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The rapid acceleration of urbanization and the surge in car ownership necessitate efficient automatic parking solutions in constricted spaces to address the escalating urban parking issue. To optimize space utilization, enhance traffic efficiency, and mitigate accident risks, a method is proposed for smooth, comfortable, and adaptable automatic parking trajectory planning. This study initially employs a hybrid A* algorithm to generate a preliminary path, then fits the velocity and acceleration based on a cubic polynomial. The kinematic constraints of the vehicle and obstacle avoidance constraints are then meticulously defined, and a coupled nonlinear model predictive control (NMPC) method is employed to optimize the trajectory. Compared to the hybrid A* algorithm, the optimized trajectory demonstrates superior space utilization and improved smoothness. The experimental results indicate that the proposed method performs effectively in automated parking tasks in confined spaces, suggesting promising applications and broad prospects for future.

Robots with parallelogram mechanisms are widely employed in industrial applications due to their mechanical rigidity and precise motion control. However, the analytical definition of feasible workspace regions free from self-collisions remains an open challenge, especially considering the nonlinear and composite nature of such regions. This work introduces a mathematical model grounded in a collision theorem that formalizes boundary functions based on joint variables and geometric constraints. These functions explicitly define the envelope of safe configurations by evaluating relative positions between critical structural components. Using the MinervaBotV3 as a case study, the symbolic joint-space boundaries and their corresponding geometric regions in both 2D and 3D are computed and visualized. The feasible region is refined through centroid-based scaling to introduce safety margins and avoid singularities. The results show that this framework enables analytically continuous workspace representations, improving trajectory planning and reliability in constrained environments. Future work will extend this method to spatial mechanisms and real-time implementations in hybrid robotic systems.

This paper considers the problem of servicing a set of locations by a fleet of robots so as to minimize overall makespan. Although motivated by a specific real-world, multi-robot drilling and fastening application, the problem also arises in a range of other multi-robot domains where service start times are subject to precedence constraints and robots must be routed in space and time to avoid collisions. We formalize this general problem and analyze its complexity. We develop a heuristic local search procedure for solving it and analyze its performance on a set of synthetically generated problem instances, some of which capture the specific structure of the motivating drilling and fastening application, and others that generalize to other application settings. We provide a differential analysis of our local search procedure and a comparison to other approaches to demonstrate the efficacy of the proposed heuristic.

This work presents an alternative method for defining feasible joint-space boundaries and their corresponding geometric workspace in a planar robotic system. Instead of relying on traditional numerical approaches that require extensive sampling and collision detection, the proposed method constructs a continuous boundary by identifying the key intersection points of boundary functions. The feasibility region is further refined through centroid-based scaling, addressing singularity issues and ensuring a well-defined trajectory. Comparative analyses demonstrate that the final robot pose and reachability depend on the selected traversal path, highlighting the nonlinear nature of the workspace. Additionally, an evaluation of traditional numerical methods reveals their limitations in generating continuous boundary trajectories. The proposed approach provides a structured method for defining feasible workspaces, improving trajectory planning in robotic systems.

In this paper, a six-degree-of-freedom (6-DOF) control scheme for asteroid landing subject to collision avoidance and line-of-sight (LOS) constraints is developed within the newly established twistor framework. The controller guarantees a spacecraft touches down at a specified landing site in a desired attitude, meanwhile satisfies the constraints with the coupling effect between the translational and rotational motions involved. First, 6-DOF dynamics of a spacecraft relative to an asteroid is described by the twistor representation to model the translational and rotational motions in a unified way. Then, the collision avoidance and LOS constraints are formulated and expressed as functions of the twistor. Following that, the constraints are encoded into an artificial potential function (APF) and a control scheme that enforces the constraints is proposed by virtue of the APF technique with the stability of the controlled closed-loop system proved via Lyapunov analysis. Finally, numerical simulations are conducted, and the results demonstrate the effectiveness of the proposed controller.

Independent modeling of various modules of an information system (IS), and consequently database subschemas, may result in formal or semantic conflicts between the modules being modeled. Such conflicts may cause collisions between the integrated database schema of a whole IS and the modeled subschemas. In our previous work, we have proposed criteria and algorithms for identifying and resolving such conflicts so as to provide a consolidation of database subschemas with the integrated database schema with respect to various database concepts, such as domains, relation schemes, primary key constraints and referential integrity constraints. In this paper, we propose a new approach and algorithms for identifying conflicts and testing consolidation of subschemas with the integrated database schema against check constraints. The proposed approach is based on satisfiability modulo theory (SMT) solvers. Hereby, we propose the integration of SMT solvers into our MDSD tool, aimed at supporting a database schema integration process.

Multi-agent path planning (MAPP) is increasingly being used to address resource allocation problems in highly dynamic, distributed environments that involve autonomous agents. Example domains include surveillance automation, traffic control and others. Most MAPP approaches assume hard collisions, e.g., agents cannot share resources, or co-exist at the same node or edge. This assumption unnecessarily restricts the solution space and does not apply to many real-world scenarios. To mitigate this limitation, this paper introduces a more general class of MAPP problems—MAPP in a soft-collision context. In soft-collision MAPP problems, agents can share resources or co-exist in the same location at the expense of reducing the quality of the solution. Hard constraints can still be modeled by imposing a very high cost for sharing. This paper motivates and defines the soft-collision MAPP problem, and generalizes the widely-used M* MAPP algorithm to support the concept of soft-collisions. Soft-collision M* (SC-M*) extends M* by changing the definition of a collision, so paths with collisions that have a quality penalty below a given threshold are acceptable. For each candidate path, SC-M* keeps track of the reduction in satisfaction level of each agent using a collision score, and it places agents whose collision scores exceed its threshold into a soft-collision set for reducing the score. Our evaluation shows that SC-M* is more flexible and more scalable than M*. It can also handle complex environments that include agents requesting different types of resources. Furthermore, we show the benefits of SC-M* compared with several baseline algorithms in terms of path cost, success rate and run time.

A bstract A search is presented for new particles produced at the LHC in proton-proton collisions at s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb − 1 , collected in 2017–2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb − 1 , collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.

Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.

This paper proposes a decoupled sequential convex programming (SCP) method for the time-coordinated trajectory optimization of multiple hypersonic entry vehicles (HEVs). The proposed method introduces the reference trajectory to decouple the collision avoidance constraints, allowing for independent optimization of each vehicle while maintaining overall system safety. Additionally, a lower bound on time coordination is employed to ensure that the vehicles reach their destinations simultaneously. These approaches break down the complex multi-vehicle optimization problem into simpler subproblems, thereby effectively reducing both the number of variables and the associated constraints. Numerical simulations confirm the reliability and convergence of the proposed method, demonstrating significant improvement in computational efficiency.