Optimal Robot Path Research Articles

Reinforcement Learning in Robot Path Optimization

Along with the development of robot technology, a robot not only need to complete a specific task, but aslo need to do path planning in the process of performing the task. So, path planning is widly studied. This paper introduce a method of robot path planning based on reinforcement learning,which aimed at Markovian decision process. In this paper, we introduce the basic concept, principle and the method of reinforcement learning and some other algorithms.Then,we do research from single robot’s path planning in the static invironment based on Q-learning, and describe the application of this algorithm on the path planning by setting off state space and action space reasonablly and designing reinforcement function.By edditting Matlab program,we do some simulation experiments,which incarnate the algorithm visually and get the optimal path.

Optimal path planning for surveillance with temporal-logic constraints*

In this paper we present a method for automatically generating optimal robot paths satisfying high-level mission specifications. The motion of the robot in the environment is modeled as a weighted transition system. The mission is specified by an arbitrary linear temporal-logic (LTL) formula over propositions satisfied at the regions of a partitioned environment. The mission specification contains an optimizing proposition, which must be repeatedly satisfied. The cost function that we seek to minimize is the maximum time between satisfying instances of the optimizing proposition. For every environment model, and for every formula, our method computes a robot path that minimizes the cost function. The problem is motivated by applications in robotic monitoring and data-gathering. In this setting, the optimizing proposition is satisfied at all locations where data can be uploaded, and the LTL formula specifies a complex data-collection mission. Our method utilizes Büchi automata to produce an automaton (which can be thought of as a graph) whose runs satisfy the temporal-logic specification. We then present a graph algorithm that computes a run corresponding to the optimal robot path. We present an implementation for a robot performing data collection in a road-network platform.

Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation

In this paper, a novel knowledge based genetic algorithm (GA) for path planning of multiple robots for multiple targets seeking behaviour in presence of obstacles is proposed. GA technique has been incorporated in Petri-Net model to make an integrated navigational controller. The proposed algorithm is based upon an iterative non-linear search, which utilises matches between observed geometry of the environment and a priori map of position locations, to estimate a suitable heading angle, there by correcting the position and orientation of the robots to find targets. This knowledge based GA is capable of finding an optimal or near optimal robot path in complex environments. The Petri-GA model can handle inter robot collision avoidance more effectively than the stand alone GA. The resulting navigation algorithm has been implemented on real mobile robots and tested in various environments to validate the developed control scheme.

The Application of an Improved Ant Colony Algorithm in Mobile Robot Path Planning

This paper presents an improved ant colony algorithm to plan an optimal collision-free path for mobile robot in complicated static environment. Based on the work space model with grid method, simulated foraging behavior of ants and to serve the mobile robot path planning, update the conventional ant colony algorithm with some special functions. To avoid mobile robot path deadlock, a dead-corner table is established and the penalty function is used to update the trail intensity when an ant explores a dead—corner in the path searching. The simulation results show that the algorithm can improve performance of path planning obviously, and the algorithm is simple and effective.

Optimal Robot Path Planning with Cellular Neural Network

This paper presents a new methodology based on neural dynamics for optimal robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. The target activity is treated as an energy source injected into the neural system and is propagated through the local connectivity of cells in the state space by neural dynamics. By formulating the local connectivity of cells as the local interaction of harmonic functions, an improved CNN model is established to propagate the target activity within the state space in the manner of physical heat conduction, which guarantees that the target and obstacles remain at the peak and the bottom of the activity landscape of the neural network. The proposed methodology cannot only generate real-time, smooth, optimal, and collision-free paths without any prior knowledge of the dynamic environment, but it can also easily respond to the real-time changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.

Optimization of Industrial, Vision-Based, Intuitively Generated Robot Point-Allocating Tasks Using Genetic Algorithms

Current industrial robot-programming methods require, depending on the task to be developed, an elevated degree of technical ability and time from a human operator, in order to obtain a precise, nonoptimal result. This correspondence paper presents a methodology used to generate an optimal sequence of robot configurations that enable a precise point-allocating task applicable, for instance, to spot-welding, drilling, or electronic component placement maneuvers. The optimization process starts from a nonoptimal, initial sequence designated intuitively by a human operator using an easy-to-use interface. In this correspondence paper, intuitive programming is considered as the process of defining, in a computer graphics environment and with a limited user knowledge of robotics or the industrial task, the sequence of motions that enable the execution of a complex industrial robotic maneuver. Such an initial sequence is later followed by a robot, very precisely, using a vision-based, calibration-free, robot control method. Further robot path optimization is performed with a genetic algorithm approach. An industrial robot, which is part of the experimental setup, was used in order to validate the proposed procedure.

Optimal Robot Path for Minimizing Thermal Variations in a Spray Deposition Process

This paper describes a method for determining the optimal robot path that minimizes thermal variations over a surface during a spray deposition process, where the deposited material is hotter than the surface. An analytical expression is formed for the amplitude of the thermal modes of the surface temperature profile. This expression is then used to determine the optimal robot velocity, scan angle, and start position. Experimental results from a metal spray deposition process are used to confirm the analysis

The Usage of Neural Networks for Navigation Tasks Solving

This article deals with the problem of choice the optimal path for autonomous wheel transport robot as a part of control system projection for technical objects. This method makes possible to remove the restrictions on the movement and to choice the optimal path taking into account real dimensions of robot It makes possible to Find a decision in true time.