Computationally Efficient Path Planning for Wheeled Mobile Robots in Obstacle Dense Environments

2007 ◽  
pp. 259-268
Author(s):  
Husnu Türker Sa̧hin ◽  
Erkan Zergeroğlu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Computationally Efficient ◽  
Wheeled Mobile Robots

Cooperative Control of Autonomous Mobile Robots in Unknown Terrain

2006 ◽  
Author(s):  
Laura E. Ray ◽  
Devin Brande ◽  
John Murphy ◽  
James Joslin
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Cooperative Control ◽  
High Speed ◽  
Control Method ◽  
Performance Characteristics ◽  
Computationally Efficient ◽  
Wheeled Mobile Robots ◽  
Planning And Control ◽  
Local Slip

This paper presents a distributed control framework for groups of wheeled mobile robots with significant (non-negligible) vehicle dynamics driving on terrain with variable performance characteristics. A dynamic model of a high-speed robot is developed with attention to representation of wheel-terrain performance characteristics. Using this model, aspects of distributed, cooperative control on unknown terrain are investigated. A potential function path planning and cooperative control algorithm is combined with a local slip controller on each robot to provide high-speed control of vehicle formation. Local slip control is shown to reduce sensitivity of the distributed path planning and control method to tire-terrain performance variation and its resulting effect on dynamic behavior of the robots. Computationally efficient methods for real-time assessment of force-slip characteristics are presented to provide slip setpoints for this control architecture.

scholarly journals Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots

2020 ◽  
Vol 15 (3) ◽  
Author(s):  
Radu-Emil Precup ◽  
Emil-Ioan Voisan ◽  
Emil M. Petriu ◽  
Marius L. Tomescu ◽  
Radu-Codrut David ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Tracking Control ◽  
Fuzzy Controller ◽  
Optimization Problems ◽  
Cost Effective ◽  
Grey Wolf Optimizer ◽  
Reference Trajectory ◽  
Grey Wolf ◽  
Wheeled Mobile Robots

This paper proposes two applications of Grey Wolf Optimizer (GWO) algorithms to a path planning (PaPl) problem and a Proportional-Integral (PI)-fuzzy controller tuning problem. Both optimization problems solved by GWO algorithms are explained in detail. An off-line GWO-based PaPl approach for Nonholonomic Wheeled Mobile Robots (NWMRs) in static environments is proposed. Once the PaPl problem is solved resulting in the reference trajectory of the robots, the paper also suggests a GWO-based approach to tune cost-effective PI-fuzzy controllers in tracking control problem for NWMRs. The experimental results are demonstrated through simple multiagent settings conducted on the nRobotic platform developed at the Politehnica University of Timisoara, Romania, and they prove both the effectiveness of the two GWO-based approaches and major performance improvement.

Path Planning and Path Tracking of Industrial Mobile Robots

2010 ◽  
pp. 84-124
Author(s):  
Sašo Blažic ◽  
El-Hadi Guechi ◽  
Jimmy Lauber ◽  
Michel Dambrine ◽  
Gregor Klancar
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Global Stability ◽  
Main Part ◽  
Path Tracking ◽  
Trajectory Design ◽  
Wheeled Mobile Robots ◽  
Physical Limitations ◽  
Takagi Sugeno ◽  
Reference Trajectories

The purpose of this chapter is to give a quick state of the art and to propose some new approaches in the area of path planning and path tracking for the differentially driven wheeled mobile robots. The main part of the chapter is devoted to the methods that ensure stable tracking of the prescribed reference trajectories. Of particular importance are the approaches that result in global stability of the tracking, e.g. Lyapunov-based control and parallel distributed Takagi-Sugeno fuzzy control. The effects of discrete measurements and delay on the control performance are also analysed. The second part of the chapter is devoted to path planning or trajectory design. Here, physical limitations of the robot and obstacle avoidance are treated.

Probability-Based Optimal Path Planning for Two-Wheeled Mobile Robots

2015 ◽  
Author(s):  
Jaeyeon Lee ◽  
Wooram Park
Keyword(s):  
Probability Density Function ◽  
Path Planning ◽  
Mobile Robots ◽  
Probability Density ◽  
Density Function ◽  
Deterministic Model ◽  
Optimal Path ◽  
Planck Equation ◽  
Practical Implementation ◽  
Wheeled Mobile Robots

Most dynamic systems show uncertainty in their behavior. Therefore, a deterministic model is not sufficient to predict the stochastic behavior of such systems. Alternatively, a stochastic model can be used for better analysis and simulation. By numerically integrating the stochastic differential equation or solving the Fokker-Planck equation, we can obtain a probability density function of the motion of the system. Based on this probability density function, the path-of-probability (POP) method for path planning has been developed and verified in simulation. However, there are rooms for more improvements and its practical implementation has not been performed yet. This paper concerns formulation, simulation and practical implementation of the path-of-probability for two-wheeled mobile robots. In this framework, we define a new cost function which measures the averaged targeting error using root-mean-square (RMS), and iteratively minimize it to find an optimal path with the lowest targeting error. The proposed algorithm is implemented and tested with a two-wheeled mobile robot for performance verification.

scholarly journals A G3-Continuous Extend Procedure for Path Planning of Mobile Robots with Limited Motion Curvature and State Constraints

2018 ◽  
Vol 8 (11) ◽  
pp. 2127 ◽  
Author(s):  
Tomasz Gawron ◽  
Maciej Michałek
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Computational Cost ◽  
Autonomous Driving ◽  
Random Trees ◽  
Planning Problem ◽  
Computationally Efficient ◽  
Convex Polygons ◽  
Robot Configuration ◽  
Arbitrary Precision

Provably correct and computationally efficient path planning in the presence of various constraints is essential for autonomous driving and agile maneuvering of mobile robots. In this paper, we consider the planning of G 3-continuous planar paths with continuous and limited curvature in a motion environment that is bounded and contains obstacles modeled by a set of (non-convex) polygons. In practice, the curvature constraints often arise from mechanical limitations for the robot, such as limited steering and articulation angles in wheeled robots, or aerodynamic constraints in unmanned aerial vehicles. To solve the planning problem under those stringent constraints, we improve upon known path primitives, such as Reeds–Shepp (RS) and CC-steer (curvature-continuous) paths. Given the initial and final robot configuration, we developed extend-procedure computing paths that can approximate RS paths with arbitrary precision, but guaranteeing G 3-continuity. We show that satisfaction of all stated path constraints is guaranteed and, contrary to many other methods known from the literature, the method of checking for collisions between the planned path and obstacles is given by a closed-form analytic expression. Furthermore, we demonstrate that our approach is not conservative, i.e., it allows for precise maneuvers in tight environments under the assumption of a rectangular robot footprint. The presented extend procedure can be integrated into various motion-planning algorithms available in the literature. In particular, we utilized the Rapidly exploring Random Trees (RRT*) algorithm in conjunction with our extend procedure to demonstrate its feasibility in motion environments of nontrivial complexity and low computational cost in comparison to a G 3-continuous extend procedure based on η 3-splines.

scholarly journals G3-splines for the path planning of wheeled mobile robots

2003 ◽  
Author(s):  
Aurelio Piazzi ◽  
Massimo Romano ◽  
Corrado Guarino Lo Bianco
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Wheeled Mobile Robots

scholarly journals A new optimization-driven path planning method with probabilistic completeness for wheeled mobile robots

2019 ◽  
Vol 52 (5-6) ◽  
pp. 317-325 ◽  
Author(s):  
Bo You ◽  
Zhi Li ◽  
Liang Ding ◽  
Haibo Gao ◽  
Jiazhong Xu
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Mobile Robots ◽  
Energy Efficient ◽  
Planning Process ◽  
Planning Method ◽  
Projection Algorithm ◽  
Wheeled Mobile Robots ◽  
Active Set ◽  
Weak Minima

Wheeled mobile robots are widely utilized for environment-exploring tasks both on earth and in space. As a basis for global path planning tasks for wheeled mobile robots, in this study we propose a method for establishing an energy-based cost map. Then, we utilize an improved dual covariant Hamiltonian optimization for motion planning method, to perform point-to-region path planning in energy-based maps. The method is capable of efficiently handling high-dimensional path planning tasks with non-convex cost functions through applying a robust active set algorithm, that is, non-monotone gradient projection algorithm. To solve the problem that the path planning process is locked in weak minima or non-convergence, we propose a randomized variant of the improved dual covariant Hamiltonian optimization for motion planning based on simulated annealing and Hamiltonian Monte Carlo methods. The results of simulations demonstrate that the final paths generated can be time efficient, energy efficient and smooth. And the probabilistic completeness of the method is guaranteed.

Sign in / Sign up

Export Citation Format

Share Document