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In complex systems, single micro/nanorobots encounter challenges related to limited loading capacity and navigation, hindering their effective utilization in targeted therapy and drug delivery. To solve these challenges, this paper explores potential field mechanisms as a means to simulate natural collective behavior. This approach aims to enhance the navigation and efficiency of micro/nanorobots in high-demand therapeutic areas. The mechanism enables micro/nanorobots to dynamically adapt to environmental gradients, minimizing off-target effects while maximizing therapeutic efficacy and enhancing robustness through redundancy. Additionally, this study introduces innovative distributed learning and cooperative control strategies. Each micro/nanorobot updates its navigation strategy through local interactions and influences with the dynamic environment. This allows micro/nanorobots to share information and improve their navigation toward therapeutic targets. The simulation results demonstrate that collective behavior and potential field mechanisms can enhance the precision and efficiency of targeted therapy and drug delivery in dynamically changing environments. In conclusion, the proposed approach can improve the limitations of single micro/nanobot, offering new possibilities for the development of advanced therapeutics and drug delivery systems.
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In this work, we address the single robot navigation problem within a planar and arbitrarily connected workspace. In particular, we present an algorithm that transforms any static, compact, planar workspace of arbitrary connectedness and shape to a disk, where the navigation problem can be easily solved. Our solution benefits from the fact that it only requires a fine representation of the workspace boundary (i.e., a set of points), which is easily obtained in practice via SLAM. The proposed transformation, combined with a workspace decomposition strategy that reduces the computational complexity, has been exhaustively tested and has shown excellent performance in complex workspaces. A motion control scheme is also provided for the class of non-holonomic robots with unicycle kinematics, which are commonly used in most industrial applications. Moreover, the tuning of the underlying control parameters is rather straightforward as it affects only the shape of the resulted trajectories and not the critical specifications of collision avoidance and convergence to the goal position. Finally, we validate the efficacy of the proposed navigation strategy via extensive simulations and experimental studies.
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Linear manipulators are versatile linear robotics systems that can be reprogrammed to accommodate product changes quickly and are flexible to meet unique requirements. Such robotic systems tend to have higher accuracy, making them the perfect automation solution for those mundane, repetitious tasks. With the demand for linear systems in real-life applications expanding consistently, this paper addresses motion planning and control (MPC) of a new modified unanchored linear manipulator consisting of an n-link robotic arm mounted on a mobile slider along a rail. Using the method of the Lyapunov-based Control Scheme (LbCS), new centralized acceleration-based controllers are designed for the navigation of the system to an unreachable target. Via the scheme, the unanchored manipulator can perform assigned tasks with enhanced reachability. The limitations and singularities of the linear manipulator are treated as artificial obstacles in this motion control scheme. The robotic arm manipulator utilized in this research can reposition its base link to a desired location in the workplace due to changes in work requirements. The effectiveness of the motion planner and the resulting acceleration-based control laws are validated numerically using the Runge-Kutta Method and illustrated via computer simulations. The controllers devised in this research can solve specific and targeted motion control problems of smart cities' modern mechanical systems. The unanchored linear manipulator could be used in various disciplines where pick-and-place, assembly, material handling, and surgical procedures are required.
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Rechargeable magnesium batteries are particularly advantageous for renewable energy storage systems. However, the inhomogeneous Mg electrodeposits greatly shorten their cycle life under practical conditions. Herein, the epitaxial electrocrystallization of Mg on a three-dimensional magnesiophilic host is implemented via the synergy of a magnesiophilic interface, lattice matching, and electrostatic confinement effects. The vertically aligned nickel hydroxide nanosheet arrays grown on carbon cloth (abbreviated as "Ni(OH)2@CC") have been delicately designed, which satisfy the essential prerequisite of a low lattice geometrical misfit with Mg (about 2.8%) to realize epitaxial electrocrystallization. Simultaneously, the ionic crystal nature of Ni(OH)2 displays a periodic and hillock-like electrostatic potential field over its exposed facets, which can precisely capture and confine the reduced Mg0 species onto the local electron-enriched sites at the atomic level. The Ni(OH)2@CC substrate undergoes sequential Mg-ion intercalation, underpotential deposition, and electrocrystallization processes, during which the uniform, lamellar Mg electrodeposits with a locked crystallographic orientation are formed. Under practical conditions (10 mA cm-2 and 10 mAh cm-2), the Ni(OH)2@CC substrate exhibits stable Mg stripping/plating cycle performances over 600 h, 2 orders of magnitude longer than those of the pristine copper foil and carbon cloth substrates.
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Considering that the actual operating environment of UAV is complex and easily disturbed by the space environment of urban buildings, the RoutE Planning Algorithm of Resilience Enhancement (REPARE) for UAV 3D route planning based on the A* algorithm and artificial potential fields algorithm is carried out in a targeted manner. First of all, in order to ensure the safety of the UAV design, we focus on the capabilities of the UAV body and build a risk identification, assessment, and modeling method such that the mission control parameters of the UAV can be determined. Then, the three-dimensional route planning algorithm based on the artificial potential fields algorithm is used to ensure the safe operation of the UAV online and in real time. At the same time, by adjusting the discriminant coefficient of potential risks in real time to deal with time-varying random disturbance encountered by the UAV, the resilience of the UAV 3D flight route planning can be improved. Finally, the effectiveness of the algorithm is verified by the simulation. The simulation results show that the REPARE algorithm can effectively solve the traditional route planning algorithm's insufficiency in anti-disturbance. It is safer than a traditional A* route planning algorithm, and its running time is shorter than that of the traditional artificial potential field route planning algorithm. It solves the problems of local optimization, enhances the UAV's ability to tolerate general uncertain disturbances, and eventually improves resilience of the system.
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Path planning techniques are of major importance for the motion of autonomous systems. In addition, the chosen path, safety, and computational burden are essential for ensuring the successful application of such strategies in the presence of obstacles. In this context, this work introduces a modified potential field method that is capable of providing obstacle avoidance, as well as eliminating local minima problems and oscillations in the influence threshold of repulsive fields. A three-dimensional (3D) vortex field is introduced for this purpose so that each robot can choose the best direction of the vortex field rotation automatically and independently according to its position with respect to each object in the workspace. A scenario that addresses swarm flight with sequential cooperation and the pursuit of moving targets in dynamic environments is proposed. Experimental results are presented and thoroughly discussed using a Crazyflie 2.0 aircraft associated with the loco positioning system for state estimation. It is effectively demonstrated that the proposed algorithm can generate feasible paths while taking into account the aforementioned problems in real-time applications.
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Robótica , Aeronaves , Algoritmos , Movimiento (Física) , Robótica/métodosRESUMEN
This paper presents a real-time global path planning method for mobile robots using harmonic functions, such as the Poisson equation, based on the Proper Generalized Decomposition (PGD) of these functions. The main property of the proposed technique is that the computational cost is negligible in real-time, even if the robot is disturbed or the goal is changed. The main idea of the method is the off-line generation, for a given environment, of the whole set of paths from any start and goal configurations of a mobile robot, namely the computational vademecum, derived from a harmonic potential field in order to use it on-line for decision-making purposes. Up until now, the resolution of the Laplace or Poisson equations has been based on traditional numerical techniques unfeasible for real-time calculation. This drawback has prevented the extensive use of harmonic functions in autonomous navigation, despite their powerful properties. The numerical technique that reverses this situation is the Proper Generalized Decomposition. To demonstrate and validate the properties of the PGD-vademecum in a potential-guided path planning framework, both real and simulated implementations have been developed. Simulated scenarios, such as an L-Shaped corridor and a benchmark bug trap, are used, and a real navigation of a LEGO®MINDSTORMS robot running in static environments with variable start and goal configurations is shown. This device has been selected due to its computational and memory-restricted capabilities, and it is a good example of how its properties could help the development of social robots.
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Robot navigation allows mobile robots to navigate among obstacles without hitting them and reaching the specified goal point. In addition to preventing collisions, it is also essential for mobile robots to sense and maintain an appropriate battery power level at all times to avoid failures and non-fulfillment with their scheduled tasks. Therefore, selecting the proper time to recharge the batteries is crucial to address the navigation algorithm design for the robot's prolonged autonomous operation. In this paper, a machine learning algorithm is used to ensure the extended robot autonomy based on a reinforcement learning method combined with a fuzzy inference system. The proposal enables a mobile robot to learn whether to continue through its path toward the destination or modify its course on the fly, if necessary, to proceed toward the battery charging station, based on its current state. The proposal performs a flexible behavior to choose an action that allows a robot to move from a starting to a destination point, guaranteeing battery charge availability. This paper shows the obtained results using an approach with thirty-six states and its reduction with twenty states. The conducted simulations show that the robot requires fewer training epochs to achieve ten consecutive successes in the fifteen proposed scenarios than traditional reinforcement learning methods exhibit. Moreover, in four scenarios, the robot ends up with a battery level above 80%, that value is higher than the obtained results with two deterministic methods.
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The modes in which the lithosphere deforms during continental collision and the mechanisms involved are not well understood. While continental subduction and mantle delamination are often invoked in tectonophysical studies, these processes are difficult to be confirmed in more complex tectonic regions such as the Gibraltar Arc. We study the present-day density and compositional structure of the lithosphere along a transect running from South Iberia to North Africa crossing the western Gibraltar Arc. This region is located in the westernmost continental segment of the African-Eurasian plates, characterized by a diffuse transpressive plate boundary. An integrated and self-consistent geophysical-petrological methodology is used to model the lithosphere structure variations and the thermophysical properties of the upper mantle. The crustal structure is mainly constrained by seismic experiments and geological data, whereas the composition of the lithospheric mantle is constrained by xenolith data. The results show large lateral variations in the topography of the lithosphere-asthenosphere boundary. We distinguish different chemical lithospheric mantle domains that reproduce the main trends of the geophysical observables and the modeled P and S wave seismic velocities. A sublithospheric body colder than the surrounding mantle is needed beneath the Betics-Rif to adjust the measured potential fields. We link this body to the Iberian slab localized just to the east of the profile and having some effect on the geoid and Bouguer anomalies. Local isostasy allows explaining most of the topography, but an elastic thickness higher than 10 km is needed to explain local misfits between the Atlas and the Rif Mountains.
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The Chain Fracture Zone is a 300-km-long transform fault that offsets the Mid-Atlantic Ridge. We analyzed new multibeam bathymetry, backscatter, gravity, and magnetic data with 100% multibeam bathymetric data over the active transform valley and adjacent spreading segments as part of the Passive Imaging of the Lithosphere Asthenosphere Boundary (PI-LAB) Experiment. Analyses of these data sets allow us to determine the history and mode of crustal formation and the tectonic evolution of the transform system and adjacent ridges over the past 20 Myr. We model the total field magnetic anomaly to determine the age of the crust along the northern ridge segment to better establish the timing of the variations in the seafloor fabric and the tectonic-magmatic history of the region. Within the active transform fault zone, we observe four distinct positive flower structures with several en échelon fault scarps visible in the backscatter data. We find up to -10 mGal residual Mantle Bouguer Anomaly in the region of the largest positive flower structure within the transform zone suggesting crustal thickening relative to the crustal thinning typically observed in fracture zones in the Atlantic. The extensional/compressional features observed in the Chain Transform are less pronounced than those observed further north in the Vema, St. Paul, and Romanche and may be due to local ridge segment adjustments.
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Drawing an analogy with quantum mechanics, a new Lagrangian is proposed for a variational formulation of the Navier-Stokes equations which to-date has remained elusive. A key feature is that the resulting Lagrangian is discontinuous in nature, posing additional challenges apropos the mathematical treatment of the related variational problem, all of which are resolvable. In addition to extending Lagrange's formalism to problems involving discontinuous behaviour, it is demonstrated that the associated equations of motion can self-consistently be interpreted within the framework of thermodynamics beyond local equilibrium, with the limiting case recovering the classical Navier-Stokes equations. Perspectives for applying the new formalism to discontinuous physical phenomena such as phase and grain boundaries, shock waves and flame fronts are provided.
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In mobile wireless sensor networks (MWSN), nodes are allowed to move autonomously for deployment. This process is meant: (i) to achieve good coverage; and (ii) to distribute the communication load as homogeneously as possible. Rather than optimizing deployment, reactive algorithms are based on a set of rules or behaviors, so nodes can determine when to move. This paper presents an experimental evaluation of both reactive deployment approaches: rule-based and behavior-based ones. Specifically, we compare a backbone dispersion algorithm with a social potential fields algorithm. Most tests are done under simulation for a large number of nodes in environments with and without obstacles. Results are validated using a small robot network in the real world. Our results show that behavior-based deployment tends to provide better coverage and communication balance, especially for a large number of nodes in areas with obstacles.