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Buses and heavy vehicles have more blind spots compared to cars and other road vehicles due to their large sizes. Therefore, accidents caused by these heavy vehicles are more fatal and result in severe injuries to other road users. These possible blind-spot collisions can be identified early using vision-based object detection approaches. Yet, the existing state-of-the-art vision-based object detection models rely heavily on a single feature descriptor for making decisions. In this research, the design of two convolutional neural networks (CNNs) based on high-level feature descriptors and their integration with faster R-CNN is proposed to detect blind-spot collisions for heavy vehicles. Moreover, a fusion approach is proposed to integrate two pre-trained networks (i.e., Resnet 50 and Resnet 101) for extracting high level features for blind-spot vehicle detection. The fusion of features significantly improves the performance of faster R-CNN and outperformed the existing state-of-the-art methods. Both approaches are validated on a self-recorded blind-spot vehicle detection dataset for buses and an online LISA dataset for vehicle detection. For both proposed approaches, a false detection rate (FDR) of 3.05% and 3.49% are obtained for the self recorded dataset, making these approaches suitable for real time applications.

Robot hands grasp parallel groove clamps in assembly operations. The locations of parts during assembly are very important in grasping planning. Directly measuring the locations of all parts in the assembly process is tedious. A motion simulation is proposed to calculate the locations of parts in the assembly process. The boundary representation method describes the geometric information of the part. Assembly sequences simplify the movements of parts. Concave-arc surface collision detection is proposed to calculate the locations of irregular concave parts. The proposed method is found to be computationally less expensive than the collision detection method based on meshing. This research provides a foundation for further studies of robot hand manipulation in irregular assemblies.

To solve the problems of poor real-time collision accuracy and low efficiency of modeling in the virtual reality environment, we propounded a depth image-based 3D modeling system and a hybrid intelligent collision detection algorithm. With the development of the 3D animation interactive system as an example, this paper uses 3D modeling technology based on depth images to build single role models, then reorganizes and merges the models to form 3D scenes. The hybrid intelligent collision detection algorithm, which combines the quantum behavior particle swarm optimization algorithm and the differential algorithm, improves the collision detection efficiency and accuracy and realizes behavior control of the characters in the interactive system. The experimental result shows that the 3D modeling technology based on depth images has greatly improved the accuracy and quantity of model texture and motion rate. By comparing the hybrid intelligent collision detection algorithm, the QPSO algorithm, and the FDH bounding box for collision detection, we conclude that the algorithm used in this paper has a shorter average collision time, more stable role behavior control, and better robustness.

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.

Perception capability assumes significant importance for human–robot interaction. The forthcoming industrial environments will require a high level of automation to be flexible and adaptive enough to comply with the increasingly faster and low-cost market demands. Autonomous and collaborative robots able to adapt to varying and dynamic conditions of the environment, including the presence of human beings, will have an ever-greater role in this context. However, if the robot is not aware of the human position and intention, a shared workspace between robots and humans may decrease productivity and lead to human safety issues. This paper presents a survey on sensory equipment useful for human detection and action recognition in industrial environments. An overview of different sensors and perception techniques is presented. Various types of robotic systems commonly used in industry, such as fixed-base manipulators, collaborative robots, mobile robots and mobile manipulators, are considered, analyzing the most useful sensors and methods to perceive and react to the presence of human operators in industrial cooperative and collaborative applications. The paper also introduces two proofs of concept, developed by the authors for future collaborative robotic applications that benefit from enhanced capabilities of human perception and interaction. The first one concerns fixed-base collaborative robots, and proposes a solution for human safety in tasks requiring human collision avoidance or moving obstacles detection. The second one proposes a collaborative behavior implementable upon autonomous mobile robots, pursuing assigned tasks within an industrial space shared with human operators.

Path planning is a very important step for mobile smart vehicles in complex environments. Sampling based planners such as the Probabilistic Roadmap Method (PRM) have been widely used for smart vehicle applications. However, there exist some shortcomings, such as low efficiency, low reuse rate of the roadmap, and a lack of guidance in the selection of sampling points. To solve the above problems, we designed a pseudo-random sampling strategy with the main spatial axis as the reference axis. We optimized the generation of sampling points, removed redundant sampling points, set the distance threshold between road points, adopted a two-way incremental method for collision detections, and optimized the number of collision detection calls to improve the construction efficiency of the roadmap. The key road points of the planned path were extracted as discrete control points of the Bessel curve, and the paths were smoothed to make the generated paths more consistent with the driving conditions of vehicles. The correctness of the modified PRM was verified and analyzed using MATLAB and ROS to build a test platform. Compared with the basic PRM algorithm, the modified PRM algorithm has advantages related to speed in constructing the roadmap, path planning, and path length.

Previous collision detection methods for virtual disassembly mainly detect collisions at discrete time intervals and use oriented bounding boxes to speed up the process. However, these discrete methods cannot guarantee no penetration occurs when the components move. Meanwhile, because some of the components are embedded into each other, these components cannot be separated in the subsequent process. To solve these problems, we propose an approach for real-time collision handling by utilizing the computational power of modern GPUs. First we present a novel GPU-based collision handling framework for virtual disassembly. Second we use a collision-streams based continuous collision detection to guarantee no collision missed. Finally we introduce a triangle intersection detection algorithm to solve the problem that collision cannot be detected when the components are embedded into each other at the initial configuration. The experimental results show that our method can improve the overall performance of collision detection and achieve real-time simulation.

This paper proposes an interference detection method for multiuser-multiple input multiple output (MU-MIMO) transmission, which utilizes periodical preamble signals in the frequency domain and the concept of full-duplex transmission when assuming idle antennas at the access point (AP) in MU-MIMO. In the propose method, collision detection (CD) of MU-MIMO is achieved by utilizing asynchronous MU-MIMO called random access MU-MIMO. In random access MU-MIMO, several antennas that are not used for the transmission exist, due to asynchronous MU-MIMO. Hence, idle antennas at the AP can receive preamble signals while the transmit antennas at the AP transmit the preamble signals: this procedure is regarded as full-duplex transmission, which cancels the self-interference between AP antennas. The interference can be detected by subtracting the short preamble signal, which is multiplied by the estimated channel response using the received signal after the FFT processing. Moreover, we utilize dual polarization to reduce the mutual coupling between transmit and receive antennas at the AP. Through a computer simulation, it is shown that the proposed method can successfully detect collision from other user terminals (UTs) with OFDM signals when the interfering power from the interfering user terminal (IT) is greater than the noise power. In addition, the interfering power from IT at the AP and the desired user terminal (DT) is measured in an actual indoor environment, and the possibility of using the proposed method at the AP is discussed by using the measurement results.

Human–robot collaboration (HRC) plays a pivotal role in today’s industry by supporting increasingly customised product development. Via HRC, the strengths of humans and robots can be combined to facilitate collaborative jobs within common workplaces to achieve specific industrial goals. Given the significance of safety assurance in HRC, in this survey paper, an update on standards and implementation approaches presented in the latest literature is given to reflect the state-of-the-art of this prominent research topic. First, an overview of safety standards for industrial robots, collaborative robots, and HRC is provided. Then, a survey of various approaches to HRC safety is conducted from two main perspectives, i.e., pre-collision and post-collision, which are further detailed in the aspects of sensing, prediction, learning, planning/replanning, and compliance control. Major characteristics, pros, cons, and applicability of the approaches are analysed. Finally, challenging issues and prospects for the future development of HRC safety are highlighted to provide recommendations for relevant stakeholders to consider when designing HRC-enabled industrial systems.

This paper introduces a Hexa parallel robot and obstacle collision detection method based on dynamic modeling and a computer vision system. The processes to deal with the collision issues refer to collision detection, collision isolation, and collision identification applied to the Hexa robot, respectively, in this paper. Initially, the configuration, kinematic and dynamic characteristics during movement trajectories of the Hexa parallel robot are analyzed to perform the knowledge extraction for the method. Next, a virtual force sensor is presented to estimate the collision detection signal created as a combination of the solution to the inverse dynamics and a low-pass filter. Then, a vision system consisting of dual-depth cameras is designed for obstacle isolation and determining the contact point location at the end-effector, an arm, and a rod of the Hexa robot. Finally, a recursive Newton-Euler algorithm is applied to compute contact forces caused by collision cases with the real-Hexa robot. Based on the experimental results, the force identification is compared to sensor forces for the performance evaluation of the proposed collision detection method.