In the current era of rapid-changing market and supply chain fluctuations, manufacturing industries face significant challenges in maintaining agility and resilience. Machinability evaluation is one of the critical steps in manufacturing planning to ensure the industry can respond to rapid market changes or customization demands. However, this process is currently conducted manually and highly relies on engineers’ knowledge and experience. This paper presents a systematic approach that employs fuzzy logic, which can be automated by a software program, for evaluating the machinability of product features and recommending reconfiguration options for computer numerical control (CNC) machines. This approach assesses each product feature against the current machine kinematics. The validated results demonstrate that the proposed fuzzy logic can generate comprehensive results that reflect varying degrees of machinability. For product features that cannot be machined with the existing machine configurations, this approach identifies specific limitations and provides data-driven recommendations for reconfiguration. This will assist machine operators in making informed decisions, thereby reducing reliance on manual evaluation and planning. Furthermore, this automated evaluation process will enable a shorter turnaround time for new production line setups and enhance the overall operational efficiency, especially in high-variety, low-volume manufacturing environments.

A virtual flexible curved bar coupled with force delay algorithm is proposed for automatic vehicle following, aiming at improving the accuracy of vehicle trajectory tracking, especially when leader vehicle is accelerating or making turns. A virtual flexible curved bar with force delay has been modeled that connects the leader to the follower. The length of this virtual bar is a function of the turning radiuses of the leader vehicle. Through this model, the follower vehicle is in effect being virtually dragged by the leader through the virtual flexible curved bar. It is the virtual dragging force that makes the follower accelerate/decelerate so as to adjust its speed to match that of the leader. Finally, simulations were carried out in MATLAB. The results showed that the proposed algorithm has improved the trajectory tracking error greatly, compared to the virtual rigid straight link approach.

This paper presents a new intuitive method for robot tool path teaching in Augmented Reality (AR) environment. Conventional industrial robot teaching method is long known to be either tedious or require a highly accurate virtual representation of robot work cell. Our method targets to provide the user with a fast and easy way of programming an industrial robot for useful tasks in a safe environment. In our system, a human robot interaction (HRI) system has been designed by fusing information from a camera and a laser ranger finder. The video images provide visual information to the user to operate the system, whereas the laser range finder captures the Cartesian information of the user intended robot working paths and trajectories. Furthermore, an AR environment has been designed where the virtual tool is superimposed onto the live video. The user simply needs to point and click on the image of the workpiece to generate the tool path. User can also adjust virtual tool orientation and simulate the tool trajectory in the AR environment, thus simplifying the robot teaching task. The proposed system has been tested for robot laser welding application. It is intuitive as no prior knowledge of robotic control is required in order to use our system. Most importantly, the system is safe and the user does not need to be physically close to the robot during path teaching.

Vehicle following can be achieved by minimizing the relative information (Kullback-Leibler or K-L distance), between the estimated poses of leader and follower vehicles by formulating the vehicle following system as an optimization problem. The aim is to search for an optimal control action for the follower vehicle in the admissible control command space. Relative information is used as a metric in the search space and for evaluating the expected performance of vehicle following. With this metric, and based on the assumption that both vehicle pose (position and orientation) distributions are Gaussian functions, the K-L distance of the vehicle following system can be computed. With a series of admissible actions, such as steering and velocity commands, for the follower vehicle at each pose prediction step, and by minimizing the K-L distance, an optimized action for the follower vehicle can be obtained. The proposed vehicle following algorithm has been tested and the performance of the follower vehicle when the leader undergoes various kinds of maneuvers has been analyzed. Results using this new method, as compared to classical methods, have shown the advantages of this method.

Through the use of a virtual trailer link model, autonomous vehicle following capabilities have been demonstrated. The principle is based on the modelling of the trailer link, used in the off-hooked trailer system, as a virtual link between the leading and led vehicles. The leader vehicle is modelled as the tractor (towing vehicle) and the follower vehicle as the trailer (towed vehicle). This method enables our autonomous vehicle to follows the estimated trajectory of the virtual trailer, which is predicted from observations of the maneuvers of the lead vehicle. Unlike conventional methods, in our system, neither communication links between two vehicles nor the installation of special road infrastructures are needed for the implementation. Communication system may failed and the installation of road infrastructures are costly. We show that system response of the virtual trailer link model is much smoother. A series of simulations and experimental results have indicated the validity of the proposed method.

In this paper, we proposed a robot vehicle following algorithm which can navigate a 10 ton armored personnel carrier to follow a leading vehicle (MPV) in the jungle. This algorithm comprises two components, the first one is a target tracking module which can detect and track the lead vehicle from the measurements of a SICK laser scanner; the second one is a obstacle avoidance module which takes the tracking results and the local environment description as input, it then generates the set-points for the vehicle to follow. Plenty of trials have been carried out in the jungle of Singapore, our technique's validity and robust is demonstrated and tested, the results will be showed in this paper.

A robust vehicle following system with obstacle avoidance capabilities for operation in natural environments is described in this paper. By combining a novel vehicle-tracking and detection algorithm with our path-planner for autonomous navigation, it was possible for a tracked logistics armoured ambulance carrier to follow a multi purpose vehicle in an equatorial jungle where few non-paved roads and markers exist. With this new approach, vehicle following performance is enhanced and vehicle safety ensured. Field trials performed in tropical jungle conditions have demonstrated the validity of the approach; results from the field works are included and discussed in this paper.