Agricultural multi-robot systems in narrow and dynamic environments require global coordination, semantic event interpretation, and responsive trajectory execution. This study presents an end–edge–cloud fast–slow semantic planning framework. The cloud maintains a farm topology and generates fleet-level dispatch policies; the edge hosts an asynchronous agentic vision–language planner and a fast trajectory planner; and the robot performs sensing, LiDAR odometry, low-level control, and execution. The fast planner reuses the latest valid semantic condition until an event-triggered update becomes available. The fast branch is pretrained on nuScenes and adapted using the training and validation subsets of a 3780-sample agricultural dataset comprising synchronized front- and rear-view images, robot states, motion histories, and future trajectories, with an independent 630-sample test set reserved for final evaluation. On an edge-side RTX 4080 SUPER, the complete planner achieves an average L₂ error of 0.67 m, a fast-step latency of 96.3 ms, and a throughput of 10.4 Hz. In the four-robot topology experiment, the framework achieves a 100.0% success rate under the representative single-blockage condition and maintains an 86.7% success rate under the dual-blockage condition. During an approximately 30 min operation at a nominal semantic update rate of 2 Hz, the cloud and robot communication round-trip times average 24.43 and 3.85 ms, respectively, with no robot deadline misses, while the mean trigger-to-updated-trajectory latency of the full event-driven pipeline is 2357.37 ms. These results demonstrate the feasibility of assigning global coordination to the cloud, semantic reasoning and trajectory inference to the edge, and sensing and execution to the robot.

Starting from the programmable motion performance of modern actuation technology, this study elucidates that the degree of quick-return in a mechanical system is no longer entirely dependent on mechanism design. A theoretical framework is proposed for analyzing and synthesizing the degree of quick-return in contemporary mechanical systems based on time efficiency. This study adopts a functional perspective, first extending the traditional definition describing the degree of quick-return mechanism to encompass broader mechanical systems. Subsequently, we present a general formula for calculating the time-ratio and travel velocity-ratio coefficients under contemporary technological conditions, supported by a detailed mathematical derivation. Finally, a corresponding procedure is established for analyzing and synthesizing the degree of quick-return in a mechanical system, fostering improved collaboration between managers and designers. Case studies demonstrate that designers can achieve the desired degree of quick-return in mechanical systems and robots by leveraging time efficiency. The theoretical framework introduced in this paper is versatile and can benefit various industries, notably the field of robotics. This broadens the applicability of the degree of quick-return concept, marking a significant advancement in enhancing the time efficiency of mechanical systems.

In response to the demand for high-precision positioning within confined or indoor environments, the application of acoustic ranging methods has been widely adopted by numerous engineers. Currently, time-of-flight (TOF)-based acoustic ranging positioning systems face challenges such as the susceptibility of sound velocity to environmental factors and the loss of acoustic signals at both short and long distances, which leads to a reduction in positioning accuracy. This paper addresses these issues by proposing a high-precision confidence interval weighting method for acoustic ranging and further introduces a method for base station deployment and self-optimization positioning within fixed indoor base station scenarios. The method is based on trilateration positioning, establishing criteria for the division of central and boundary areas. It categorizes mobile terminal nodes based on their coordinates from the previous moment, selects distance information from nearby base stations in different modes, and employs weights for decision-making and computation, ultimately yielding two-dimensional positioning coordinates. Experiments demonstrate that the proposed method can effectively enhance the positioning accuracy of acoustic positioning systems compared to traditional four-base station weighted average positioning algorithms.