Robot Navigation Problem Research Articles

Reinforcement based mobile robot navigation in dynamic environment

In this paper, a new approach is developed for solving the problem of mobile robot path planning in an unknown dynamic environment based on Q-learning. Q-learning algorithms have been used widely for solving real world problems, especially in robotics since it has been proved to give reliable and efficient solutions due to its simple and well developed theory. However, most of the researchers who tried to use Q-learning for solving the mobile robot navigation problem dealt with static environments; they avoided using it for dynamic environments because it is a more complex problem that has infinite number of states. This great number of states makes the training for the intelligent agent very difficult. In this paper, the Q-learning algorithm was applied for solving the mobile robot navigation in dynamic environment problem by limiting the number of states based on a new definition for the states space. This has the effect of reducing the size of the Q-table and hence, increasing the speed of the navigation algorithm. The conducted experimental simulation scenarios indicate the strength of the new proposed approach for mobile robot navigation in dynamic environment. The results show that the new approach has a high Hit rate and that the robot succeeded to reach its target in a collision free path in most cases which is the most desirable feature in any navigation algorithm.

Probabilistic Autonomous Robot Navigation in Dynamic Environments with Human Motion Prediction

This paper considers the problem of autonomous robot navigation in dynamic and congested environments. The predictive navigation paradigm is proposed where probabilistic planning is integrated with obstacle avoidance along with future motion prediction of humans and/or other obstacles. Predictive navigation is performed in a global manner with the use of a hierarchical Partially Observable Markov Decision Process (POMDP) that can be solved on-line at each time step and provides the actual actions the robot performs. Obstacle avoidance is performed within the predictive navigation model with a novel approach by deciding paths to the goal position that are not obstructed by other moving objects movement with the use of future motion prediction and by enabling the robot to increase or decrease its speed of movement or by performing detours. The robot is able to decide which obstacle avoidance behavior is optimal in each case within the unified navigation model employed.

2A2-G07 群れにおけるリーダー観測数の変化が速度合意に与える影響

This paper presents a solution to the problem of multiple robot navigation by an approach called Swarm Leading. We develop and analyze a method to form an effective network by introducing three kinds of robot about communication based on the approach. The simulation results verify the proposed method.

A biologically inspired method for robot navigation in a cluttered environment

SUMMARYThe problem of wheeled mobile robot (WMR) navigation toward an unknown target in a cluttered environment has been considered. The biologically inspired navigation algorithm is the equiangular navigation guidance (ENG) law combined with a local obstacle avoidance technique. The collision avoidance technique uses a system of active sensors which provides the necessary information about obstacles in the vicinity of the robot. In order for the robot to avoid collision and bypass the enroute obstacles, the angle between the instantaneous moving direction of the robot and a reference point on the surface of the obstacle is kept constant. The performance of the navigation strategy is confirmed with computer simulations and experiments with ActivMedia Pioneer 3-DX wheeled robot.

Probabilistic planning with clear preferences on missing information

For many real-world problems, environments at the time of planning are only partially-known. For example, robots often have to navigate partially-known terrains, planes often have to be scheduled under changing weather conditions, and car route-finders often have to figure out paths with only partial knowledge of traffic congestions. While general decision-theoretic planning that takes into account the uncertainty about the environment is hard to scale to large problems, many such problems exhibit a special property: one can clearly identify beforehand the best (called clearly preferred) values for the variables that represent the unknowns in the environment. For example, in the robot navigation problem, it is always preferred to find out that an initially unknown location is traversable rather than not, in the plane scheduling problem, it is always preferred for the weather to remain a good flying weather, and in route-finding problem, it is always preferred for the road of interest to be clear of traffic. It turns out that the existence of the clear preferences can be used to construct an efficient planner, called PPCP (Probabilistic Planning with Clear Preferences), that solves these planning problems by running a series of deterministic low-dimensional A*-like searches. In this paper, we formally define the notion of clear preferences on missing information, present the PPCP algorithm together with its extensive theoretical analysis, describe several useful extensions and optimizations of the algorithm and demonstrate the usefulness of PPCP on several applications in robotics. The theoretical analysis shows that once converged, the plan returned by PPCP is guaranteed to be optimal under certain conditions. The experimental analysis shows that running a series of fast low-dimensional searches turns out to be much faster than solving the full problem at once since memory requirements are much lower and deterministic searches are orders of magnitude faster than probabilistic planning.

A Method For Guidance of a Wheeled Mobile Robot Based on Received Radio Signal Strength Measurements

We propose a new guidance law for the problem of wheeled mobile robot (WMR) navigation towards an unknown target based on Received Signal Strength (RSS) Information. Given a mobile robot moving with a constant linear velocity, we use the miss-distance derivative as a measure for the angle at which the robot approaches the target. Miss-distance derivative is estimated from RSS, using a Robust Extended Kalman Filter (REKF). Having applied the proposed steering control law, termed Equiangular Navigation Guidance (ENG), the robot approaches the stationary or maneuvering target along a semi-equiangular spiral and eventually goes into a circular trajectory around it. In order to avoid circling, the algorithm is modified to decrease the robot's linear velocity to that of the target as it approaches the moving target and follow it in a smooth trajectory while preserving a safety distance from the target. Eventually, the performance of the guidance law and its effectiveness is confirmed with an extensive simulation study.

An Idiotypic Immune Network As a Short-Term Learning Architecture for Mobile Robots

A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then transferred to an idiotypic Artificial Immune System (AIS), which forms the STL phase, and the system is said to be seeded. The combined LTL-STL approach is compared with using STL only, and with using a handdesigned controller. In addition, the STL phase is tested when the idiotypic mechanism is turned off. The results provide substantial evidence that the best option is the seeded idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS for the STL. They also show that structurally different environments can be used for the two phases without compromising transferability.

A Fuzzy-Logic-Based Approach for Mobile Robot Path Tracking

One important problem in autonomous robot navigation is the effective following of an unknown path traced in the environment in compliance with the kinematic limits of the vehicle, i.e., bounded linear and angular velocities and accelerations. In this case, the motion planning must be implemented in real-time and must be robust with respect to the geometric characteristics of the unknown path, namely curvature and sharpness. To achieve good tracking capability, this paper proposes a path following approach based on a fuzzy-logic set of rules which emulates the human driving behavior. The input to the fuzzy system is represented by approximate information concerning the next bend ahead the vehicle; the corresponding output is the cruise velocity that the vehicle needs to attain in order to safely drive on the path. To validate the proposed algorithm two completely different experiments have been run: in the first experiment, the vehicle has to perform a lane-following task acquiring lane information in real-time using an onboard camera; in the second, the motion of the vehicle is obtained assigning in real-time a given time law. The obtained results show the effectiveness of the proposed method

Stable antiswing PD control for overhead crane systems with velocity estimation and uncertainties compensation

A common problem in robot navigation is behaviour rule selection when more than one action of the same type is available. Here action selection or decision-making procedure incorporating α-level fuzzy logic is discussed and used for selecting an appropriate action during mobile robot navigation. In the present approach, the operational strategies of the human expert driver are transferred via fuzzy logic to the robot navigation in the form of optimising behaviour rules without decreasing the input space and resolved the conflicts similar to human thinking. This paper presents the mathematical aspects of resolving conflicts when more than one context rule of the same kind is in action. Simulation results are presented based on the real life situation. The techniques presented in this paper are validated by conducting real world experiments using Khepera miniature Robot.

Toward multidimensional assignment data association in robot localization and mapping

It is well accepted that the data association or the correspondence problem is one of the toughest problems faced by any state estimation algorithm. Particularly in robotics, it is not very well addressed. This paper introduces a multidimensional assignment (MDA)-based data association algorithm for the simultaneous localization and map building (SLAM) problem in mobile robot navigation. The data association problem is cast in a general discrete optimization framework and the MDA formulation for multitarget tracking is extended for SLAM using sensor location uncertainty with the joint likelihood of measurements over multiple frames as the objective function. Methods for feature initialization and management are also integrated into the algorithm. When clutter is high and features are sparse, the compatibility information of features of a single measurement frame is not sufficient to make effective data-association decisions,thus compromising performance of single-frame-based methods. However, in a multiple-measurement-frame approach, the availability of more than one frame of measurement provides for more effective data-association decisions to be made, as consistency of measurements are looked at in several frames of measurement. Simulations are conducted to verify the performance gains over the conventional nearest neighbor (NN) data association algorithm and the joint compatibility branch and bound (JCBB) algorithm, especially in the presence of varying densities of spurious measurements and dynamic objects. Experimental results with ground truth are presented to demonstrate the practicality of the proposed data-association method in complex and large outdoor environments and its effectiveness over single-frame-based NN and JCBB schemes.

Line of sight robot navigation toward a moving goal

In this paper, we consider the problem of robot tracking and navigation toward a moving goal. The goal's maneuvers are not a priori known to the robot. Thus, off-line strategies are not effective. To model the robot and the goal, we use geometric rules combined with kinematics equations expressed in a polar representation. The intent of the strategy is to keep the robot between a reference point, called the observer, and the goal. We prove under certain assumptions that the robot navigating using this strategy reaches the moving goal successfully. In the presence of obstacles, the method is combined with an obstacle avoidance algorithm. The robot then moves in two modes, the navigation mode and the obstacle avoidance mode. Simulation of various scenarios highlights the efficiency of the method and provides an instructive comparison between the paths obtained for different reference points.

Robot navigation based on electrostatic field computation

This paper addresses the problem of mobile robot navigation using artificial potential fields. Many potential field based methodologies are found in the robotics literature, but most of them have problems with spurious local minima, which cause the robot to stop before reaching its target position. Although some free of local minima methodologies are found in the literature, none of them are easy to implement and generalize for complex shaped environments and robots. We propose a perfect analogy between electrostatic field computation and robot path planning. Thus, an easy solution to the problem, which is based on standard finite-element methods, can be applied with generic geometries and can even take into account the robot's orientation. To demonstrate the elegance of the proposed methodology, several experimental results with actual mobile robots are included

MOBILE ROBOT LOCALIZATION. REVISITING THE TRIANGULATION METHODS

Localization is one of the fundamental problems in mobile robot navigation. In this context, triangulation is used to determine the robot pose from landmarks position and angular measurements. The method based on circle intersection is the preferred one because it is independent of the robot heading. Nevertheless, it becomes undetermined when the robot point is on the circumference that contains the landmarks used. To cope with it, a triangulation method based on straight lines intersection is presented in this paper. The robot heading angle, which is needed in this method, can be accurately determined by means of a geometrical procedure. The accuracy of the presented approach is evaluated and compared to that of alternative methods by means of experimental results and computer simulations.

GA-Hard 문제를 풀기 위한 공진화 모델

일반적으로 유전자 알고리즘은 최적 시스템을 디자인하는데 주로 이용된다. 하지만 알고리즘의 성능은 적합도 함수나 시스템 환경에 의해 결정된다. 두 개의 개체군이 꾸준히 상호작용하고 공진화 하는 공진화 알고리즘은 이러한 문제를 극복할 수 있을 것으로 기대된다. 본 논문에서는 GA가 풀기 어려운 GA-hard problem을 풀기 위하여 저자가 제안한 3가지 공진화 모델을 설명한다. 첫 번째 모델은 찾고자하는 해와 환경을 각각 경쟁하는 개체군으로 구성해 진화하는 방법으로 사용자의 환경설정에 의해 지역적 해를 찾는 것을 방지하는 경쟁적 공진화 알고리즘이다. 두 번째 모델은 호스트 개체군과 기생(스키마) 개체군으로 구성된 스키마 공진화 알고리즘이다. 이 알고리즘에서 스키마 개체군은 호스트 개체군에 좋은 스키마를 공급한다. 세 번째 알고리즘은 두 개체군이 서로 게임을 통해 진화하도록 하는 게임이론에 기반한 공진화 알고리즘이다. 각 알고리즘은 비주얼 서보잉, 로봇 주행, 다목적 최적화 문제에 적용하여 그 유효성을 입증한다. Usually genetic algorithms are used to design optimal system. However the performance of the algorithm is determined by the fitness function and the system environment. It is expected that a co-evolutionary algorithm, two populations are constantly interact and co-evolve, is one of the solution to overcome these problems. In this paper we propose three types of co-evolutionary algorithm to solve GA-Hard problem. The first model is a competitive co-evolutionary algorithm that solution and environment are competitively co-evolve. This model can prevent the solution from falling in local optima because the environment are also evolve according to the evolution of the solution. The second algorithm is schema co-evolutionary algorithm that has host population and parasite (schema) population. Schema population supply good schema to host population in this algorithm. The third is game model-based co-evolutionary algorithm that two populations are co-evolve through game. Each algorithm is applied to visual servoing, robot navigation, and multi-objective optimization problem to verify the effectiveness of the proposed algorithms.

PATH PLANNING AND NAVIGATION IN A SPARSE WORLD SPACE ENVIRONMENT

This paper investigates the mobile robot navigation problem in a situation where the information about the navigation world can be available in a form of a sparse data set. Such a problem arises when the information about the navigation environment, obtained usually in a raster format from a vision sensor, is subject to noise, continuity and connectivity distortions. In order to deal with the data sparseness, an approach based on spatial filtering, a derivative of a Gabor filter, is proposed. The results of the filtering procedure are used in a different way for path planning and for navigation tasks. The mobile robot path is defined in terms of a skeleton that retains the connectivity information of the shape of the admissible navigation area. The navigation algorithm applies gradient-based neuromorphic processing on the environment map obtained by using a procedure for adaptive thresholding of the filtered sparse data.

The micronavigation algorithm: A minimalist approach to navigation

Psychologists' debate about the role of knowledge to control actions in living beings have strongly influenced the research in the field of artificial intelligence and, consequently, of robotics. In both fields a focus of the debate has been the relevance (or even the presence) of a mental/internal representation in driving the course of actions of biological/artificial beings. In this paper we show that even very complex navigation tasks within maze-like environments can be carried out by an agent which needs to store just ‘a handful of bytes’ of internal representation about the world in which it is moving. The approach is called micronavigation, since it aims to capture the problem of mobile robot navigation in its entirety but with a minimalist approach.

Dynamic positioning of a mobile robot using a laser-based goniometer

Positioning is a fundamental problem in mobile robot navigation. Several approaches to cope with the dynamic positioning problem have been made. Most of them are based on the inconsistent use of the algorithm for static positioning enhanced by predictive algorithms. In this paper, a method that guarantees the consistent use of this algorithm at any time under dynamic condition is presented. It combines vehicle kinematics and laser-based goniometer data for real time simulation of the evolution of the straight lines between the laser-based goniometer and the set of artificial landmarks used. Experimental results showing the accuracy of this method are presented.

Image-based memory for robot navigation using properties of omnidirectional images

This paper proposes a new technique for vision-based robot navigation. The basic framework is to localise the robot by comparing images taken at its current location with reference images stored in its memory. In this work, the only sensor mounted on the robot is an omnidirectional camera. The Fourier components of the omnidirectional image provide a signature for the views acquired by the robot and can be used to simplify the solution to the robot navigation problem. The proposed system can calculate the robot position with variable accuracy (‘hierarchical localisation’) saving computational time when the robot does not need a precise localisation (e.g. when it is travelling through a clear space). In addition, the system is able to self-organise its visual memory of the environment. The self-organisation of visual memory is essential to realise a fully autonomous robot that is able to navigate in an unexplored environment. Experimental evidence of the robustness of this system is given in unmodified office environments.

不正確さを許すマップにおける移動ロボットの自己位置推定

A major problem in mobile robot navigation using a map is that an accurate map needs a lot of building cost. A framework of navigation using a roughly measured map would be a solution of this problem. The paper proposes a Topological-Geometrical map, or TG map for short, which permits inaccurate description, and a method of mobile robot localization on a TG map. A TG map is built by defining the relative poses between geometrical entities in the environment. The models of the geometrical entities are supposed to be predefined, and the relative poses between them can be as inaccurate as those measured by eye. These features allow the map-building cost to be small. The robot pose is represented in a local frame attached on each entity since the robot pose based on a global reference frame might be inconsistent because of the inaccuracy of the map. Errors in relative poses between entities are represented by probability density functions, and the robot pose is estimated using a variant of Markov localization which is augmented so as to incorporate map errors into data fusion process. Simulation and experiments show that the robot pose is estimated correctly on a TG map by the proposed method.

DEEP-SARSA: A REINFORCEMENT LEARNING ALGORITHM FOR AUTONOMOUS NAVIGATION

In this paper we discuss the application of reinforcement learning algorithms to the problem of autonomous robot navigation. We show that the autonomous navigation using the standard delayed reinforcement learning algorithms is an ill posed problem and we present a more efficient algorithm for which the convergence speed is greatly improved. The proposed algorithm (Deep-Sarsa) is based on a combination between the Depth-First Search (a graph searching algorithm) and Sarsa (a delayed reinforcement learning algorithm).