Robot grippers have drawn a lot of attention due to their various applications in the fields of manufacturing, agriculture, etc. The shape and mass of the workable objects have been considerable issues. For effective gripping, many studies have sought to control gripping forces; however, force control often requires complex external control structures. Here, it is aimed to develop a gripper of simple structure that resists moments, grasps object edges in the absence of complete object envelopment, and does not tilt the object. Moment resistance and edge grasping are key capabilities in ensuring stable object gripping. To ensure an energy-efficient, simple structure, a mechanical trigger and a variable torque joint link the output torque to the finger actuation angle without electrical sensing. The variable torque joint creates different torques for each finger, thus ensuring the sufficient reactive moments required for stable gripping without tilting the object. To implement the output torque profile, a mechanical cam was designed and utilized in the torque joint. The developed gripper effectively resists moments and grasps object edges without the need for electrical components such as sensors, wires, or batteries. This study shows that form-sensing data can be used in various scenarios to ensure successful gripping.

In view of the post-stroke finger contracture period, the patient’s muscle weakness causes the fingers to bend and not be extended, and the fingers are in a contracture state. A new hand rehabilitation exoskeleton with a cable and leaf spring hybrid drive is designed. The high-stiffness leaf spring helps the patient complete the extension movement, and the flexion and grasping movement is completed under the action of the cable. The exoskeleton combines the cable and the leaf spring in the driving form. It is flexible and can generate enough grasping force to meet daily activities. The workspace when wearing the exoskeleton is analyzed, and the simulation verifies that the exoskeleton has a high movement space. The stiffness of the finger module is analyzed to determine the appropriate size parameters of the leaf spring. The experimental prototype is built, and the structural performance test is carried out. A prosthetic hand model is made to simulate the hand of a patient without motor ability. The position control is carried out to complete the gesture experiment and grasping experiment, which verifies that the exoskeleton can meet the rehabilitation needs and daily grasping movements. Finally, a variety of performance parameters are designed to evaluate a variety of exoskeletons. The comparison shows that the exoskeleton in this paper has significant advantages, and the area coverage rate of the performance evaluation map can reach 70.4% of the ideal exoskeleton.

Path planning, as a critical component of mobile robotic systems, significantly impacts operational efficiency and energy consumption ratios. State-of-the-art algorithms often suffer from inadequate real-time adjustment capability, insufficient dynamic environment adaptation, and suboptimal computational efficiency. To resolve these limitations, we propose a bidirectionally optimized path planning algorithm named Bidirectional Q-learning LPA* (BQ-LPA*), which incorporates three key innovations. Specifically, to enhance the global search capability of the LPA* framework, we replace fixed heuristic functions with a Q-learning-driven adaptive heuristic mechanism, which improves path quality through dynamic heuristic weighting and update strategies. Additionally, to improve the convergence rate and sample efficiency of Q-learning in complex environments, we propose integrating the LPA* framework to provide prior knowledge guidance, which can effectively minimize redundant exploration attempts by informed pathfinding initialization. Moreover, the Q-learning method inherently faces dimensionality challenges in high-dimensional continuous spaces, which manifest as action space congestion, storage bottlenecks, and computational inefficiency. To mitigate these risks, we devise an LPA*-based space discretization strategy that can reduce action space dimensionality and preserve the path feasibility. Experimental results show that, compared with mainstream path planning algorithms, BQ-LPA* achieves higher accuracy and faster convergence in mobile robot path planning.

Domains exhibit a variety of different aspects, some are order theoretical, some are topological, some belong to topological algebra. In this paper, we introduce two kinds of congruence relations on domains: I-congruence relation and II-congruence relation on domains. We obtain that there is a bijection from the set of all kernel operators of domain $P$ preserving directed sups onto the set of all I-congruence relations on $P$ which exclude $P\times P$. There is also a bijection from the set of all closure operators of domain $P$ preserving directed sups onto the set of all II-congruence relations on $P$ which exclude $P\times P$. Furthermore, between two domains, we propose a new homomorphism called I-homomorphism and II-homomorphism, respectively. We conclude that the kernels of I-homomorphisms and II-homomorphisms between domains are I-congruence relations and II-congruence relations on domains, respectively. Therefore, we obtain the I-homomorphism and I-isomorphism theorems, as well as II-homomorphism and II-isomorphism theorems for domains. Besides, we give a positive answer to an open problem on homomorphisms and quotients of continuous semilattices posed by G. Gierz, et al.

This paper presents a logic programming-based framework for policy-aware autonomous agents that can reason about potential penalties for noncompliance and act accordingly. While prior work has primarily focused on ensuring compliance, our approach considers scenarios where deviating from policies may be necessary to achieve high-stakes goals. Additionally, modeling noncompliant behavior can assist policymakers by simulating realistic human decision-making. Our framework extends Gelfond and Lobo’s Authorization and Obligation Policy Language ($\mathscr{AOPL}$) to incorporate penalties and integrates Answer Set Programming (ASP) for reasoning. Compared to previous approaches, our method ensures well-formed policies, accounts for policy priorities, and enhances explainability by explicitly identifying rule violations and their consequences. Building on the work of Harders and Inclezan, we introduce penalty-based reasoning to distinguish between noncompliant plans, prioritizing those with minimal repercussions. To support this, we develop an automated translation from the extended $\mathscr{AOPL}$ into ASP and refine ASP-based planning algorithms to account for incurred penalties. Experiments in two domains demonstrate that our framework generates higher-quality plans that avoid harmful actions while, in some cases, also improving computational efficiency. These findings underscore its potential for enhancing autonomous decision-making and informing policy refinement.

In answer set programming, two groups of rules are considered strongly equivalent if they have the same meaning in any context. Strong equivalence of two programs can be sometimes established by deriving rules of each program from rules of the other in an appropriate deductive system. This paper shows how to extend this method of proving strong equivalence to programs containing the counting aggregate.