Search Results (5)

Autonomous navigation in mobile robotics faces tremendous challenges from partial observability due to sensor degradations such as noise and flickering in laser scans. Traditional methods like Adaptive Monte Carlo Localization (AMCL) and Gmapping perform well in ideal conditions but fail under these sensor degradations. This paper develops a unified framework that integrates reinforcement learning with temporal sequence modeling, augmented by high-level semantic reasoning and parameterized quantum representations within a coherent architecture, to enable robust navigation for mobile robots. The framework models navigation as a partially observable Markov decision process (POMDP) and analyzes degraded LiDAR scans and odometry to generate velocity commands for motion planning and mapping. Experiments in a sim-to-real platform across four environments and real-world tests in indoor offices, outdoor terrains, and dynamic parking lots demonstrate substantial improvements compared to state-of-the-art methods. Success rates increase by up to 45 percentage points in dynamic scenarios, path lengths shorten by 20–25%, and map accuracies improve by 40% compared to baselines. The proposed approach achieves these gains through quantum-enhanced feature extraction for exploration, temporal modeling for state correction, and semantic reasoning for obstacle interpretation. This work advances reliable robot autonomy in uncertain environments. Full article

Autonomous shuttles have been used as end-mile solutions for smart mobility in smart cities. The urban driving conditions of smart cities with many other actors sharing the road and the presence of intersections have posed challenges to the use of autonomous shuttles. Round intersections are more challenging because it is more difficult to perceive the other vehicles in and near the intersection. Thus, this paper focuses on the decision-making of autonomous vehicles for handling round intersections. The round intersection is introduced first, followed by introductions of the Markov Decision Process (MDP), the Partially Observable Markov Decision Process (POMDP) and the Object-Oriented Partially Observable Markov Decision Process (OOPOMDP), which are used for decision-making with uncertain knowledge of the motion of the other vehicles. The Partially Observable Monte-Carlo Planning (POMCP) algorithm is used as the solution method and OOPOMDP is applied to the decision-making of autonomous vehicles in round intersections. Decision-making is formulated first as a POMDP problem, and the penalty function is formulated and set accordingly. This is followed by an improvement in decision-making with policy prediction. Augmented objective state and policy-based state transition are introduced, and simulations are used to demonstrate the effectiveness of the proposed method for collision-free handling of round intersections by the ego vehicle. Full article

Condition-based maintenance (CBM) scheduling of an aircraft fleet in a disruptive environment while considering health prognostics for a set of systems is a very complex combinatorial problem, which is becoming more challenging in light of the uncertainty included in health prognostics. This type of problem falls under the broad category of resource-constrained scheduling problems under uncertainty and is often solved using a mixed integer linear programming (MILP) formulation. While a MILP framework is very promising, the problem size can scale exponentially with the number of considered aircraft and considered tasks, leading to significantly high computational costs. The most recent advances in artificial intelligence have demonstrated the capability of deep reinforcement learning (DRL) algorithms to alleviate this curse of dimensionality, as once the DRL agent is trained, it can achieve real-time optimization of the maintenance schedule. However, there is no guarantee of optimality. These comparative merits of a MILP and a DRL formulation for the aircraft fleet maintenance scheduling problem have not been discussed in the literature. This study is a response to this research gap. We conduct a comparison of a MILP and a DRL scheduling model, which are used to derive the optimal maintenance schedule for various maintenance scenarios for aircraft fleets of different sizes in a disruptive environment, while considering health prognostics and the available resources for the execution of each task. The quality of solutions is evaluated on the basis of four planning objectives, defined according to real airline practice. The results show that the DRL approach achieves better results with respect to scheduling of prognostics-driven tasks and requires less computational time, whereas the MILP model produces more stable maintenance schedules and induces less maintenance ground time. Overall, the comparison provides valuable insights for the integration of health prognostics in airline maintenance practice. Full article

Pancreatic cancer is a leading cause of cancer death, and boron neutron capture therapy (BNCT) is one of the promising radiotherapy techniques for patients with pancreatic cancer. In this study, we evaluated the biological effectiveness of BNCT at multicellular levels using in vitro and in silico models. To recapture the phenotypic characteristic of pancreatic tumors, we developed a cell self-assembly approach with human pancreatic cancer cells Panc-1 and BxPC-3 cocultured with MRC-5 fibroblasts. On substrate with physiological stiffness, tumor cells self-assembled into 3D spheroids, and the cocultured fibroblasts further facilitated the assembly process, which recapture the influence of tumor stroma. Interestingly, after 1.2 MW neutron irradiation, lower survival rates and higher apoptosis (increasing by 4-fold for Panc-1 and 1.5-fold for BxPC-3) were observed in 3D spheroids, instead of in 2D monolayers. The unexpected low tolerance of 3D spheroids to BNCT highlights the unique characteristics of BNCT over conventional radiotherapy. The uptake of boron-containing compound boronophenylalanine (BPA) and the alteration of E-cadherin can partially contribute to the observed susceptibility. In addition to biological effects, the probability of induced α-particle exposure correlated to the multicellular organization was speculated to affect the cellular responses to BNCT. A Monte Carlo (MC) simulation was also established to further interpret the observed survival. Intracellular boron distribution in the multicellular structure and related treatment resistance were reconstructed in silico. Simulation results demonstrated that the physical architecture is one of the essential factors for biological effectiveness in BNCT, which supports our in vitro findings. In summary, we developed in vitro and in silico self-assembly 3D models to evaluate the effectiveness of BNCT on pancreatic tumors. Considering the easy-access of this 3D cell-assembly platform, this study may not only contribute to the current understanding of BNCT but is also expected to be applied to evaluate the BNCT efficacy for individualized treatment plans in the future. Full article

Decentralized partially observable Markov decision processes (Dec-POMDPs) are general multi-agent models for planning under uncertainty, but are intractable to solve. Doubly exponential growth of the search space as the horizon increases makes a brute-force search impossible. Heuristic methods can guide the search towards the right direction quickly and have been successful in different domains. In this paper, we propose a new Q-value function representation—Monte Carlo Q-value function ${\mathrm{Q}}_{\mathrm{MC}}$ , which is proved to be an upper bound of the optimal Q-value function ${\mathrm{Q}}^{*}$ . We introduce two Monte Carlo tree search enhancements—heavy playout for a simulation policy and adaptive samples—to speed up computation of ${\mathrm{Q}}_{\mathrm{MC}}$ . Then, we present a clustering and expansion with Monte-Carlo algorithm (CEMC)—an offline planning algorithm using ${\mathrm{Q}}_{\mathrm{MC}}$ as Q-value function, which is based on the generalized multi-agent A* with incremental clustering and expansion (GMAA*-ICE or ICE). CEMC calculates Q-value functions as required, without computing and storing all Q-value functions. An extended policy pruning strategy is used in CEMC. Finally, we present empirical results demonstrating that CEMC outperforms the best heuristic algorithm with a compact Q-value presentation in term of runtime for the same horizon, and has less memory usage for larger problems. Full article