Motion planning is a fundamental research area in robotics. Sampling-based methods offer an efficient solution for what is otherwise a rather challenging dilemma of path planning. Consequently, these methods have been extended further away from basic robot planning into further difficult scenarios and diverse applications. A comprehensive survey of the growing body of work in sampling-based planning is given here. Simulations are executed to evaluate some of the proposed planners and highlight some of the implementation details that are often left unspecified. An emphasis is placed on contemporary research directions in this field. We address planners that tackle current issues in robotics. For instance, real-life kinodynamic planning, optimal planning, replanning in dynamic environments, and planning under uncertainty are discussed. The aim of this paper is to survey the state of the art in motion planning and to assess selected planners, examine implementation details and above all shed a light on the current challenges in motion planning and the promising approaches that will potentially overcome those problems.

Sampling-Based Robot Motion Planning: A Review

Heuristic approaches in robot path planning

A review: On path planning strategies for navigation of mobile robot

Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great examples of mobile robots. They can be distinguished from other robots by their ability to move autonomously, with ...

https://journals.sagepub.com/doi/pdf/10.1177/1729881419839596

A review of mobile robots: Concepts, methods, theoretical framework, and applications

Dynamic path planning of mobile robots with improved genetic algorithm

In this paper, a knowledge based genetic algorithm (GA) for path planning of a mobile robot is proposed, which uses problem-specific genetic algorithms for robot path planning instead of the standard GAs. The proposed knowledge based genetic algorithm incorporates the domain knowledge into its specialized operators, where some also combine a local search technique. The proposed genetic algorithm also features a unique and simple path representation and a simple but effective evaluation method. The knowledge based genetic algorithm is capable of finding an optimal or near-optimal robot path in both complex static and dynamic environments. The effectiveness and efficiency of the proposed genetic algorithm is demonstrated by simulation studies. The irreplaceable role of the specialized genetic operators in the proposed GA for solving robot path planning problem is demonstrated by a comparison study.

A knowledge based genetic algorithm for path planning of a mobile robot

This paper proposes a knowledge based genetic algorithm (GA) for path planning of a mobile robot in unstructured environments. The algorithm uses a unique problem representation method to represent 2-dimensional robot environments with complex obstacle layouts of arbitrary obstacle shapes. An effective evaluation method is specially developed for the proposed genetic algorithm. The evaluation method is able to accurately detect collisions between a robot path and an arbitrarily shaped obstacle, and assigns costs that are very effective for the proposed genetic algorithm. The proposed GA uses problem-specific genetic algorithms for robot path planning instead of the standard GAs. The proposed knowledge based genetic algorithm incorporates the domain knowledge of robot path planning into its specialized operators, where some also combine a local search technique. The effectiveness and efficiency of the proposed genetic algorithm is demonstrated by simulation studies. The irreplaceable role of the specialized genetic operators for solving robot path-planning problem is shown by a comparison study

A Knowledge Based Genetic Algorithm for Path Planning in Unstructured Mobile Robot Environments

In this paper, a knowledge based genetic algorithm (GA) for on-line path planning of multiple mobile robots in dynamic environments is proposed. The proposed GA uses a unique problem representation method to represent 2-D robot environments with complex obstacle layouts and obstacles are allowed to be of arbitrary shapes. Correspondingly, an effective evaluation method is developed specially for the proposed GA. The proposed evaluation method is capable of accurately detecting collisions among robot paths and arbitrarily shaped obstacles, and assigns costs that are effective for the proposed GA. Problem-specific GA instead of the standard GAs are used for robot path planning. The proposed knowledge based GA incorporates the domain knowledge into its specialized operators, some of which also combine a local search technique. The effectiveness and efficiency of the proposed genetic algorithm is demonstrated by simulation studies.

A Knowledge Based GA for Path Planning of Multiple Mobile Robots in Dynamic Environments

Abstruct- In this paper, a knowledge based genetic algorithm (GA) for path planning of a mobile robot is proposed, which uses problem-specific genetic algorithms for robot path planning instead of the standard GAS. The proposed knowledge based genetic algorithm incorporates the domain knowledge into its specialized operators, where some also combine a local search technique. The proposed genetic algorithm also features a unique and simple path representation and a simple but effective evaluation method. The knowledge based genetic algorithm is capable of finding an optimal or near-optimal robot path in both complex static and dynamic environments. The effectiveness and efficiency of the proposed genetic algorithm is demonstrated by simulation studies. The irreplaceable role of the specialized genetic operators in the proposed GA for solving robot pathplanning problem is demonstrated by a comparison study.

A Knowledge Based Genetic A1gorith:m for Path Planning of a Mobile Robot.

In this paper, a fuzzy neural dynamics based tracking controller for nonholonomic wheeled mobile robots is proposed The nonholonomic kinematic constraints are considered in the development of the controller The proposed model is suitable for both continuous and discrete paths Fuzzy rules are formulated to deal with the discontinuity in path directions This model is capable of generating smooth velocity commands to drive the robot to track the desired paths In the situation with large initial errors, the proposed model can automatically generate a smooth curve to reach the desired robot path from an arbitrary initial configuration without any explicit algorithms for the connection curve At sharp turns, this model can automatically round off the sharp turns with a smooth curve The effectiveness of the proposed tracking controller is demonstrated by simulation studies

Yanrong Hu

Papers

A knowledge based genetic algorithm for path planning of a mobile robot

A Knowledge Based Genetic Algorithm for Path Planning in Unstructured Mobile Robot Environments

A Knowledge Based GA for Path Planning of Multiple Mobile Robots in Dynamic Environments

A Knowledge Based Genetic A1gorith:m for Path Planning of a Mobile Robot.

A fuzzy neural dynamics based tracking controller for a nonholonomic mobile robot