Traditional Potential Field Research Articles

Reducing Oscillations for Obstacle Avoidance in a Dense Environment Using Deep Reinforcement Learning and Time-Derivative of an Artificial Potential Field

Obstacle avoidance plays a crucial role in ensuring the safe path planning of quadrotor unmanned aerial vehicles (QUAVs). In this study, we propose a hierarchical framework for obstacle avoidance, which combines the use of artificial potential field (APF) and deep reinforcement learning (DRL) for training low-level motion controllers. Unlike traditional potential field methods, our approach modifies the state information received by the motion controllers using the outputs of the APF path planner. Specifically, the assumed target position is pushed away from obstacles, resulting in adjustments to the perceived position errors. Additionally, we address path oscillations by incorporating the target’s velocity information, which is calculated based on the time-derivative of the repulsive force. Experimental results have validated the effectiveness of our proposed framework in avoiding collisions with obstacles and reducing oscillations.

A Time Cooperation Guidance for Multi-Hypersonic Vehicles Based on LSTM Network and Improved Artificial Potential Field Method

Time cooperation guidance is a key technology which can greatly increase the success rate of flight missions. However, it is difficult to satisfy all the strict constraints when designing the guidance system for multiple hypersonic vehicles. To solve these problems, a time cooperation framework is proposed. In this paper, the longitudinal predictor–corrector guidance law is firstly applied to meet the terminal and path constraints simultaneously. To settle the inaccurate estimation problem of residual flight time, a long short-term memory network (LSTM network) is trained and adopted in a time decision module, whose inputs are selected as six-dimensional feature vectors combined with the features of the sequential ballistics. In the time control module, the traditional artificial potential field method is modified to handle the no-fly zone constraints problem. Furthermore, the time potential field as a new type of potential field is added to indirectly control the flight time of hypersonic vehicles. The final simulation results show that the novel time potential field is compatible with the traditional potential field, which can satisfy the no-fly zone and flight time constraints at the same time. Meanwhile, compared with other time cooperative guidance, the algorithm proposed in this paper performs better in terms of time adjustable range.

Research on Active Obstacle Avoidance of Intelligent Vehicles Based on Improved Artificial Potential Field Method

In the study of autonomous obstacle avoidance of intelligent vehicles, the traditional artificial potential field method has the problem that the vehicle may fall into the local minima and lead to obstacle avoidance failure. Therefore, this paper improves the traditional potential field function. Based on the vehicle dynamics model, a strategy of jumping out of local minima based on smaller steering angles is proposed. By finding a smaller steering angle and setting a suitable jump out step length, the intelligent vehicle is enabled to jump out of the local minima. Simulation experiments by MATLAB show that the improved method can jump out of the local minima. By comparing the planned trajectories of the traditional method and the improved method in static and dynamic obstacles situations, the trajectory planned by the improved method is smooth and the curvature is smaller. The planned trajectory is tracked by the Carsim platform, and the test results show that the improved method reduces the front steering wheel angle while the intelligent vehicle satisfies the vehicle dynamics constraints during active obstacle avoidance, which verifies the stability and rationality of the improved method.

Focusing joint inversion of gravity and magnetic data using a clustering stabilizer in a space of weighted parameters

SUMMARY This paper develops a minimum-support focusing stabilizer to perform a joint inversion of the vertical components of gravity and magnetic data using fuzzy c-means clustering (FCM) with the regularized Newton method in a space of weighted parameters. Not only does this joint inversion technology arrive at the conditionally well-posed traditional potential field inversion, but it also increases the structural correlation between multiple inverted models. The FCM and the focusing stabilizer make it possible to balance the convergence of the data space (D) and the model space (M), guiding multimodal geophysical parameters toward assigned petrophysical values, which makes the results more stable and realistic. Two model studies are presented to illustrate the method, a simple synthetic model with two rectangular bodies in a homogenous background and a realistic model of the Volcanogenic Massive Sulfide (VMS) deposits in northeastern New Brunswick, Canada. These models demonstrate that the new focusing joint inversion algorithm produces better images than traditional methods because the FCM function uses the structural correlation of density contrast and magnetic susceptibility as constraints.

A Novel Potential Field Controller for Use on Aerial Robots

Unmanned aerial vehicles, commonly known as drones, have many potential uses in real-world applications. Drones require advanced planning and navigation algorithms to enable them to safely move through and interact with the world around them. This paper presents an extended potential field controller (ePFC) which enables an aerial robot, or drone, to safely track a dynamic target location while simultaneously avoiding any obstacles in its path. The ePFC outperforms a traditional potential field controller with smoother tracking paths and shorter settling times. The proposed ePFC’s stability is evaluated by Lyapunov approach, and its performance is simulated in a MATLAB environment. Finally, the controller is implemented on an experimental platform in a laboratory environment which demonstrates the effectiveness of the controller.

A Multi-Layered Potential Field Method for Water-Jet Propelled Unmanned Surface Vehicle Local Path Planning with Minimum Energy Consumption

Abstract Focusing on the influence of wind and surface currents on local path planning in the marine environment, a multilayered potential field (MPF) method is proposed to minimize the energy consumption of a water-jet propelled unmanned surface vehicle (USV). A synthetic environment framework that can incorporate the information of the base potential field layer and the environment layer is constructed first. This framework provides a base for minimizing the energy consumption of the water-jet propelled USV through proper force weighting. For the purpose of USV path planning, the traditional potential field method is extended by including the velocity information of the USV and the approached obstacles to avoid collisions with dynamic obstacles. The proposed method integrates kinematic control to prevent considering the vehicle as a point mass or rigid body. Finally, simulation and comparison experiments are performed to demonstrate the energy-saving efficiency of the proposed local path planning approach for the water-jet propelled USV.

Consensus-based formation control with dynamic role assignment and obstacle avoidance

In this paper, dynamic role assignment and collision avoidance based on consensus tracking algorithm for multi-agent systems are investigated. First, based on the traditional consensus protocol, dynamic role assignment is introduced to minimize the total cost of the team of agents by solving an optimal problem using Hungarian algorithm. Secondly, considering the existence of local minimum position of traditional potential field function for collision avoidance, modified potential field function is proposed to guarantee the agent avoiding the obstacle. Finally, modified consensus algorithms containing corresponding gradient terms are presented for multi-agents systems. Simulations and experiments are included to show the effectiveness of the proposed algorithms.

UAV Formation Control Based on the Improved APF

Malfunction such as target non-reach ability, local minimum pole and oscillation happens when traditional APF is applied to route planning. This paper proposes an improved APF model, considering the relative distance between the UAV and the target, the relative distance between the lead craft and the wing craft and the safety. We ensure the point of target to be the global minimum point in the entire potential fieldtarget non-reach ability caused by the threat and target point being too close are solved by adopting the modified repulsive potential function; planning failure in which UAVs falling into local minimum point is solved by adopting random fluctuation method; considering the oscillation of traditional potential field, the obstacle link method is proposed. The results of the simulation indicate the single UAV simulation route after obstacles overall planning, track route of UAV formation and formation route with obstacles with turning angle constraints. Requirements of Formation control and formation obstacle avoidance have been well satisfied.

Improved Virtual Potential Field Algorithm Based on Probability Model in Three-Dimensional Directional Sensor Networks

In conventional directional sensor networks, coverage control for each sensor is based on a 2D directional sensing model. However, 2D directional sensing model failed to accurately characterize the actual application scene of image/video sensor networks. To remedy this deficiency, we propose a 3D directional sensor coverage-control model with tunable orientations. Besides, a novel criterion for judgment is proposed in view of the irrationality that traditional virtual potential field algorithms brought about on the criterion for the generation of virtual force. Furthermore, cross-set test is used to determine whether the sensory region has any overlap and coverage impact factor is introduced to reduce profitless rotation from coverage optimization, thereby the energy cost of nodes was restrained and the performance of the algorithm was improved. The extensive simulations results demonstrate the effectiveness of our proposed 3D sensing model and IPA3D (improved virtual potential field based algorithm in three-dimensional directional sensor networks).

An energy-efficient self-deployment with the centroid-directed virtual force in mobile sensor networks

The deployment of sensors at their intended locations enables not only adequate sensing coverage of the area of interest, but also efficient sensor resource management. However, for static wireless sensor networks, it is sometimes impossible to manually deploy the sensors in those locations as they can be distributed in unexploited, hostile, or disaster areas. Nevertheless, if each sensor has locomotive capability, they can re-deploy themselves using the location information of neighboring sensors. In particular, in our previous study, we showed that the coverage area can be efficiently expanded by having sensors move to the centroids of their Voronoi polygon generated using the location information of neighboring sensors. In this paper, we present an energy-efficient self-deployment scheme to utilize the attractive force generated from the centroid of a sensor’s local Voronoi polygon as well as the repulsive force frequently used in self-deployment schemes using the potential field. We also provide the design and implementation of the simulator used to analyze the performance of the proposed approach as compared with existing self-deployment schemes. The simulation results show that our scheme can achieve a higher coverage and enables less sensor movements in shorter times than self-deployment schemes using the traditional potential field.

An Improved Algorithm of Virtual Potential Field in Directional Sensor Networks

In view of the irrationality that traditional virtual potential field algorithms brought about on the criterion for the generation of virtual force in directional sensor networks, we propose a novel criterion for judgment. Cross-set test is used to determine whether the sensory region has any overlap, thereby restrain the energy cost of nodes and the performance of the algorithm is improved. In simulation experiment, the validity of the novel criterion and IAPD (improved algorithm of virtual potential field in directional sensor networks) are verified by the compare of IAPD and traditional virtual field algorithms.

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.

Shared Potential Fields and their place in a multi-robot co-ordination taxonomy

Previously our novel Shared Potential Field (SPF) method has been introduced and compared against a non-sharing control in both simulation and laboratory settings. In this paper, extended from a paper presented at the CIRAS 2008 conference, we compare the SPF against an existing type of robot architecture, a hybrid robotic system. The SPF method is compared to the traditional potential field method, and it is shown that the SPF is less susceptible to the traditional limitations of potential fields. The SPF method’s position in Farinelli’s multi-robot taxonomy is also discussed, and it is shown that rather than being placed in one category it encompasses two, corresponding to the two levels of control within the architecture. In experiments, the multi-robot systems are given the task of traversing an unknown environment, in an attempt to locate a target. The metric of performance for this task was the time taken to find the target. Experiments show that the hybrid system showed similar performance to the non-sharing control. In contrast, our Pessimistic variant of the SPF outperformed the hybrid system in the cluttered environment, and the Optimistic SPF variant outperformed the hybrid system in the sparse environment. We conclude that the SPF method reacts more robustly to the dynamic nature of the real world, and so performed significantly better throughout the experimentation.

High-speed navigation of unmanned ground vehicles on uneven terrain using potential fields

SUMMARYMany applications require unmanned ground vehicles (UGVs) to travel at high speeds on sloped, natural terrain. In this paper, a potential field-based method is proposed for UGV navigation in such scenarios. In the proposed approach, a potential field is generated in the two-dimensional “trajectory space” of the UGV path curvature and longitudinal velocity. In contrast to traditional potential field methods, dynamic constraints and the effect of changing terrain conditions can be easily expressed in the proposed framework. A maneuver is chosen within a set of performance bounds, based on the local potential field gradient. It is shown that the proposed method is subject to local maxima problems, rather than local minima. A simple randomization technique is proposed to address this problem. Simulation and experimental results show that the proposed method can successfully navigate a small UGV between predefined waypoints at speeds up to 7.0 m/s, while avoiding static hazards. Further, vehicle curvature and velocity are controlled during vehicle motion to avoid rollover and excessive side slip. The method is computationally efficient, and thus suitable for onboard real-time implementation.

日本の重力探査事情―地下構造とのかかわり

This article introduces the gravity methods for petroleum explorations. Gravitational methods are based on the sensitive measurement at the surface of small variations in the gravitational field, but the measurement technology of land gravimeter has already established. In the present, the new trends of gravity measurement are coming. Gravity measurements by ship and airplane are possible. Therefore, Bouguer anomaly map of Japan map was compiled from about 350, 000 land gravity data, about 1, 000, 000 ship-borne gravity data and altimeter data from satellite in area where observation data are sparse, and the gravity basement of Japan was obtained from these data.New technology, which is capable of accurately measuring the full gravity gradient tensor, has only recently become available in the exploration geophysics domain from military research projects (e. g. the Bell Geospace gradiometer). Gravity gradiometry data have superior characteristics to traditional gravity measurements. The components of the gravity gradient tensor hold much more information about the subsurface than do traditional potential field measurements. Such gravity data can constrain geology in areas where seismic interpretation is difficult, such as sub-basalt and sub-salt. These data are capable of resolving shallow structures, such as the limbs of salt domes, far more accurately than can seismic surveys.

A fast approach for the robust trajectory planning of redunant robot manipulators

AbstractIn this article, a fast approach for robust trajectory planning, in the task space, of redundant robot manipulators is presented. The approach is based on combining an original method for obstacle avoidance by the manipulator configuration with the traditional potential field approach for the motion planning of the end‐effector. This novel method is based on formulating an inverse kinematics problem under an inexact context. This procedure permits dealing with the avoidance of obstacles with an appropriate and easy to compute null space vector; whereas the avoidance of singularities is attained by the proper pseudoinverse perturbation. Furthermore, it is also shown that this formulation allows one to deal effectively with the local minimum problem frequently associated with the potential field approaches. The computation of the inverse kinematics problem is accomplished by numerically solving a linear system, which includes the vector for obstacle avoidance and a scheme for the proper pseudoinverse perturbation to deal with the singularities and/or the potential function local minima. These properties make the proposed approach suitable for redundant robots operating in real time in a sensor‐based environment. The developed algorithm is tested on the simulation of a planar redundant manipulator. From the results obtained it is observed that the proposed approach compares favorably with the other approaches that have recently been proposed. © 1995 John Wiley & Sons, Inc.