Natural Orifice Transluminal Endoscopic Surgery (NOTES) holds great promise due to its ability to eliminate external incisions, reduce trauma, and accelerate recovery. However, the adoption of NOTES is hindered by the limited capabilities of existing instruments, particularly in achieving the required balance between compact size, dexterity, and load capacity. This paper introduces a novel robotic manipulator designed for NOTES, featuring a 5 mm diameter and 7 degrees of freedom (DoF). The manipulator incorporates an innovative 3-PRS flexible parallel mechanism combined with a continuum parallel structure, achieving enhanced dexterity and variable stiffness functionality within a miniaturized design. A kinematic and variable stiffness analysis is performed, and experimental validation demonstrates its bending performance and stiffness modulation. Additionally, the feasibility and practicality of the robotic system are confirmed through a peg-transfer experiment, proving its potential for real-world surgical applications. This research offers a viable solution for enhancing the performance of NOTES instruments.

The parallel continuum mechanism offers distinct advantages in the design of surgical manipulators, including enhanced stiffness, improved precision, and a simplified structure compared to traditional Tendon-driven systems. Conventional kinematic models based on constant-curvature assumptions are often inadequate for accurately capturing the complex bending behaviors of this mechanism. In contrast, the Cosserat rod theory provides a rigorous framework for precise kinematic modeling of flexible structures. However, its computational complexity results in slow solving speeds, particularly when dealing with spatial points that are widely separated. This paper focuses on a miniaturized parallel continuum manipulator and employs the Cosserat rod model for kinematic modeling, combined with a neural network-based inverse kinematics solver to achieve rapid real-time computation. To expedite inverse kinematics, a multilayer perceptron is trained on 5,000 samples generated from the Cosserat rod model, yielding the average absolute error of 0.046mm and the average relative error of 0.41% in predicting rod lengths. Experimental validation demonstrates that the neural network solver reduces computation time to about 0.16ms compared to 700–3100ms for conventional numerical methods, underscoring its potential for enhancing the precision and responsiveness of surgical systems in minimally invasive procedures.

One concern in Pulse-Doppler (PD) processing for airborne radar is range ambiguity resolving. This paper presents a method for collaborative range-ambiguity resolving among multi-radar using a single pulse repetition frequency (Mono-PRF). The approach eliminates the need for designing multiple PRF sets to resolve range ambiguity. Instead, it acquires the target’s ambiguous range in each radar coordinate system and extends the range based on the maximum ambiguity number. Subsequently, collaborative localization equations are applied to co-locate the same target from multi-radar, resulting in a set of range-angle curves in the reference radar’s coordinate system. The intersection of these curves provides a collection of range points, among which the true target range lies. Finally, a chi-square test based on Mahalanobis distance is used to select the true target point. Other radars utilize collaborative localization equations to resolve range ambiguity. Simulation results validate the effectiveness of this approach.

Measuring target angles is a crucial issue in airborne radar detection. Traditional dynamic programming track-before-detect (DP-TBD) often operates in low signal-to-noise ratio (SNR), posing challenges to angle measurement and localization. This letter presents an angle estimation technique for TBD processing. It interprets target angles across multiple frames using initial frame angle (IF-AG) and inter-frame angular rate of change (IF-ARC). Compensating for the latter and performing noncoherent accumulation to improve SNR before estimating the IF-AG enhance multiframe target angle estimation precision. The method initially formulates the cost function for multiframe angle measurement based on the raw data extracted from the multiframe array by DP-TBD. It subsequently estimates and compensates for IF-ARC, related to velocity, followed by IF-AG estimation, providing target angles for all frames concurrently. Simulation results validate the effectiveness of this method.

It is extremely difficult to train a superhuman Artificial Intelligence (AI) for games of similar size to StarCraft II. AlphaStar is the first AI that beat human professionals in the full game of StarCraft II, using a league training framework that is inspired by a game-theoretic approach. In this paper, we improve AlphaStar's league training in two significant aspects. We train goal-conditioned exploiters, whose abilities of spotting weaknesses in the main agent and the entire league are greatly improved compared to the unconditioned exploiters in AlphaStar. In addition, we endow the agents in the league with the new ability of opponent modeling, which makes the agent more responsive to the opponent's real-time strategy. Based on these improvements, we train a better and superhuman AI with orders of magnitude less resources than AlphaStar (see Table 1 for a full comparison). Considering the iconic role of StarCraft II in game AI research, we believe our method and results on StarCraft II provide valuable design principles on how one would utilize the general league training framework for obtaining a least-exploitable strategy in various, large-scale, real-world games.

We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state and action spaces than those of traditional 1v1 games, such as Go and Atari series, which makes it very difficult to search any policies with human-level performance. In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. Our system is of low coupling and high scalability, which enables efficient explorations at large scale. Our algorithm includes several novel strategies, including control dependency decoupling, action mask, target attention, and dual-clip PPO, with which our proposed actor-critic network can be effectively trained in our system. Tested on the MOBA game Honor of Kings, the trained AI agents can defeat top professional human players in full 1v1 games.