haptic effect is achieved by elevating the minimum pressure required to deflect the joystick rod. Test measurements encompass detection of obstacles along the primary directions perpendicular to the electric vehicle’s trajectory, determination of the maximum achievable speed, and evaluation of the joystick’s maximum operational range within an illuminated environment.

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Section: Contributionsmentioning

confidence: 99%

Section: Description Of the Joystick System And Electric Vehiclementioning

confidence: 99%

Linear Actuators in a Haptic Feedback Joystick System for Electric Vehicles

Daniel,

Kowol,

Lo Sciuto

2024

Computers

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“…Sensing and communication are two essential functions of AVs [1]. AVs have many sensors, such as LiDAR, which acquires information about the surroundings and avoids collisions by detecting the presence of obstacles [2,3]. In the meantime, AVs are equipped with wireless communication transceivers to achieve large-scale and high-capacity data exchange in vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Reinforcement Learning-Based Resource Allocation for Multiple Vehicles with Communication-Assisted Sensing Mechanism

Fan,

Fei,

Huang

et al. 2024

Electronics

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Autonomous vehicles (AVs) can be equipped with Integrated sensing and communications (ISAC) devices to realize sensing and communication functions simultaneously. Time-division ISAC (TD-ISAC) is advantageous due to its ease of implementation, efficient deployment and integration into any system. TD-ISAC greatly enhances spectrum efficiency and equipment utilization and reduces system energy consumption. In this paper, we propose a communication-assisted sensing mechanism based on TD-ISAC to support multi-vehicle collaborative sensing. However, there are some challenges in applying TD-ISAC to AVs. First, AVs should allocate resources for sensing and communication in a dynamically changing environment. Second, the limited spectrum resources bring the problem of mutual interference of multi-vehicle signals. To address these issues, we construct a multi-vehicle signal interference model, formulate an optimization problem based on the partially observable Markov decision process (POMDP) framework and design a decentralized dynamic allocation scheme for multi-vehicle time–frequency resources based on a deep reinforcement learning (DRL) algorithm. Simulation results show that the proposed scheme performs better in miss detection probability and average system interference power compared to the DRQN algorithm without the communication-assisted sensing mechanism and the random algorithm without reinforcement learning. We can conclude that the proposed scheme can effectively allocate the resources of the TD-ISAC system and reduce interference between multiple vehicles.

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“…Collaborative driving systems comprise coupled shared control (relevant to driver's haptic feedback) and uncoupled shared control (driver-input correction or blending) [18,19]. The former provides drivers with appropriate vibrations or force feedback through the throttle pedal or steering wheel [20,21] to guide them towards correct and rational driving behaviors. The latter involves blending the driver's inputs with the machine's inputs, overlaying or adjusting the driver's actions, and achieving a harmonious collaboration between the human and the machine [22,23].…”

Section: Introductionmentioning

confidence: 99%

Fuel-Saving-Oriented Collaborative Driving Strategy for Commercial Vehicles Based on Driving Style Recognition

Chu,

Li,

Wang

et al. 2023

Energies

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Fuel-saving-oriented collaborative driving is a highly promising yet challenging endeavor that requires satisfying the driver’s operational intentions while surpassing the driver’s fuel-saving performance. In light of this challenge, the paper introduces an innovative collaborative driving strategy tailored to the objective of fuel conservation in the context of commercial vehicles. An enhancement to this strategy involves the development of a network prediction model for vehicle speed, leveraging insights from driver style recognition. Employing the predicted speed as a reference, a model-predictive-control-based optimal controller is designed to track the reference while optimizing fuel consumption. Furthermore, a straightforward yet effective collaborative rule is proposed to ensure alignment with the driver’s intention. Subsequently, the proposed control scheme is validated through simulation and real-world driving data, revealing that the human–machine cooperative driving controller saves 4% more fuel than human drivers.

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Haptic Feedback Remote Control System for Electric Mechanical Assembly Vehicle Developed to Avoid Obstacles

Cited by 5 publications

References 38 publications

Linear Actuators in a Haptic Feedback Joystick System for Electric Vehicles

Linear Actuators in a Haptic Feedback Joystick System for Electric Vehicles

Reinforcement Learning-Based Resource Allocation for Multiple Vehicles with Communication-Assisted Sensing Mechanism

Fuel-Saving-Oriented Collaborative Driving Strategy for Commercial Vehicles Based on Driving Style Recognition

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