Dynamic Potential Function Research Articles

Dynamic potential stochastic resonance for weak signal detection

To suppress the low-frequency interference noise, a dynamic potential stochastic resonance (DPSR) model is proposed in this paper for weak signal detection. The DPSR model introduces a single dynamic parameter k that simplifies parameter optimization. Its dynamic potential function can adaptively adjust to match noisy input signals. This model provides a new nonlinear model for triggering the SR phenomenon. Experimental results indicate that compared to the traditional methods that rely on clear interference frequency distributions to filter low-frequency components directly, the SR model offers greater flexibility and convenience. Unlike classical SR models, the proposed DPSR model demonstrates a 1.5 dB improvement in output performance for suppressing low-frequency interference. Therefore, the DPSR model not only robustly suppresses interference but also effectively enhances and detects characteristic signals in variant-noise environments. Application to sea trial signals highlights the superior performance of the DPSR model in significantly reducing low-frequency interference and improving target signal recognizability compared to other models.

Dynamic optimal payload path planning of mobile manipulators among moving obstacles

This paper is dealt with dynamic analysis of the wheeled mobile manipulators in the presence of moving obstacles considering optimal payload criterion. General dynamic formulation of the system was derived, and the moving obstacle avoidance strategy was proposed in terms of dynamic potential functions. The problem of dynamic motion planning and payload maximization was formulated using open-loop optimal control theory. Then, the indirect solution based on Pontryagin’s minimum principle was employed to solve the problem. Using the proposed method, complete nonlinear states and control constraints were treated without any simplifications such as linearizing the dynamics equations, discretizing the robot’s workspace, or parameterizing the solution. The proposed method will be useful for the system design and in the situation where the trajectories of obstacles are predefined. Finally, capability and applicability of the proposed method were investigated by the number of simulations on a two-link mobile manipulator.

Trajectory optimization of nonholonomic mobile manipulators departing to a moving target amidst moving obstacles

How to plan the optimal trajectory of nonholonomic mobile manipulators in dynamic environments is a significant and challenging task, especially in the system with a moving target. This paper presents trajectory optimization of a nonholonomic mobile manipulator in dynamic environment pursuing a moving target. Full nonlinear dynamic equations of the system considering the nonholonomic constraints of wheels are presented. Then, dynamic motion planning of the system is formulated as an optimal control problem considering moving obstacle avoidance conditions. Accordingly, a new formulation of dynamic potential function was proposed based on the dynamic distance between colliding objects. In addition, an appropriate boundary value for a moving target was defined, and the resulted boundary value problem was solved to optimize the trajectory of the system. To solve the problem, an indirect solution of optimal control was applied which leads to transform the optimal control problem into a set of coupled differential equations. To demonstrate the efficiency and applicability of the method a number of simulations and experiments was performed for a spatial nonholonomic mobile manipulator.

The Study of Soccer Robot Path Planning Based on Grid-Based Potential Field Method Improvements

An improved dynamic Grid-based potential field method was proposed based on the consideration that the goal, robot and obstacles in robot soccer compete are all dynamic. We combined the advantages of potential field method and the grid method, set the grid method to represent the environment, and got dynamic potential function in the potential field method. We used dynamic potential function to form the inspire function of the search algorithm A* which is used for the search of adjacent nodes. The dynamic Grid-based potential field method meets the real-time planning requirements in the complex and dynamic environment. And it has received very good results in solving the local minima problem of the traditional potential field and improving the planning efficiency. It is better in security and reliability. Simulation results show that the method is feasible and effective in soccer robot path planning.

Guiding flows for controlling crowds

In this paper, we present a novel method for controlling massive crowds by using control particles. Our method differs from previous ones that generate attraction (or repelling) forces around the control particles. Instead of doing this, we create a steady-state, flow-like control field that guides the crowd to move along with the control particles. Our control field can be naturally incorporated into the original simulation by using density-based weighted blending. Although we focus on simulation methods that use dynamic potential functions, our method can also be used to improve the controllability of agent-based simulation methods. Since the control particles can be easily manipulated by traditional key-framing, our method provides animators with an intuitive interface for manipulating the position of crowd over time. We illustrate the effectiveness of our method on several examples.

DYPOF: Dynamically Adaptive RBF Neural Network with Potential Functions

We present a method for data classification, which performs recognition based on a set of potential fields synthesized over the domain on input space by a number of potential function units. Proposed is DYPOF (DYnamic POtential Functions) neural network (NN), based on radial basis functions with a two-stage training procedure. A fundamental component in building DYPOF is a potential function entity (PFE), designed to generate a respective decision potential function. The desirable shape of the potential field characterizing the distribution of training set is synthesized by adjusting the weights as well as the parameter vectors of cumulative potential functions generated by the PFEs. The automatic adjustment of the minimum necessary number of hidden units-learning adjustment units (LAU)-for a given set of teaching patterns provides the network with a capability of performing dynamic adaptation and self-organization. We investigate the dependence of our method on these parameters and apply it to several data sets. The results indicate the power of the PFEs in generating classification solutions for various shapes of teaching patterns that are robust with respect to noise in the data.