This paper addresses the crossing order problem for connected and automated vehicles at an intersection where the problem can lead to higher traffic congestion, especially in high traffic density. First, we formulated the problem with mixed integer linear programming where the solution yields optimal crossing time for each vehicle to cross the intersection. Then, we present an optimization framework to drive each vehicle towards the assigned crossing time while conserving the fuel consumption. Finally, we simulate the intersection scenario with the proposed solution, where it is shown that both congestion and fuel consumption can be reduced significantly.
We consider solving a large-scale Lyapunov equation for a multi-agent system. As is well known, the Lyapunov equation can be solved by equivalently rewriting it as a system of linear equations. The difficulties in solving this system are memory requirement and computational complexity due to the large-scale coefficient matrix involving a number of Kronecker products. This paper presents a modified GMRES method for solving the aforementioned system of linear equations taking account of its tensor structure and the symmetry of the unknown matrix in the Lyapunov equation. Through numerical experiments, the improvement in memory requirement and computational time by the present algorithms is verified in comparison with the previous GMRES-based methods.
A probabilistic method for constructing an empirical discrimination model to inspect defective cast-iron parts (such as graphite-spheroidized defective parts) by the hammering test is proposed. The hammering-sound frequency spectrum includes multiple resonance lines whose frequencies vary according to the degree of defect. To construct the model, only non-defective hammering-sound data that can be collected from the production line are input, and a distribution function that fits the frequency distribution of each resonance line is estimated. Since the frequency distribution shows multimodality and asymmetry, the function is estimated by using automatic differentiation variational inference with a mixed-normal distribution function. The confidence interval of the obtained distribution function is then regarded as a section with no defective parts, and the discrimination model is automatically constructed by connecting the sections of all resonance lines in the audible range. Then, parts outside the sections are discriminated as defective. Experimentally determined accuracy confirmed that it is possible to achieve hammer-test inspection with the detection rate of 100% and prevent overlooking of defective parts.
This paper investigates constrained semi-autonomous robotic swarms in the presence of inter-robot communication delays. The objective is that all robots converge to the optimal point related to the reference given by a human operator without violating the constraints. In the presence of constraints, directly applying the human reference may make the system violate constraints. We thus apply a reference governor which suitably modifies the reference to avoid constraint violations. Due to the limited access of global information, we present a passivity-based distributed reference governor scheme. However, the presence of the communication delays could cause instability of the system. To integrate communication delays into the passivity paradigm, a passivation technique using the scattering transformation is implemented in the communication path. Based on passivity theory, we prove the convergence of the estimated states of the reference governor to the optimal solution. The simulations and experiments demonstrate the effectiveness of the proposed solution.