Path planning of humanoids based on artificial potential field method in unknown environments

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.

show abstract

“…The traditional APF algorithm has the following problems when it is used to plan the local obstacle avoidance trajectory of vehicles [ 39 ].…”

Section: Obstacle Avoidance Trajectory Planning Modelmentioning

confidence: 99%

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

Sun

Guo

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Rao et al presented grey wolf searching technique for navigation of autonomous mobile robots in various environments with less time, energy, and collision‐free characteristic . Kumar et al proposed various intelligent approaches for smooth and hassle‐free navigation of humanoid robots in complex environments …”

Section: Introductionmentioning

confidence: 99%

Implementation of grey wolf optimization controller for multiple humanoid navigation

Muni

Parhi

Kumar

2020

Computer Animation & Virtual

Self Cite

View full text Add to dashboard Cite

In this paper, grey wolf optimization controller (GWOC) is considered as a multiobjective technique for multiple humanoid navigations. Upon activation of GWOC, the humanoids mimic the group hunting behavior of grey wolves and navigate toward the target in a collision-free manner in presence of both static and dynamic hurdles. The wolves in the pack will either diverge for searching prey or converge together for attacking the prey following the best search agent (Leader Alpha). GWOC has the ability to keep the humanoid free from being trapped in local minima whereas it facilitates it to head toward global minima. GWOC provides better results as compared to other intelligent techniques because of its five characteristics that include safe boundary, protection, following, hunting, and caring. Both simulation and experimental navigation in laboratory conditions for single as well as for multiple humanoid NAOs have been carried out. From the results of simulation and experimental data, it is confirmed that GWOC provides global minima for humanoid robots in complex environments with different shaped obstacles. A Petri-net controller is considered while navigating multiple humanoids, as during multiple humanoid navigations, one humanoid robot acts as a dynamic obstacle to other humanoids. K E Y W O R D SGWOC, NAO humanoid, navigation, petri-net model INTRODUCTIONLot of research works have been performed for path navigation of mobile robots, biped robots, and industrial robots by using both classical and artificial intelligent techniques. But there is still some improvement needed to avoid the trapping of robots in local minima. Today, the main challenging task for researchers is the path navigation of humanoids having multidegrees of freedom by using computational methods. Though several researchers are working on it, still it is in virtue to obtain the best intelligent method for effective and efficient navigation. For this purpose, grey wolf optimization controller (GWOC) is considered. The humanoid NAO considered for this research work is developed by ALDEBARAN robotics France, which is having 25 degrees of freedom. 1 There are sensors for detecting the obstacles, cameras to take the images of hurdles, motors, actuators, and batteries to perform the predefined tasks. The obstacle distances and shape of the obstacles are detected by inbuilt ultrasonic sensors and camera mount on the head. The programmed GWOC activates when NAO finds any obstacle in its path while heading toward target. Several researchers have worked toward the navigation of different kinds of robots using many intelligent techniques as path navigation of robots plays a Comp Anim Virtual Worlds. 2020;31:e1919. wileyonlinelibrary.com/journal/cav

show abstract

“…The common methods in the trajectory planning include genetic algorithm [9][10], simulated annealing [11][12], artificial neural network [13][14], A � algorithm [15], vector field method [16], adaptive algorithm [17][18], particle swarm optimization algorithm [19][20], artificial potential field method [21][22], etc. Among these algorithms, the artificial potential field method has a simple structure, is convenient for real-time control on hardware entities, and can usually plan smoother and safer paths.…”

Section: Introductionmentioning

confidence: 99%

Research on motion planning for an indoor spray arm based on an improved potential field method

et al. 2020

View full text Add to dashboard Cite

The target spraying effect of spray robots mainly depends on the control performance of the spraying arm during the processes of aiming and tracking. To further improve the robustness of the endpoint control and positioning accuracy of the spray arm, an improved potential field algorithm for the motion planning and control of the spray arm is proposed based on prophase research. The algorithm introduces a velocity potential field, visual field constraints and joint position limit constraints into the traditional artificial potential field method. The velocity potential field is used to ensure that the target state of the spraying arm is at the same velocity as the target crop (relative velocity) to achieve stable target tracking. The visual field constraints and joint position limit constraints are utilized to ensure the efficiency of the visual servo control and the movement of the spray arm. The algorithm can plan a feasible trajectory for the spraying arm in Cartesian space and image space, and use the speed controller to control the spraying arm movement along the trajectory for aiming and tracking. Simulation analysis shows that the algorithm can plan better motion trajectories than the servo controller based on image moments in previous studies. In addition, the experimental results show that the algorithm can effectively improve the robustness of targeting and tracking control for the spray robot.

show abstract

Path planning of humanoids based on artificial potential field method in unknown environments

Cited by 36 publications

References 45 publications

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

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

Implementation of grey wolf optimization controller for multiple humanoid navigation

Research on motion planning for an indoor spray arm based on an improved potential field method

Contact Info

Product

Resources

About