Huang Yanwen

Bio: Huang Yanwen is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Social robot & Mobile robot. The author has an hindex of 1, co-authored 2 publications receiving 65 citations.

An Evolutionary Artificial Potential Field Algorithm for Dynamic Path Planning of Mobile Robot

Abstract: The artificial potential field (APF) method is widely used for autonomous mobile robot path planning due to its simplicity and mathematical elegance. However, most researches are focused on solving the path-planning problem in a stationary environment, where both targets and obstacles are stationary. This paper proposes a new APF method for path planning of mobile robots in a dynamic environment where the target and obstacles are moving. Firstly, the new force function and the relative threat coefficient function are defined. Then, a new APF path-planning algorithm based on the relative threat coefficient is presented. Finally, computer simulation and experiment are used to demonstrate the effectiveness of the dynamic path-planning scheme.

Multiagent cooperation based entertainment robot

Abstract: In recent years, cooperation of heterogeneous multiagents (HMAs) has achieved formidable results and gained an increasing attention by researchers. This paper presents an entertainment robot system based on the HMAs' cooperation. This robot is used to distinguish the basic information of a person from the audience and entertain him in the exhibition hall. The main agents include the Arbitration System (AS), the Face Recognition System (FRS), the Position Cognition System (PCS), and the Behavior Planner (BP), which are carved up according to their respective functions. Each agent completes its own task independently and the robot finishes the whole mission through their cooperation. The hybrid control architecture and the data-fusion algorithm based on Bayesian Belief Network are also discussed in this paper.

Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles

Abstract: The BPF proposal ensures a feasible, optimal and safe path for robot navigation.The results of BPF overcomes APF and other EAPF methods like those based in GAs.The BPF is quite faster in optimization leading to reduction in computation burden.The BPF running in parallel mode is the most suitable to fulfill local and global controllability.The BPF is capable to work in offline and online mode with static and dynamic obstacles. In this paper, optimal paths in environments with static and dynamic obstacles for a mobile robot (MR) are computed using a new method for path planning. The proposed method called Bacterial Potential Field (BPF) ensures a feasible, optimal and safe path. This novel proposal makes use of the Artificial Potential Field (APF) method with a Bacterial Evolutionary Algorithm (BEA) to obtain an enhanced flexible path planner method taking all the advantages of using the APF method, strongly reducing its disadvantages. Comparative experiments for sequential and parallel implementations of the BPF method against the classic APF method, as well as with the Pseudo-Bacterial Potential Field (PBPF) method, and with the Genetic Potential Field (GPF) method, all of them based on evolutionary computation to optimize the APF parameters, were achieved. A simulation platform that uses an MR realistic model was designed to test the path planning algorithms. In general terms, it was demonstrated that the BPF outperforms the APF, GPF, and the PBPF methods by reducing the computational time to find the optimal path at least by a factor of 1.59. These results have a positive impact in the ability of the BPF path planning method to satisfy local and global controllability in dynamic complex environments, avoiding collisions with objects that will interfere the navigation of the MR.

Path Planning for the Mobile Robot: A Review

Abstract: Good path planning technology of mobile robot can not only save a lot of time, but also reduce the wear and capital investment of mobile robot. Several methodologies have been proposed and reported in the literature for the path planning of mobile robot. Although these methodologies do not guarantee an optimal solution, they have been successfully applied in their works. The purpose of this paper is to review the modeling, optimization criteria and solution algorithms for the path planning of mobile robot. The survey shows GA (genetic algorithm), PSO (particle swarm optimization algorithm), APF (artificial potential field), and ACO (ant colony optimization algorithm) are the most used approaches to solve the path planning of mobile robot. Finally, future research is discussed which could provide reference for the path planning of mobile robot.

Optimal Path Planning Generation for Mobile Robots using Parallel Evolutionary Artificial Potential Field

Abstract: In this paper, we introduce the concept of Parallel Evolutionary Artificial Potential Field (PEAPF) as a new method for path planning in mobile robot navigation. The main contribution of this proposal is that it makes possible controllability in complex real-world sceneries with dynamic obstacles if a reachable configuration set exists. The PEAPF outperforms the Evolutionary Artificial Potential Field (EAPF) proposal, which can also obtain optimal solutions but its processing times might be prohibitive in complex real-world situations. Contrary to the original Artificial Potential Field (APF) method, which cannot guarantee controllability in dynamic environments, this innovative proposal integrates the original APF, evolutionary computation and parallel computation for taking advantages of novel processors architectures, to obtain a flexible path planning navigation method that takes all the advantages of using the APF and the EAPF, strongly reducing their disadvantages. We show comparative experiments of the PEAPF against the APF and the EAPF original methods. The results demonstrate that this proposal overcomes both methods of implementation; making the PEAPF suitable to be used in real-time applications.

Robot path planning in dynamic environments using a simulated annealing based approach

Abstract: This paper proposes a simulated annealing based approach to determine the optimal or near-optimal path quickly for a mobile robot in dynamic environments with static and dynamic obstacles. The approach uses vertices of the obstacles to define the search space. It processes off-line computation based on known static obstacles, and re-computes the route online if a moving obstacle is detected. The contributions of the work include the employment of the simulated annealing algorithm for robot path planning in dynamic environments, and the development of a new algorithm planner for enhancement of the efficiency of the path planning algorithm. The effectiveness of the proposed approach is demonstrated through simulations under typical dynamic environments and comparisons with existing methods.