Object counting is the core of perception components in the industry system. Nevertheless, real-world occlusion and dense clustering pose significant challenges to fruit harvesting. Existing methods closely rely on physics-agnostic 2D clustering with rigid thresholds, neglecting critical 3D geometric cues in complex scenes. Thus, they suffer from the multi-view double-counting issue. In this paper, we propose a 3D object counting framework that integrates semantic Neural Radiance Fields (NeRF) with a physics-guided adaptive clustering algorithm. In particular, we employ a semantic NeRF to achieve implicit 3D scene reconstruction, which effectively isolates target objects from the background. Based on the semantic NeRF, we introduce a physics-guided adaptive clustering algorithm that exploits physically interpretable features, including surface normals and elevation gradients, for accurate unsupervised point cloud segmentation. Subsequently, an energy function optimization mechanism is utilized to autonomously aggregate multi-dimensional features for dynamically adjusting clustering thresholds, which enables the 3D counting to adapt to diverse fruit morphologies without manual parameter tuning. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed framework, which improves counting accuracy by an average of 9.0 percentage points (6.9 pp on synthetic, 7.2 pp on real-world, and 12.9 pp on Fuji) over the state-of-the-art 3-D baseline.

Pneumatic soft actuators are known for their versatility and reliability; however, their control presents a major challenge as systems scale beyond tens of actuators. Traditional rigid pneumatic valves add bulk, weight, and complexity, while most soft valves fail to generate programmable independent output states. We propose PneuChip, a compact pneumatic controller designed for large-scale soft actuators. The PneuChip functions as a two-dimensional array with rows and columns, controlled by m + n input signals to generate ${2^{m + n}} - {2^m} - {2^n} + 2$ distinct output states, enabling programmable control over m × n soft actuators. To validate its effectiveness, we implemented PneuChip in a muscular-skeletal robotic arm comprising 24 Miura-Ori inspired, negative pressure-actuated artificial muscles and a rigid two-link skeleton connected by a ball joint. A 4×6 PneuChip was fabricated and integrated to control the arm’s 3 degree of freedoms (DOFs) motion. Within a 120° rotation range, the robot arm achieved 946 distinct positions with smooth state transitions, paving the way for future applications in trajectory tracking and dexterous manipulation. The compact design and high controllability of PneuChip promise to notably simplify complex pneumatic systems, significantly enhancing the practicality of large-scale soft robots for various applications.

The maximum clique problem (MCP) has various applications to reveal the structure and function of graphs. Graphs are constantly updated in the real life. However, no algorithm is specifically designed for dynamic graph. AlthoughMCPin dynamic graphs can be solved by simply invoking a state-of-the-art static approach, such asPMC, when the graph is updated, such an approach of simply re-calculating from scratch is inefficient. The key issue withMCPalgorithm is to find a large clique, namely aseed, as fast as possible. Thus, search space can be pruned based on the seed. Size of the seed greedily found byPMCcannot be guaranteed, as it fluctuates considerably. Moreover, the time required to find a seed underPMCis up to$O(| E| \cdot \Delta (G))$, where$\Delta (G)$is the highest degree inG. In this article, we intend to find a sizable seed by updating the previous maximum clique with the incident vertices of the inserted/deleted edge. Size of the seed now is guaranteed to be no less than$\omega (G^{\prime})\; - \;1$, where$\omega (G^{\prime})$is the size of the maximum clique on the updated graph. Moreover, the seed can be found in a time complexity of$O(\Delta (G)^{2})$. Two other crucial issues related to theMCPin dynamic graphs are refreshing rate and refreshing overhead. After a tight upper bound is imposed on$\omega (G^{\prime})$, the necessity of refreshing is evaluated by comparing the seed with its largest challenger, then unnecessary refreshing is wiped out effectively. The size of the largest challenger is judiciously estimated using a lazy growth strategy. Subsequently, the search space in refreshing is confined on a much smaller subgraph using a local refreshing strategy. Extensive experiments indicate that the proposed approach outperforms the baseline algorithm by approximately one order of magnitude.

Maximal Clique Enumeration (MCE) is a long standing problem in database community. Though it is extensively studied, almost all researches focus on calculating maximal cliques as a one-time effort. MCE on dynamic graph has been rarely discussed so far, the only work on this topic is to maintain maximal cliques with graph evolving. The key within this problem is to find maximal cliques that contains vertices incident to the inserted edge when edge insertion happens. Up to O(W2) candidates are generated in prior method based on Cartesian product, the overall complexity is O(W2n2) where n, W represents the number of vertices and maximal cliques on the graph. Besides, maximality verification of candidate is conducted frequently by global search. Change of maximal clique induced by graph's updating presents some localities. We propose novel local construction strategy to generate candidates based on linear scan, number of candidates is reduced to O(W), the overall complexity is then reduced to O(Wn2). Furthermore, we present heuristics to reduce the cost incurred by maximality verification. Theoretical analysis and experiments on real graphs indicate that our proposals are effective and efficient.