Novel hybrid optimization algorithm using PSO and MADS for the trajectory estimation of a four track wheel skid-steered mobile robot

Ha, Xuan Vinh; Ha, Cheolkeun; Lee, Jewon

doi:10.1080/01691864.2013.839089

Cited by 8 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PSO use assists in calculation reduction and the maintenance of more constant convergence characteristics. To achieve an accurate trajectory and avoid being stuck in local optima PSO and MADS algorithms were integrated by Xuan et al [38]. (Mesh Adaptive Direct Search).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A review: On bio-inspired optimization methods for path planning of mobile robot

Hason

Al-Darraji

2023

IJICI

View full text Add to dashboard Cite

In recent years, researchers have paid attention to algorithms inspired by nature where these algorithms have proven their efficiency in solving many optimization problems, especially in complex situations, due to their high precision, speed of optimization, simplicity of the techniques, and efficiency in agent cooperation. The primary issue in the field of autonomous mobile robots is navigation. An autonomous robot's navigation ability is one of its most crucial and distinctive features. There are four components of the autonomous robot navigation issue: vision, localization, cognition, and path planning. Many academics have used bio-inspired methods to solve navigation difficulties in mobile robots in recent years, including path planning where they considered the path planning problem as an optimization problem. Many novel path-planning methods have been created, and those using bio-inspired algorithms have received much attention. These algorithms have been shown to be useful in solving complex problems where the solution space isn't always adequately characterized and the problem necessitates solving complex mathematical models of live processes. More intricate optimization methods that transcend the constraints of classical procedures must be applied as the complexities of the optimization problem increase. This work contributes to presenting a group of algorithms inspired by nature that has been used to solve the problem of planning the path of mobile robots, and then making a comparison between these algorithms based on three factors (cost, time, and path length). Choosing an appropriate path-planning method contributes to ensuring safe and efficacious navigation from one point to another.

show abstract

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A review: On bio-inspired optimization methods for path planning of mobile robot

Hason

Al-Darraji

2023

IJICI

View full text Add to dashboard Cite

show abstract

“…Fig. (5)(6)(7)(8) depicts the simulation results of scenario 3parallel-1-obstacle. For the convenience of discussion, the legend robot 𝑖 in all figures is referred to as 𝑣 , and 𝑣 has a higher priority than 𝑣 if 𝑖 < 𝑗.…”

Section: Formulation Of the Robot Collision Avoidance Constraints As Qcmentioning

confidence: 99%

“…The problem has been well studied for one robot avoiding static or moving obstacles. The methods such as cell decomposition [4], Roadmap Approach [5], Artificial Potential Field Approach [6], Genetic Algorithm [7] and Particle Swarm Optimization [8] have been widely used in this problem. However, finding collision-free paths in a complex environment with other robots or pedestrian still remains challenges, because it should predict other agents motion as well as satisfy its kinematics or dynamics constraints simultaneously, which needs to be computationally tractable for real-time implementation.…”

Section: Introductionmentioning

confidence: 99%

Distributed Non-Convex Model Predictive Control for Non-Cooperative Collision Avoidance of Networked Differential Drive Mobile Robots

Mao

Dai

2022

IEEE Access

View full text Add to dashboard Cite

This paper proposes a novel Non-Cooperative Distributed Model Predictive Control (NC-DMPC) strategy for collision avoidance of networked multiple Differential Drive Mobile Robots (multi-DDMRs). This strategy is based on Priority-Based NC-DMPC (PB-NC-DMPC). To avoid the loss of the prediction consistency caused by PB-NC-DMPC, a concept of urgency is introduced to DDMRs in the Networked Control Systems (NCS), and a corresponding priority queue is obtained by sorting these urgency values. Renumbering these priorities in descending order converts the partial order priorities into a topological order, which is a valid sequence for the DDMRs to solve their optimization problems. At last, the Sequential Convex Programming (SCP) is presented to solve the non-convex optimization problem resulting from the collision avoidance constraints. As a special application, the proposed strategy solves the problem of collision avoidance of Multi-DDMRs, compared to the original PB-NC-DMPC, the proposed strategy offer a more effective and safe performance in complex applications.

show abstract

“…The fitness function is optimized by using the particle swarm optimization (PSO) algorithm. To obtain a precise trajectory and prevent becoming stuck in local optima, the PSO algorithm combined with the MADS (mesh adaptive direct search) algorithm was used by Xuan et al [91]. When combined with the GA and EKF algorithms, the PSO-MADS algorithm produces an effective result (the extended Kalman filter).…”

mentioning

confidence: 99%

Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

Abdulsaheb

Kadhim

2023

Robotics

View full text Add to dashboard Cite

The most important research area in robotics is navigation algorithms. Robot path planning (RPP) is the process of choosing the best route for a mobile robot to take before it moves. Finding an ideal or nearly ideal path is referred to as “path planning optimization.” Finding the best solution values that satisfy a single or a number of objectives, such as the shortest, smoothest, and safest path, is the goal. The objective of this study is to present an overview of navigation strategies for mobile robots that utilize three classical approaches, namely: the roadmap approach (RM), cell decomposition (CD), and artificial potential fields (APF), in addition to eleven heuristic approaches, including the genetic algorithm (GA), ant colony optimization (ACO), artificial bee colony (ABC), gray wolf optimization (GWO), shuffled frog-leaping algorithm (SFLA), whale optimization algorithm (WOA), bacterial foraging optimization (BFO), firefly (FF) algorithm, cuckoo search (CS), and bat algorithm (BA), which may be used in various environmental situations. Multiple issues, including dynamic goals, static and dynamic environments, multiple robots, real-time simulation, kinematic analysis, and hybrid algorithms, are addressed in a different set of articles presented in this study. A discussion, as well as thorough tables and charts, will be presented at the end of this work to help readers understand what types of strategies for path planning are developed for use in a wide range of ecological contexts. Therefore, this work’s main contribution is that it provides a broad view of robot path planning, which will make it easier for scientists to study the topic in the near future.

show abstract

Novel hybrid optimization algorithm using PSO and MADS for the trajectory estimation of a four track wheel skid-steered mobile robot

Cited by 8 publications

References 16 publications

A review: On bio-inspired optimization methods for path planning of mobile robot

A review: On bio-inspired optimization methods for path planning of mobile robot

Distributed Non-Convex Model Predictive Control for Non-Cooperative Collision Avoidance of Networked Differential Drive Mobile Robots

Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

Contact Info

Product

Resources

About