Artificial Potential Approach Research Articles

2‐dimensional human‐like driver model for autonomous vehicles in mixed traffic

Classical artificial potential approach of motion planning is extended for emulating human driving behaviour in two dimensions. Different stimulus parameters including type of ego-vehicle, type of obstacles, relative velocity, relative acceleration, and lane offset are used. All the surrounding vehicles are considered to influence drivers' decisions. No emphasis is laid on vehicle control; instead, an ego vehicle is assumed to reach the desired state. The study is on human-like driving behaviour modelling. The developed motion planning algorithm formulates repulsive and attractive potentials in a data-driven way in contrast to the classical arbitrary formulation. Interaction between the stimulus parameters is explicitly considered by using multivariate cumulative distribution functions. Comparison of two-dimensional (lateral and longitudinal) performance indicators with a baseline model and generative adversarial networks indicate the effectiveness and suitability of the developed motion planning algorithm in the mixed traffic environment.

Formation control of a second-order UAVs system under switching topology with obstacle/collision avoidance

In this paper, the formation control problem of a second-order unmanned aerial vehicle swarm system with switching topology and collision/obstacle avoidance has been studied. First, the linear consensus protocol for formation control with switching topology is designed using graph theory. The conditions for information consensus are given. Second, the problem of collision/obstacle avoidance is also considered. And the consensus protocol with switching topology and collision/obstacle avoidance is proposed. The improved artificial potential approach is applied to realize collision/obstacle avoidance, which can avoid potential fields falling into the local minimum. Third, system stability under the proposed protocol is proved using the Lyapunov theory. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed protocol.

A Distributed Formation Control Scheme with Obstacle Avoidance for Multiagent Systems

This study considers the problem of formation control for second-order multiagent systems. We propose a distributed nonlinear formation controller where the control input of each follower can be expressed as a product of a nonlinear term that relies on the distance errors under the leader–follower structure. In the leader–follower structure, a small number of agents are assumed to be the leaders, and they are responsible for steering a group of agents to the specific destination, while the rest of the agents are called followers. The stability of the proposed control laws is demonstrated by utilizing the Lyapunov function candidate. To solve the obstacle avoidance problem, the artificial potential approach is employed, and the agents can avoid each possible obstacle successfully without getting stuck in any local minimum point. The control problem of multiagent systems in the presence of unknown constant disturbances is also considered. To attenuate such disturbances, the integral term is introduced, and the static error is eliminated through the proposed PI controller, which makes the system stable; the adaptive controller is designed to reduce the effect of time-varying disturbances. Finally, numerical simulation results are presented to support the obtained theoretical results.

2P1-G09 近接センサを利用したリアルタイム障害物回避アルゴリズムの比較

We compared real time obstacle avoidance algorithm which is using an approach sensor. We made Khatib's artificial potential approach and Kamoi's CPFS algorithm the comparative subject. We compared the collision number of times and arrival time based on each algorithm in the various operational environment. As a result, the potential approach generated a process at high speed by the simple operational society. On the other hand the CPFS algorithm could find a solution in an environment that a robot is surrounded. And also, an obstacle avoidance experiment was made by an experimental robot equipped with proximity sensors. As a result of the experiment, the same result was obtained mostly with a simulation experiment.

Optimal deployment of mobile sensors for target tracking in 2D and 3D spaces

This paper proposes a control strategy to autonomously deploy optimal placements of range-only mobile sensors in 2D and 3D spaces. Based on artificial potential approaches, the control strategy is designed to minimize the intersensor and external potentials. The inter-sensor potential is the objective function for optimal sensor placements. A placement is optimal when the inter-sensor potential is minimized. The external potential is introduced to fulfill constraints on sensor trajectories. Since artificial potential approaches can handle various issues such as obstacle avoidance and collision avoidance among sensors, the proposed control strategy provides a flexible solution to practical autonomous optimal sensor deployment. The control strategy is applied to several optimal sensor deployment problems in 2D and 3D spaces. Simulation results illustrate how the proposed control strategy can improve target tracking performance.

Vehicle path planning in various driving situations based on the elastic band theory for highway collision avoidance

This paper presents an emergency path generation method which could be applied in various driving situations, such as on a straight or a curved road, and in the lane-change and lane-departure scenarios which are the most likely from a traffic viewpoint. It is based on the artificial potential approach and the elastic band theory. The assessment of the emergency path is based on the dynamic performances; the yaw rate and the lateral acceleration of the host vehicle are chosen in this paper. In order to make evasion manoeuvres steadier, a guide potential attached at the front of the obstacle vehicle and a guide potential attached at the rear of the obstacle vehicle are built to affect the moving vehicles in such a way that it encourages the host vehicle to change lane appropriately. Meanwhile, a hazard map of the road environment including the borders and the obstacle vehicles is generated. Thus the forces acting on the corresponding nodes of an elastic band should be zero because of the equilibrium condition; the trajectory with a low hazard is calculated via a numerical method. The simulation results show that the approach is acceptable and leads to a successful collision avoidance manoeuvre in various driving situations.

収束時間を調節可能な人工ポテンシャル法によるマニピュレータの動作計画

This paper presents a new control strategy of the artificial potential field approach to a real-time motion planning problem in a known environment. In the artificial potential approach, the goal is represented by an attractive artificial potential and the obstacles are represented by a repulsive one, so that a robot reaches the goal without colliding with obstacles by using a gradient technique such as the steepest descent method. Although this approach is quite simple and computationally much less expensive than other methods based on the global information on the task space, so far few attentions have been paid to dynamic behavior of the generated trajectories such as movement time from the initial position to the goal and the velocity profile of the end-effector motion. In the present paper, we argue that the dynamic behavior of the arm trajectory to be generated should be taken into account within the framework of the artificial potential field approach, and introduce a time base generator that acts as a time varying gain and determines a dynamic behavior of the arm motion. By synchronizing a potential function used in the artificial potential field with the time base generator, convergence time of the generated arm trajectory can be adjusted through the time base generator without any change of the potential function itself.

Real-time obstacle avoidance using harmonic potential functions

A novel formulation of the artificial potential approach to the obstacle avoidance problem for a mobile robot or a manipulator in a known environment is presented. Previous formulations of artificial potentials for obstacle avoidance have exhibited local minima in a cluttered environment. To build an artificial potential field, the authors use harmonic functions that completely eliminate local minima even for a cluttered environment. The panel method is used to represent arbitrarily shaped obstacles and to derive the potential over the whole space. Based on this potential function, an elegant control strategy for the real-time control of a robot is proposed. Simulation results are presented for a bar-shaped mobile robot and a three-degree-of-freedom planar redundant manipulator. >

An Implicit Approach for a Mobile Robot Running on a Force Field without Generation of Local Minima

The artificial potential approach is good and elegant one to plan a robot path from a start point to a goal point while avoiding the obstacles in the workspace. However, one potential approach constructs its potential field with several local minima, and consequently the robot may stop at one of the local minima until it arrives at the goal point (the global minimum). Another potential approach eliminates the local minima from its potential field in order to move surely the robot at the goal point, and consequently it requires much calculation time to build such a good potential field by a hard pre-processing.For this point of view, we propose a similar approach to plan a robot path between start and goal points surely. This approach defines implicitly a force field Without generation of local minima by an easy pre-processing, and thus let the robot arrive at the goal point easily and certainly. As a result, our approach can be useful for a path-planning one in a works pace whose allocation of the obstacles and the goal point is altered very often.