Collision response and obstacle avoidance of the tethered-space net robot system with non-target objects

Purpose This paper aims to study the encounter issues of the Tethered-Space Net Robot System (TSNRS) with non-target objects on orbit during the maneuver, including the collision issues with small space debris and the obstacle avoidance from large obstacles. Design/methodology/approach For the collision of TSNRS with small debris, the available collision model of the tethered net and its limitation is discussed, and the collision detection method is improved. Then the dynamic response of TSNRS is studied and a closed-loop controller is designed. For the obstacle avoidance, the variable enveloping circle of the TSNRS has coupled with the artificial potential field (APF) method. In addition, the APF is improved with a local trajectory correction method to avoid the overbending segment of the trajectory. Findings The collision model coupled with the improved collision detection method solves the detection failure and speeds up calculation efficiency by 12 times. Collisions of TSNRS with small debris make the local thread stretch and deforms finally making the net a mess. The boundary of the disturbance is obtained by a series of collision tests, and the designed controller not only achieved the tracking control of the TSNRS but also suppressed the disturbance of the net. Practical implications This paper fills the gap in the research on the collision of the tethered net with small debris and makes the collision model more general and efficient by improving the collision detection method. And the coupled obstacle avoidance method makes the process of obstacle avoidance safer and smoother. Originality/value The work in this paper provides a reference for the on-orbit application of TSNRS in the active space debris removal mission.

CycleGuard

Every year 41,000 cyclists die in road traffic-related incidents worldwide [47]. One of the most startling and infuriating conflicts that cyclists experience is the so-called "right hook". It refers to a vehicle striking a cyclist heading in the same direction by turning right into the cyclist. To prevent such a crash, this work presents CycleGuard, an acoustic-based collision detection system using smartphones. It is composed of a cheap commercial off-the-shelf (COTS) portable speaker that emits imperceptible high-frequency acoustic signals and a smartphone for reflected signal reception and analysis. Since received acoustic signals bear rich information of their reflecting objects, CycleGuard applies advanced acoustic ranging techniques to extract those information for traffic analysis. Cyclists are alerted if any pending right hook crashes are detected. Real-time alerts ensure that cyclists have sufficient time to react, apply brakes, and eventually avoid the hazard. To validate the efficacy of CycleGuard, we implement a proof-of-concept prototype and carry out extensive in-field experiments under a broad spectrum of settings. Results show that CycleGuard achieves up to 95% accuracy in preventing right hook crashes and is robust to various scenarios. It is also energy-friendly to run on battery-powered smartphones.

Reduced Simulation: Real-to-Sim Approach toward Collision Detection in Narrowly Confined Environments

Recently, several deep-learning based navigation methods have been achieved because of a high quality dataset collected from high-quality simulated environments. However, the cost of creating high-quality simulated environments is high. In this paper, we present a concept of the reduced simulation, which can serve as a simplified version of a simulated environment yet be efficient enough for training deep-learning based UAV collision avoidance approaches. Our approach deals with the reality gap between a reduced simulation dataset and real world dataset and can provide a clear guideline for reduced simulation design. Our experimental result confirmed that the reduction in visual features provided by textures and lighting does not affect operating performance with the user study. Moreover, by conducting collision detection experiments, we verified that our reduced simulation outperforms the conventional cost-effective simulations in adaptation capability with respect to realistic simulation and real-world scenario.

Quantum anonymous collision detection for quantum networks

Quantum mechanics offers new opportunities for diverse information processing tasks in communication and computational networks. In the last two decades, the notion of quantum anonymity has been introduced in several networking tasks that provide an unconditional secrecy of identity for the communicating parties. In this article, we propose a quantum anonymous collision detection (QACD) protocol which detects not only the collision but also guarantees the anonymity in the case of multiple senders. We show that the QACD protocol serves as an important primitive for a quantum anonymous network that features tracelessness and resource efficiency. Furthermore, the security analysis shows that this protocol is robust against the adversary and malicious participants.

Innovative Research of Trajectory Prediction Algorithm Based on Deep Learning in Car Network Collision Detection and Early Warning System

Predicting the trajectories of neighboring vehicles is essential to evade or mitigate collision with traffic participants. However, due to inadequate previous information and the uncertainty in future driving maneuvers, trajectory prediction is a difficult task. Recently, trajectory prediction models using deep learning have been addressed to solve this problem. In this study, a method of early warning is presented using fuzzy comprehensive evaluation technique, which evaluates the danger degree of the target by comprehensively analyzing the target’s position, horizontal and vertical distance, speed of the vehicle, and the time of the collision. Because of the high false alarm rate in the early warning systems, an early warning activation area is established in the system, and the target state judgment module is triggered only when the target enters the activation area. This strategy improves the accuracy of early warning, reduces the false alarm rate, and also speeds up the operation of the early warning system. The proposed system can issue early warning prompt information to the driver in time and avoid collision accidents with accuracy up to 96%. The experimental results show that the proposed trajectory prediction method can significantly improve the vehicle network collision detection and early warning system.

Optimizing Kinematic Modeling and Self-Collision Detection of a Mobile Manipulator Robot by Considering the Actual Physical Structure

In this paper, an optimized kinematic modeling method to accurately describe the actual structure of a mobile manipulator robot with a manipulator similar to the universal robot (UR5) is developed, and an improved self-collision detection technology realized for improving the description accuracy of each component and reducing the time required for approximating the whole robot is introduced. As the primary foundation for trajectory tracking and automatic navigation, the kinematic modeling technology of the mobile manipulator has been the subject of much interest and research for many years. However, the kinematic model established by various methods is different from the actual physical model due to the fact that researchers have mainly focused on the relationship between driving joints and the end positions while ignoring the physical structure. To improve the accuracy of the kinematic model, we present a kinematic modeling method with the addition of key points and coordinate systems to some components that failed to model the physical structure based on the classical method. Moreover, self-collision detection is also a primary problem for successfully completing the specified task of the mobile manipulator. In traditional self-collision detection technology, the description of each approximation is determined by the spatial transformation of each corresponding component in the mobile manipulator robot. Unlike the traditional technology, each approximation in the paper is directly established by the physical structure used in the kinematic modeling method, which significantly reduces the complicated analysis and shortens the required time. The numerical simulations prove that the kinematic model with the addition of key point technology is similar to the actual structure of mobile manipulator robots, and the self-collision detection technology proposed in the article effectively improves the performance of self-collision detection. Additionally, the experimental results prove that the kinematic modeling method and self-collision detection technology outlined in this paper can optimize the inverse kinematics solution.

Voice Controlled Guided System for Wheelchair with Collision Detection and Avoidance

Many people are suffering from temporary or permanent disabilities due to accidents or any pre-illness that may introduce a wheelchair for the person as essential. The use of a wheelchair independently can be derived from the severity of the disability. But in the case of severe situations when a person relies on somebody else to handle the movement of a wheelchair. This paper aims to provide a solution to this problem by using voice command to guide the wheelchair eliminating the reliability of another person.