Adaptive Potential Field Research Articles

Autonomous Robot Navigation Using Adaptive Potential Field

This work presents a novel approach to mobile robot autonomous navigation, utilizing enhanced artificial potential fields in conjunction with path planning,obstacle avoidance and SLAM . In traditional adaptive potential fields, which integrate sensor data to enable robots to navigate safely and efficiently through various environments. The robot's perception of its surroundings, obtained through sensors like cameras, ultrasonic devices, is processed to create a dynamic potential field that reflects the spatial distribution of obstacles, goals, and other relevant features.We propose the design and implementation of an autonomous robot-based real-time vision-based system for detecting and tracking features in a structured environment ensuring robust obstacle avoidance and path planning. Keywords- Adaptive Potential Fields, Autonomous Navigation, Autonomous Systems, Machine Learning in Robotics, Motor driver, Obstacle Avoidance, Path Planning, Raspberry Pi, Real-time Adaptation, ROS (Robot Operating System), Sensor-based Navigation, Servo Motor, SLAM (Simultaneous Localization and Mapping),Ultrasonic Sensors

Clothoid Curve-Based Emergency-Stopping Path Planning With Adaptive Potential Field for Autonomous Vehicles

Clothoid Curve-Based Emergency-Stopping Path Planning With Adaptive Potential Field for Autonomous Vehicles

Dynamic Path Planning of Vehicles Based on the Adaptive Potential Field and Hierarchical Replacement Immune Algorithm

Dynamic Path Planning of Vehicles Based on the Adaptive Potential Field and Hierarchical Replacement Immune Algorithm

Collision-free output-feedback super-twisting control of robotic surface vehicles for coordinated path following with experiments

This paper addresses the coordinated path following (CPF) control of robotic surface vehicles (RSVs) in the presence of multiple obstacles and lumped disturbances over a resource-constrained wireless network. A collision-free output-feedback super-twisting control architecture is proposed for CPF without using velocity measurements. Specifically, robust exact differentiator observers are designed at first to recover the unmeasured velocities, and estimate the lumped disturbances, simultaneously. Next, anti-disturbance output-feedback super-twisting control laws are proposed to achieve individual path following in earth-fixed reference frame, and an adaptive potential field with a user-defined collision hazard degree is introduced to avoid the collisions with obstacles. To reduce the network traffic, self-triggered path updating laws are then designed for avoiding the continuous transmitting and listening of neighboring RSVs. The stability analysis shows that the closed-loop system is input-to-state stable outside the collision avoidance region, and Zeno behavior will not happen. Experiments are finally conducted to validate the efficacy of the proposed collision-free output-feedback super-twisting control method for the self-triggered CPF of RSVs.

Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

Adaptive Potential Field-Based Path Planning for Complex Autonomous Driving Scenarios

An adaptive potential field is designed to adapt the acceleration/deceleration and mass of the obstacle. The potential fields are established in a transformed road coordinate system to improve the feasibility and robustness. A path planning method is proposed based on the designed adaptive potential field to improve the driving safety and the ride comfort of autonomous vehicles in complex driving scenarios, which including the cut-in, emergency braking, obstacle sudden accelerating during overtaking and the curve road driving scenarios. The effectiveness of the proposed method is validated by simulations with constructed and real data, respectively. The $TTC\text{s}$ (Time-to-Collision) and the maximum lateral accelerations are used to evaluate the improvements on safety and ride comfort. The results show that both the driving safety and ride comfort are efficiently improved by using the proposed approach in emergency braking and accelerating scenarios. Meanwhile, the proposed method can be well applied in a curve road driving environment.

Adaptive vs. conventional potential field approaches for solving navigation problems of a real car-like wheeled robot

Adaptive Potential Field Methods (APFMs) have been proposed in this paper and their performances have been compared among them and with that of Conventional Potential Field Method (CPFM) to solve navigation problems of the mobile robot. The performance of a potential field method (PFM) depends on its chosen attractive and repulsive potential functions and the constant terms associated with them. Robots that navigate using the CPFM may not find time-optimal path and may suffer from the deadlock situations. APFM could solve the said problems by changing the constant terms associated with the potential functions to cope with the varying situations of the environment. The performances of the proposed adaptive and CPFMs have been tested through computer simulations and on a real car-like wheeled robot. The proposed PFM is found to perform better than the conventional one.