Problem Of Mobile Robot Navigation Research Articles

A GENETIC NAVIGATION ALGORITHM FOR AUTONOMOUS MOBILE ROBOTS

The use of genetic algorithms GAs for solving the navigation problem of mobile robots is explored in this paper. Without using any logical model of the robot's world, the proposed approach evolves feasible robot paths by performing an adaptive search on populations of candidate robot actions. Unlike standard GAs, the proposed approach uses variable length chromosome structures and a set of special genetic operators. More importantly, the genetic navigation algorithm GNA can adapt its behavior for dealing with different types of static or changing simulated environments. The performance of the algorithm is examined through a plethora of computersimulated robot navigation experiments carried out on various types of planar grid environments. Furthermore, experimental comparisons between the GNA and two other heuristics are provided showing a substantially better performance for the GNA.

Uncertainty representation for mobile robots: Perception, modeling and navigation in unknown environments

We present a fuzzy-sets based approach to the problem of mobile robot navigation in unknown environments. Fuzzy sets are used to represent the uncertainty that is inherent to the perception of the environment through the robot sensors. This uncertainty is then propagated in the process of map building so that not only a plausible spatial layout of the environment, but also the confidence on this layout, is obtained. The initial map built by the robot is then used for self-localization as it continues navigating in the same environment. The new information collected by the sensors is matched to the initial map and the transformation that brings them together is used to correct and bound the dead-reckoning errors. Uncertainty representation is a key aspect of this process since it allows to immediately detect pairs of coincident boundaries, thus, permitting real-time self-localization. The approach is illustrated by experiments in office environments.

Adaptive internal state space construction method for reinforcement learning of a real-world agent

One of the difficulties encountered in the application of the reinforcement learning to real-world problems is the construction of a discrete state space from a continuous sensory input signal. In the absence of a priori knowledge about the task, a straightforward approach to this problem is to discretize the input space into a grid, and to use a lookup table. However, this method suffers from the curse of dimensionality. Some studies use continuous function approximators such as neural networks instead of lookup tables. However, when global basis functions such as sigmoid functions are used, convergence cannot be guaranteed. To overcome this problem, we propose a method in which local basis functions are incrementally assigned depending on the task requirement. Initially, only one basis function is allocated over the entire space. The basis function is divided according to the statistical property of locally weighted temporal difference error (TD error) of the value function. We applied this method to an autonomous robot collision avoidance problem, and evaluated the validity of the algorithm in simulation. The proposed algorithm, which we call adaptive basis division (ABD) algorithm, achieved the task using a smaller number of basis functions than the conventional methods. Moreover, we applied the method to a goal-directed navigation problem of a real mobile robot. The action strategy was learned using a database of sensor data, and it was then used for navigation of a real machine. The robot reached the goal using a smaller number of internal states than with the conventional methods.

Adding Memory Processing Behaviors to the Fuzzy Behaviorist Approach (Fba): Resolving Limit Cycle Problems in Autonomous Mobile Robot Navigation

ABSTRACTMost fuzzy logic-based reasoning schemes developed for robot control are fully reflexive, i.e., the reasoning modules consist of fuzzy rule bases that represent direct mappings from the stimuli provided by the perception systems to the responses implemented by the motion controllers. Due to their totally reflexive nature, such reasoning systems can encounter problems such as infinite loops and limit cycles. In this paper, we propose an approach to remedy these problems by adding a memory and memory-related behaviors to basic reflexive systems. Three major types of memory behaviors are addressed: memory feeding, memory management, and memory utilization. The concepts underlying these major classes of behaviors are first presented. An example of their implementation is then described for the recognition of limit cycles during the sensor-based navigation of mobile robots. Results of sample cases involving an autonomous robot navigating in a priori unknown environments are presented and discussed to i...

Information processing using chaos with application to mobile robot navigation problems

Throughout this study on information processing using an artificial neural network (ANN) and chaos we are attempting to devise a memory model that resembles human behavioral characteristics. For that purpose we construct a framework of the macroscopic model of the responding process in biological systems. Incoming stimuli are applied to the sensory receptors and preprocessed. A pattern-matching block allows one of the chaotic memories to find a feasible response in an associative way. After the chaotic memory is stabilized on one of the stable equilibrium points or limit cycles, its performance is evaluated. Since chaotic memory and the performance evaluation block form a feedback loop, they can handle features of the information blocks and store newly updated information blocks. Two kinds of chaotic memories are established in this paper: one is a 1-D map in which many information blocks can be stored as unstable periodic orbits, and the other is the famous Lozi attractor with rich dynamics. Simulations are performed for the mobile robot navigation problem in each case.

A mobile robot navigation method using a fuzzy logic approach

SummaryThis paper treats, in a general way, the problem of mobile robot navigation in a totally unknown environment. The different aspects of this problem are dealt with one by one. We begin by introducing a simple method for perceiving and analyzing the robot's local environment based on a limited amount of distance information. Using this analysis as our base, we present a navigation algorithm containing different action modules; some of these actions use Fuzzy Logic. The results presented whether experimental or simulation show that our method is well adapted to this type of problem.

A multi-valued Boltzmann machine

The idea of a Hopfield network is based on the Ising spin glass model in which each spin has only two possible states: up and down. By introducing stochastic factors into this network and performing a simulated annealing process on it, it becomes a Boltzmann machine which can escape from local minimum states to achieve the global minimum. This paper generalizes the above ideas to multi-value case based on the XY spin glass model in which each spin can be in any direction in a plane. Simply using the gradient descent method and the analog Hopfield network, two different analog connectionist structures and their corresponding evolving rules are first designed to transform the XY spin glass model to distributed computational models. These two analog computational models are single-layered connectionist structures and multi-layered Hopfield analog networks. The latter network eases the node (neuron) computational requirement of the former at the expense of more neurons and connections. With the proposed evolving rules, the proposed models evolve according to a predefined Hamiltonian (energy function) which will decrease until it reaches a (perhaps local) minimum. Since these two structures can easily get stuck in local minima, a multi-valued Boltzmann machine is proposed which adopts the discrete planar spin glass model for the local minimum problem. Each neuron in the multi-valued Boltzmann machine can only take n discrete directions (states). The stochastic simulated annealing method is introduced to the evolving rules of the multi-valued Boltzmann machine to solve the local minimum problem. The multi-valued Boltzmann machine can be applied to the mobile robot navigation problem by defining proper artificial magnetic field on the traverse terrain. This new artificial magnetic field approach for the mobile robot navigation problem has shown to have several advantages over existing graph search and potential field techniques.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Robot path planning using VLSI resistive grids

The resistive grid algorithm for mobile robot path planning is described. A major advantage of the method is that it is capable of a fine-grained parallel analogue VLSI implementation, which offers a fast, low-power solution to the problem of mobile robot navigation. The results from a small-scale test chip are presented, together with their implications for scaling up to a full-sized path-planning chip.

Non-Holonomic Local Navigation by Means of Time and Speed Dependent Artificial Potential Fields

The study presented here concerns the local navigation problem of mobile robots, using the Artificial Potential Fields method. First, a solution to solve it main basic limitation, the local minima presence in the potential field, is proposed. This is done by a systematic local reshaping that allows to conserve the low complexity and the local aspect of the basic method. In a second time, a theoretical analysis of mobile robot non holonomic constraints leads to introduce a new concept : the “electrostatic like” or speed-dependent potential fields. Those repulsive fields create on the robot a continuous varying centrifugal force that realises the obstacles avoidance and respects non holonomy. Simulation results are very encouraging when we respect the basic assumption that the robot does not have to manoeuvre

Mobile robot localization by tracking geometric beacons

The application of the extended Kaman filter to the problem of mobile robot navigation in a known environment is presented. An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed. The algorithm is based on an extended Kalman filter that utilizes matches between observed geometric beacons and an a priori map of beacon locations. Two implementations of this navigation algorithm, both of which use sonar, are described. The first implementation uses a simple vehicle with point kinematics equipped with a single rotating sonar. The second implementation uses a 'Robuter' mobile robot and six static sonar transducers to provide localization information while the vehicle moves at typical speeds of 30 cm/s. >

Satisficing feedback strategies for local navigation of autonomous mobile robots

A general approach to the local navigation problem for autonomous mobile robots (AMRs) is presented and its application to omnidirectional and conventionally steered wheelbases is described. The problem of driving an AMR to a goal in an unknown environment is formulated as a dynamic feedback control problem in which local feedback information is used to make steering decisions while the AMR is moving. To obtain a computationally tractable algorithm, a class of satisficing feedback strategies that generate reasonable, collision-free trajectories to the goal using simplified representations of the AMR dynamics and constrains is proposed. Realizations of the feedback strategy are presented and illustrated by simulation under the assumptions of perfect feedback information and zero servo error. Straightforward extensions of the approach to handle uncertainties in real systems are briefly described. >