scholarly journals A Real-Time Obstacle Avoidance Method for Autonomous Vehicles Using an Obstacle-Dependent Gaussian Potential Field

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Jang-Ho Cho ◽  
Dong-Sung Pae ◽  
Myo-Taeg Lim ◽  
Tae-Koo Kang
Keyword(s):  
Real Time ◽  
Obstacle Avoidance ◽  
Calculation Method ◽  
Potential Field ◽  
Autonomous Vehicles ◽  
Field Calculation ◽  
Gaussian Potential

A new obstacle avoidance method for autonomous vehicles calledobstacle-dependent Gaussian potential field(ODG-PF) was designed and implemented. It detects obstacles and calculates the likelihood of collision with them. In this paper, we present a novel attractive field and repulsive field calculation method and direction decision approach. Simulations and the experiments were carried out and compared with other potential field-based obstacle avoidance methods. The results show that ODG-PF performed the best in most cases.

Development of Autonomous Mobile Robot for Obstacle Avoidance

1993 ◽  
Vol 5 (5) ◽  
pp. 481-486 ◽  
Author(s):  
Masafumi Uchida ◽  
◽  
Syuichi Yokoyama ◽  
Hideto Ide ◽  
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Potential Method ◽  
Working Environment ◽  
Planning Problem ◽  
Autonomous Mobile Robot ◽  
The Real

The potential method is superior for solving the problem of motion planning; however, it must address the problem of the real-time generation of potential field. Obstacle avoidance is a motion planning problem. In a previous study, we investigated the real-time generation of potential field. Based on parallel processing with element group, we proposed the system by Sensory Point Moving (SPM) method. As a result of computer simulation, it was confirmed that the SPM method is effective for generating an obstacle avoidance path in 2-D and a more complex working environment like a 3-D one. In this paper, we discuss the development of autonomous mobile robot for obstacle avoidance based on the SPM method.

Motion planning approach for car-like robots in unstructured scenario

2021 ◽  
pp. 014233122199439
Author(s):  
Xuehao Sun ◽  
Shuchao Deng ◽  
Tingting Zhao ◽  
Baohong Tong
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Optimal Path ◽  
Conversion Function ◽  
Elastic Band ◽  
Emergency Situations ◽  
Time Motion ◽  
Planning Approach

When a car-like robot travels in an unstructured scenario, real-time motion planning encounters the problem of unstable motion state in obstacle avoidance planning. This paper presents a hybrid motion planning approach based on the timed-elastic-band (TEB) approach and artificial potential field. Different potential fields in an unstructured scenario are established, and the real-time velocity of the car-like robot is planned by using the conversion function of the virtual potential energy of the superimposed potential field and the virtual kinetic energy of the robot. The optimized TEB approach plans the local optimal path and solves the problems related to the local minimum region and non-reachable targets. The safety area of the dynamic obstacle is constructed to realize turning or emergency stop obstacle avoidance, thereby effectively ensuring the safety of the car-like robot in emergency situations. The simulation experiments show that the proposed approach has superior kinematic characteristics and satisfactory obstacle avoidance planning effects and can improve the motion comfort and safety of the car-like robot. In the practical test, the car-like robot moves stably in a dynamic scenario, and the proposed approach satisfies the actual application requirements.

scholarly journals Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the Driver’s Operation Characteristics

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4821
Author(s):  
Qinyu Sun ◽  
Yingshi Guo ◽  
Rui Fu ◽  
Chang Wang ◽  
Wei Yuan
Keyword(s):  
Obstacle Avoidance ◽  
Trajectory Planning ◽  
Trajectory Tracking ◽  
Potential Field ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Planning Model ◽  
Repulsive Potential ◽  
Tracking Model ◽  
Operation Characteristics

Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers’ obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver’s obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers’ operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs.

scholarly journals Real-Time Obstacle Avoidance Using Potential Field for a Nonholonomic Vehicle

10.5772/9508 ◽  
2010 ◽  
Author(s):  
Hiroaki Seki ◽  
Yoshitsugu Kamiya ◽  
Masatoshi Hikizu
Keyword(s):  
Real Time ◽  
Obstacle Avoidance ◽  
Potential Field
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