A Motion Planning Algorithm for Redundant Manipulators Using Rapidly Exploring Randomized Trees and Artificial Potential Fields

Considering the collision-prone problem near the initial point, goal non-reachable with obstacle nearby (GNWON) and the chattering problem of the traditional artificial potential field algorithm(TAPF), the attractive potential field function and the repulsive potential field function are improved respectively, and an improved adaptive artificial potential field algorithm(AAPF) is proposed. The algorithm can calculate the different potential field of each path point on-line in real time. On this basis, a 2D improved adaptive artificial potential field algorithm is extended to 3D space for autonomous obstacle avoidance planning of quadrotor UAV. A 3D obstacle model is established, and the principle of obstacle avoidance at the nearest contact point is designed to calculate the repulsive potential field. Then the obstacle avoidance planning strategy of Four-rotor UAV based on 3D adaptive potential field algorithm is designed. The simulation results show that the improved adaptive algorithm effectively compensates for the shortcomings of the traditional artificial potential field method and can be applied in 3D.

Car-following model based on artificial potential field with consideration of horizontal curvature in connected vehicles environment

Energy-optimal adaptive artificial potential field method for path planning of free-flying space robots

In this paper, we present a rule‐based heuristic method of motion planning for an animated human agent with massively redundant degrees of freedom. It constructs motion plans to achieve 3D‐space goals of control points on the body, e.g. a hand, while avoiding collisions. Like the artificial potential field approach, the method performs motion decisions in 3D world space rather than in joint space. To handle the massively redundant degrees of freedom, we use a qualitative kinematic model, which specifies motions of body parts and dependencies among them, without specifying the exact distance parameters. This model helps the body select appropriate primitive motions for given goals of control points more globally than does the gradient vector of an artificial potential field of the body. The method simulates (in imagination) the suggested plan to find whether some body parts hit objects, and how much they penetrate the objects. Based on this simulated collision information, the method suggests intermediate goals of the collision body parts. A subplan to achieve these intermediate goals is again postulated by using the qualitative kinematic model. This explicit reasoning helps alleviate the basic cause of local minima in the potential field approach, namely, conflicts between attractive potential fields due to goal positions of control points and repulsive potential fields due to obstacles.

Probabilistic multiresolution occupancy grid modeling has recently been developed to map both 2D and 3D cluttered spaces. These models can be used to provide an enhanced representation of the cluttering state of space in a robot workspace. As a result, they reveal to be promising tools to improve classical potential field based robot path planning strategies. These approaches rely on a combination of repulsive and attractive potential fields to attract the robot toward a given goal while ensuring safe distance from the obstacles. This paper proposes an new approach to directly compute repulsive and attractive potential fields from the probabilistic occupancy models without the need for distance tables or wave propagation. Experimentation revealed that multiresolution probabilistic models encoded as quadtrees or octrees significantly reduce processing time and speed up robot operation.

Trajectory planning of a free-floating dual-arm space robot with minimal base disturbance in obstacle environments

A novel model predictive artificial potential field based ship motion planning method considering COLREGs for complex encounter scenarios

Motion planning for multi-HUG formation in an environment with obstacles

A distributed swarm aggregation algorithm is developed for a team of multiple kinematic agents. Each agent is assigned with a control law which is the sum of two elements: a repulsive potential field, which is responsible for the collision avoidance objective, and an attractive potential field, that drives the agents to a configuration where they are close to each other and forces the agents that are initially located within the sensing radius of an agent to remain within this area for all time. In this way, the connectivity properties of the initially formed communication graph are rendered invariant for the trajectories of the closed loop system. It is shown that agents converge to a configuration where each agent is located at a bounded distance from each of its neighbors. The results are also extended to the case of nonholonomic kinematic agents and to the dynamic edge addition case, for which we derive a better bound in the swarm size than in the static case.

The objective of this paper is decentralized swarm aggregation with collision avoidance. Specifically, each agent is assigned with a control law which is the sum of two elements: a repulsive potential field, which is responsible for the collision avoidance objective, and an attractive potential field, that forces the agents to converge to a configuration where they are close to each other. It is shown that under the proposed control law agents converge to a configuration where each agent is located at a bounded distance from each of its neighbors. Connectivity of the communication graph is then shown to be a sufficient condition for swarm aggregation. When a global objective is imposed, namely aggregation of the robotic swarm close to a desired location we show that only one agent, which plays the role of the leader of the swarm, has to be aware of this objective. The leader is shown to be able to drag the swarm around the desired location. We also show that these results are applied to both cases of robots satisfying single integrator kinematics and kinematic unicycle-type robots.

In this paper, an advanced PSO has been proposed to solve multi-modal functions optimization. Multiple swarms are used to optimize parallel search and artificial repulsive potential field on local points are set up to prevent multiple swarm converging to the same place and repeatedly search. In addition, this paper provides a theoretical basis for the strategy of multi-swarm parallel search used in the advanced algorithm. Finally, the method is tested on some benchmark functions and the results show a superior performance compared to the others PSO variant.

The artificial potential field method is frequently employed for obstacle avoidance both of mobile robots and of manipulator arms. This paper extends previous results for planar problems to the general n-dimensional case. The complexity is reduced by projecting the n-dimensional workspace onto a 2D subspace called operation plane. Furthermore, only the closest obstacles are taken into account when designing the artificial potential field. Still, global convergence is achieved. The methodology is illustrated with several numerical examples.

To enhance the navigation and control efficiency of mobile robots in the field of health monitoring, a novel path planning and control strategy for mobile robots with uncertain dynamics based on improved artificial potential fields is proposed in this paper. Specifically, we propose an attractive potential field rotation method to overcome the limitation that traditional artificial potential fields tend to fall into local minima. Then, we define a new class of attractive potential fields to address the goals non-reachable with obstacles nearby (GNRON) and collisions caused by excessive attractive force at long distances from the target point. Furthermore, a control law is proposed for the mobile robot with uncertain dynamics, and the stability of the closed-loop system is rigorously proven using the Lyapunov method. Finally, the feasibility and effectiveness of the proposed method are verified by simulations and experiments.

In recent years, different kinds of natural hazards or man-made disasters happened that were diversified and difficult to control with heavy casualties. In this paper, we focus on the rapid and systematic evacuation of large-scale densities of people after disasters to reduce loss in an effective manner. The optimal evacuation planning is a key challenge and becomes a hotspot of research and development. We design our system based on an Internet of Things (IoT) scenario that utilizes a mobile cloud computing platform in order to develop the crowd lives oriented track and help optimization system (CLOTHO). CLOTHO is an evacuation planning system for large-scale densities of people in disasters. It includes the mobile terminal (IoT side) for data collection and the cloud backend system for storage and analytics. We build our solution upon a typical IoT/fog disaster management scenario and we propose an IoT application based on an evacuation planning algorithm that uses the artificial potential field (APF), which is the core of CLOTHO. APF is conceptualized as an IoT service, and can determine the direction of evacuation automatically according to the gradient direction of the potential field, suitable for rapid evacuation of large population. Based on APF, we propose an evacuation planning algorithm names as APF with relationship attraction (APF-RA). APF-RA guides the evacuees with relationship to move to the same shelter as much as possible, to calm evacuees and realize a more humanitarian evacuation. The experimental results show that CLOTHO (using APF and APF-RA) can effectively improve convergence rate, shorten the evacuation route length and evacuation time, and make the remaining capacity of the surrounding shelters well balanced.

Artificial potential field method is widely used in robot path planning because of its simplicity, efficiency and smooth path generation. In this paper, based on the introduction of the basic principle of the artificial potential field method, the limitations of the algorithm are analysed in depth, and improvement methods are summarized for these problems. Aiming at the problem that the target near the obstacle is unreachable in the traditional artificial potential field method, an improved repulsive force potential field function is used to introduce the distance between the robot and the target point into the potential field function, so that the potential field of the target position is minimized in the global potential field, so that the robot can successfully reach the target. Using the obstacle connection method, the robot can quickly get rid of the local minimum point, go out of the local minimum area, and complete the path planning. The simulation results show that the method is effective.