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This paper presents a new algorithm to navigate robots in dynamically cluttered environments. The proposed algorithm uses basic concepts of space attraction (hence the term Agoraphilic) to navigate robots through dynamic obstacles. The new algorithm in this paper is an advanced development of the original Agoraphilic navigation algorithm that was only able to navigate robots in static environments. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for a free space (solutions) to follow. Therefore, it is also described as an optimistic navigation algorithm. This algorithm uses only one attractive force created by the available free space. The free-space concept allows the Agoraphilic algorithm to overcome inherited challenges of general navigation algorithms. However, the original Agoraphilic algorithm has the limitation in navigating robots only in static, not in dynamic environments. The presented algorithm was developed to address this limitation of the original Agoraphilic algorithm. The new algorithm uses a developed object tracking module to identify the time-varying free spaces by tracking moving obstacles. The capacity of the algorithm was further strengthened by the new prediction module. Future space prediction allowed the algorithm to make decisions considering future growing/diminishing free spaces. This paper also includes a bench-marking study of the new algorithm compared with a recently published APF-based algorithm under a similar operating environment. Furthermore, the algorithm was validated based on experimental tests and simulation tests. [ABSTRACT FROM AUTHOR]

SUMMARY: A new coverage path planning (CPP) algorithm, namely cell permeability-based coverage (CPC) algorithm, is proposed in this paper. Unlike the most CPP algorithms using approximate cellular decomposition, the proposed algorithm achieves exact coverage with lower coverage overlap compared to that with the existing algorithms. Apart from a formal analysis of the algorithm, the performance of the proposed algorithm is compared with two representative approximate cellular decomposition-based coverage algorithms reported in the literature. Results of demonstrative experiments on a TurtleBot mobile robot within the robot operating system/Gazebo environment and on a Fire Bird V robot are also provided. [ABSTRACT FROM AUTHOR]

SUMMARY: Navigation and path analysis in a cluttered environment is a challenging task over the last few decades. In this paper, a behavior-based neural network (BNN) and reactive control architecture have been presented for navigation of the mobile robot. Two different reactive behaviors have been taken as inputs function. Obstacle position is the first reactive behavior given by u(o), whereas obstacle angle u(n) according to the target position is the second reactive behavior. The angular velocity and steering angle are the output of the controller. The backpropagation architecture reduces the errors of weight function and records the best weight data that match the BNN controller. Using the BNN algorithm, the robot reacts quickly as compared to other developed techniques. To validate the performance of the controller, simulation and experimental results have been compared in the common platforms. The deviation in results for both the scenarios is found to be within 10%. The results of the BNN algorithm have also been compared with other existing techniques. Effectiveness of the proposed technique is measured in terms of smoothness of the realistic path, collision point detection, path length, and performance time. [ABSTRACT FROM AUTHOR]

SUMMARY: In this paper we address the problem of coverage path planning (CPP) for multiple cooperating mobile robots. We use a 'partition and cover' approach using Voronoi partition to achieve natural passive cooperation between robots to avoid task duplicity. We combine two generalizations of Voronoi partition, namely geodesic-distance-based Voronoi partition and Manhattan-distance-based Voronoi partition, to address contiguity of partition in the presence of obstacles and to avoid partition-boundary-induced coverage gap. The region is divided into 2 D ×2 D grids, where D is the size of the robot footprint. Individual robots can use any of the single-robot CPP algorithms. We show that with the proposed Geodesic-Manhattan Voronoi-partition-based coverage (GM-VPC), a complete and non-overlapping coverage can be achieved at grid level provided that the underlying single-robot CPP algorithm has similar property.We demonstrated using two representative single-robot coverage strategies, namely Boustrophedon-decomposition-based coverage and Spanning Tree coverage, first based on so-called exact cellular decomposition and second based on approximate cellular decomposition, that the proposed partitioning scheme completely eliminates coverage gaps and coverage overlaps. Simulation experiments using Matlab and V-rep robot simulator and experiments with Fire Bird V mobile robot are carried out to validate the proposed coverage strategy. [ABSTRACT FROM AUTHOR]

Evaluative techniques offer a tremendous potential for online controller design. However, when the optimization space is large and the performance metric is noisy, the overall adaptation process becomes extremely time consuming. Distributing the adaptation process reduces the required time and increases robustness to failure of individual agents. In this paper, we analyze the role of the four algorithmic parameters that determine the total evaluation time in a distributed implementation of a Particle Swarm Optimization (PSO) algorithm. For an obstacle avoidance case study using up to eight robots, we explore in simulation the lower boundaries of these parameters and propose a set of empirical guidelines for choosing their values. We then apply these guidelines to a real robot implementation and show that it is feasible to optimize 24 control parameters per robot within 2 h, a limited amount of time determined by the robots' battery life. We also show that a hybrid simulate-and-transfer approach coupled with a noise-resistant PSO algorithm can be used to further reduce experimental time as compared to a pure real-robot implementation. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic localization algorithm for a variable speed mobile robot. In order to localize a mobile robot with active beacon sensors, a relatively long time is needed, since the distance to the beacon is measured using the time-of-flight of the ultrasonic signal. The measurement time does not cause a high error rate when the mobile robot moves slowly. However, with an increase of the mobile robot's speed, the localization error becomes too high to use for accurate mobile robot navigation. Therefore, in this research into high-speed mobile robot operations, instead of using two active beacons for localization, an active beacon and the predicted path are utilized to localize the mobile robot. This new approach resolves the high-localization error caused by the speed of mobile robot. The performance of this method was verified by comparing it to the conventional method through real-world experiments. [ABSTRACT FROM PUBLISHER]

This paper considers planning and control algorithms that enable a programmable sheet to realize different shapes by autonomous folding. Prior work on self-reconfiguring machines has considered modular systems in which independent units coordinate with their neighbors to realize a desired shape. A key limitation in these prior systems is the typically many operations to make and break connections with neighbors, which lead to brittle performance. We seek to mitigate these difficulties through the unique concept of self-folding origami with a universal fixed set of hinges. This approach exploits a single sheet composed of interconnected triangular sections. The sheet is able to fold into a set of predetermined shapes using embedded actuation.We describe the planning algorithms underlying these self-folding sheets, forming a new family of reconfigurable robots that fold themselves into origami by actuating edges to fold by desired angles at desired times. Given a flat sheet, the set of hinges, and a desired folded state for the sheet, the algorithms (1) plan a continuous folding motion into the desired state, (2) discretize this motion into a practicable sequence of phases, (3) overlay these patterns and factor the steps into a minimum set of groups, and (4) automatically plan the location of actuators and threads on the sheet for implementing the shape-formation control. [ABSTRACT FROM AUTHOR]

This paper describes a complete laser-based approach for tracking the pose of a robot in a dynamic environment. The main novelty of this approach is that the matching between consecutively acquired scans is achieved using their associated curvature-based representations. The proposed scan matching algorithm consists of three stages. Firstly, the whole raw laser data is segmented into groups of consecutive range readings using a distance-based criterion and the curvature function for each group is computed. Then, this set of curvature functions is matched to the set of curvature functions associated to the previously acquired laser scan. Finally, characteristic points of pairwise curvature functions are matched and used to correctly obtain the best local alignment between consecutive scans. A closed form solution is employed for computing the optimal transformation and minimizing the robot pose shift error without iterations. Thus, the system is outstanding in terms of accuracy and computation time. The implemented algorithm is evaluated and compared to three state of the art scan matching approaches. [ABSTRACT FROM AUTHOR]

This paper presents a continuous wall-following controller for wheeled mobile robots based on odometry and distance information. The reference for this controller is the desired distance from the robot to the wall and allows the robot to follow straight wall contour as well as smoothly varying wall contours by including the curvature of the wall into the controller. The asymptotic stability of the control system is proved using a Lyapunov analysis. The controller is designed so as to avoid saturation of the angular velocity command to the robot. A novel switching scheme is also proposed that allows the robot to follow discontinuous contours allowing the robotic system to deal with typical problems of continuous wall-following controllers such as open corners and possible collisions. This strategy overcomes these instances by switching between dedicated behavior-based controllers. The stability of the switching control system is discussed by considering Lyapunov concepts. The proposed control systems are verified experimentally in laboratory and office environments to show the feasibility and good performance of the control algorithms. [ABSTRACT FROM AUTHOR]

SUMMARYThis paper presents a global ultrasonic system with selective activation algorithm for autonomous navigation of an indoor mobile robot. The global ultrasonic system consists of several ultrasonic transmitters fixed at reference positions in global coordinates and two receivers at moving coordinates of a mobile robot. By activating the ultrasonic transmitters through an radiofrequency (RF) channel, the robot is able to obtain distance information to the reference positions and localize itself in the global coordinates. Due to limitations in signal strength and beam width, the ultrasonic signals from some transmitters may not be delivered to the robot and the ultrasonic data become invalid. In order to improve the effectiveness of the global ultrasonic system, a so-called selective activation algorithm is developed. Based on the current position of the robot, the selective activation calls a proper ultrasonic transmitter and generates valid ultrasonic data at every sampling instant, resulting in faster, more accurate response for self-localization than does simple sequential activation. Path-following control experiments are conducted to verify the effectiveness of the self-localization based on the proposed selective activation algorithm with the global ultrasonic system. [ABSTRACT FROM AUTHOR]

FastSLAM 2.0 is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem. The sampling process is one of the most important phases in the FastSLAM 2.0 framework. Its estimation accuracy depends heavily on a correct prior knowledge about the control and observation noise statistics (the covariance matrices Q and R). Without the correct prior knowledge about these matrices, the estimation accuracy of the robot path and landmark positions may degrade seriously. However in many applications, the prior knowledge is unknown, or these noises are non-stationary. In this paper, these covariance matrices are supposed to be dynamic and denoted as Qt and Rt. Since there are noises, time-adjacent observations are inconsistent with each other. This inconsistency can reflect the real value of the covariance matrices. By the inconsistency, an extra step is introduced to the FastSLAM 2.0 framework. This step makes Qt and Rt match with their real value by using a particle swarm optimization method based on fractional calculus and alpha stable distribution (FC&ASD-PSO). Both simulation and experimental results show that the proposed algorithm improves the accuracy by the more accurate estimation on the noise covariance matrices. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an enhanced Monte Carlo localization (EMCL) algorithm for mobile robots, which deals with the premature convergence problem in global localization as well as the estimation error existing in pose tracking. By incorporating a mechanism for preventing premature convergence (MPPC), which uses a “reference relative vector” to modify the weight of each sample, exploration of a highly symmetrical environment can be improved. As a consequence, the proposed method has the ability to converge particles toward the global optimum, resulting in successful global localization. Furthermore, by applying the unscented Kalman Filter (UKF) to the prediction state and the previous state of particles in Monte Carlo Localization (MCL), an EMCL can be established for pose tracking, where the prediction state is modified by the Kalman gain derived from the modified prior error covariance. Hence, a better approximation that reduces the discrepancy between the state of the robot and the estimation can be obtained. Simulations and practical experiments confirmed that the proposed approach can improve the localization performance in both global localization and pose tracking. [ABSTRACT FROM AUTHOR]

A brain–computer interface (BCI) is a system for commanding a device by means of brain signals without having to move any muscle. One kind of BCI is based on Steady-State Visual Evoked Potentials (SSVEP), which are evoked visual cortex responses elicited by a twinkling light source. Stimuli can produce visual fatigue; however, it has been well established that high-frequency SSVEP (>30 Hz) does not. In this paper, a mobile robot is remotely navigated into an office environment by means of an asynchronous high-frequency SSVEP-based BCI along with the image of a video camera. This BCI uses only three electroencephalographic channels and a simple processing signal method. The robot velocity control and the avoidance obstacle algorithms are also herein described. Seven volunteers were able to drive the mobile robot towards two different places. They had to evade desks and shelves, pass through a doorway and navigate in a corridor. The system was designed so as to allow the subject to move about without restrictions, since he/she had full robot movement's control. It was concluded that the developed system allows for remote mobile robot navigation in real indoor environments using brain signals. The proposed system is easy to use and does not require any special training. The user's visual fatigue is reduced because high-frequency stimulation is employed and, furthermore, the user gazes at the stimulus only when a command must be sent to the robot. [ABSTRACT FROM AUTHOR]

This paper presents decentralized algorithms for coverage with mobile robots on a graph. Coverage is an important capability of multi-robot systems engaged in a number of different applications, including placement for environmental modeling, deployment for maximal quality surveillance, and even coordinated construction. We use distributed vertex substitution for locational optimization and equal mass partitioning, and the controllers minimize the corresponding cost functions. We prove that the proposed controller with two-hop communication guarantees convergence to the locally optimal configuration. We evaluate the algorithms in simulations and also using four mobile robots. [ABSTRACT FROM AUTHOR]

This paper introduces an optimal interval type-2 fuzzy proportional–integral–derivative (PID) controller to achieve the best trajectory tracking for nonholonomic wheeled mobile robots (WMRs). In the core of the proposed method, a novel population-based optimization algorithm, called teaching–learning-based optimization (TLBO), is employed for evolving the parameters of the controller as well as the parameters of the input and output membership functions. Two PID controllers are designed for each of two wheels separately whereas each controller has two inputs and one output that are logically connected by nine rules. The controller can handle the problem of integrated kinematic and dynamic tracking in the presence of uncertainties. Simulation results demonstrate the superiority of the proposed control scheme. [ABSTRACT FROM PUBLISHER]

This paper proposes a precise outdoor localization algorithm with the integration of Global Positioning System (GPS) and Inertial Navigation System (INS). To achieve precise outdoor localization, two schemes are recently proposed, which consist of de-noising the INS signals and fusing the GPS and INS data. To reduce the noise from the internal INS sensors, the discrete wavelet transform and variable threshold method are utilized, and to fuse the GPS and INS data while filtering out the noise caused by the acceleration, deceleration, and unexpected slips, the Unscented Particle Filter (UPF) is adopted. Conventional de-noising methods mainly employ a combination of low-pass and high-pass filters, which results in signal distortion. This newly proposed system also utilizes the vibration information of the actuator according to the fluctuations of the velocity to minimize the signal distortion. The UPF resolves the nonlinearities of the actuator and non-normal distributions of the noise more effectively than the conventional particle filter (PF) or Extended Kalman Filter–PF. The superiority of the proposed algorithm was verified through experiments, and the results are reported. [ABSTRACT FROM AUTHOR]

This paper presents an approach to visual obstacle avoidance and reactive robot navigation for outdoor and indoor environments. The obstacle detection algorithm includes an image feature tracking procedure followed by a feature classification process based on the IPT (Inverse Perspective Transformation). The classifier discriminates obstacle points from ground points. Obstacle features permit to draw out the obstacle boundaries which are used to construct a local and qualitative polar occupancy grid, analogously to a visual sonar. The navigation task is completed with a robocentric localization algorithm to compute the robot pose by means of an EKF (Extended Kalman Filter). The filter integrates the world coordinates of the ground points and the robot position in its state vector. The visual pose estimation process is intended to correct possible drifts on the dead-reckoning data provided by the proprioceptive robot sensors. The experiments, conducted indoors and outdoors, illustrate the range of scenarios where our proposal has proved to be useful, and show, both qualitatively and quantitatively, the benefits it provides. [ABSTRACT FROM AUTHOR]

This paper presents a new method using hierarchical optimal control for path planning and calculating maximum allowable dynamic load (MADL) of wheeled mobile manipulator (WMM). This method is useful for high degrees of freedom WMMs. First, the overall system is decoupled to a set of subsystems, and then, hierarchical optimal control is applied on them. The presented algorithm is a two-level hierarchical algorithm. In the first level, interaction terms between subsystems are fixed, and in the second level, the optimization problem for subsystems is solved. The results of second level are used for calculating new estimations of interaction variables in the first level. For calculating MADL, the load on the end effector is increased until actuators get into saturation. Given a large-scale robot, we show how the presenting in distributed hierarchy in optimal control helps to find MADL fast. Also, it enables us to treat with complicated cost functions that are generated by obstacle avoidance terms. The effectiveness of this approach on simulation case studies for different types of WMMs as well as an experiment for a mobile manipulator called Scout is shown. [ABSTRACT FROM PUBLISHER]

The formation of autonomous mobile robots to an arbitrary geometric pattern in a distributed fashion is a fundamental problem in formation control. This paper presents a new asynchronous, memoryless (oblivious) algorithm to the formation problem via distributed optimization techniques. The optimization minimizes an appropriately defined difference function between the current robot distribution and the target geometric pattern. The optimization processes are performed independently by individual robots in their local coordinate systems. A movement strategy derived from the results of the distributed optimizations guarantees that every movement makes the current robot configuration approaches the target geometric pattern until the final pattern is reached. [ABSTRACT FROM PUBLISHER]

Trilateration is the most adopted external reference-based localization technique for mobile robots, given the correspondence of external references. The nonlinear least-squares trilateration formulation provides an optimal position estimate from a general number (greater than or equal to the dimension of the environment) of reference points and corresponding distance measurements. This paper presents a novel closed-form solution to the nonlinear least-squares trilateration problem. The performance of the proposed algorithm in dealing with erroneous inputs of reference points and distance measurements has been analyzed through representative examples. The proposed trilateration algorithm has low computational complexity, high operational robustness, and reduced systematic error and uncertainty in position estimation. The effectiveness of the proposed algorithm has been further verified through an experimental test. [ABSTRACT FROM AUTHOR]

In this paper, we propose novel algorithms for reconfiguring modular robots that are composed of n atoms. Each atom has the shape of a unit cube and can expand/contract each face by half a unit, as well as attach to or detach from faces of neighboring atoms. For universal reconfiguration, atoms must be arranged in 2 × 2 × 2 modules. We respect certain physical constraints: each atom reaches at most constant velocity and can displace at most a constant number of other atoms. We assume that one of the atoms has access to the coordinates of atoms in the target configuration.Our algorithms involve a total of O(n2) atom operations, which are performed in O(n) parallel steps. This improves on previous reconfiguration algorithms, which either use O(n2) parallel steps or do not respect the constraints mentioned above. In fact, in the settings considered, our algorithms are optimal. A further advantage of our algorithms is that reconfiguration can take place within the union of the source and target configuration space, and only requires local communication. [ABSTRACT FROM AUTHOR]

High accuracy is usually difficult to obtain with a robotic arm installed on a mobile base, since the errors of the base are transferred to the manipulator. This paper proposes a method to address this problem through integration of a self-calibration algorithm and low-cost sensors. The self-calibration algorithm might be repeated several times during execution of a mission by the robot and is only based on the internal sensors of the robot, meaning that external observers or reference point transceivers (e.g., ultrasonic transceivers) are not used. The proposed self-calibration system was implemented on a pole climbing robot and effectively improved the positioning accuracy of the climbing arm. [ABSTRACT FROM AUTHOR]

This paper presents a quadratic programming (QP) form algorithm to realize on-line planning of mobile manipulators with consideration for improving the stability level. With Lie group and screw tools, the general tree topology structure mobile robot dynamics and dynamic stability attributes were analysed. The stable support condition for a mobile robot is constructed not only in a polygonal support region, but also in a polyhedral support region. For a planar supporting region, the tip-over avoiding requirement is formulated as the tip-over prevent constraints with the reciprocal products of the resultant support wrench and the imaginary tip-over twists, which are constructed with the boundaries of the convex support polygon. At velocity level, the optimized resolution algorithm with standard QP form is designed to resolve the inverse redundant kinematics of the Omni-directional Mobile ManipulatorS (OMMS) with stability considerations. Numerical simulation results show that the presented methods successfully improve the stability level of the robot within an on-line planning process. [ABSTRACT FROM AUTHOR]

SUMMARYThis paper deals with the stability of null-space velocity control algorithms in extended operational space for redundant robots. We compare the performance of the control algorithm based on the minimal null-space projection and generalized-inverse-based projection into the Jacobian null-space. We show how the null-space projection affects the performance of the null-space tracking algorithm. The results are verified with the simulation and real implementation on a redundant mobile robot composed of 3 degrees of freedom (DOFs) mobile platform and 7-DOF robot arm. [ABSTRACT FROM AUTHOR]

The problem studied in this paper is a mobile robot that autonomously navigates in a domestic environment, builds a map as it moves along and localizes its position in it. In addition, the robot detects predefined objects, estimates their position in the environment and integrates this with the localization module to automatically put the objects in the generated map. Thus, we demonstrate one of the possible strategies for the integration of spatial and semantic knowledge in a service robot scenario where a simultaneous localization and mapping (SLAM) and object detection recognition system work in synergy to provide a richer representation of the environment than it would be possible with either of the methods alone. Most SLAM systems build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. The novelty is the augmentation of this process with an object-recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way, the user can command the robot to retrieve a certain object from a certain room. We present the results of map building and an extensive evaluation of the object detection algorithm performed in an indoor setting. [ABSTRACT FROM AUTHOR]

We study a problem of K-barrier coverage by employing a network of self-deployed, autonomous mobile robotic sensors. A decentralized coordination algorithm is proposed for the robotic sensors to address the coverage problem. The algorithm is developed based on some simple rules that only rely on local information. By applying the algorithm to the robotic sensors, K layers of sensor barriers are formed to cover the region between two given points. To illustrate the proposed algorithm, numerical simulations are carried out for a number of scenarios. [ABSTRACT FROM PUBLISHER]

SUMMARY: A data association algorithm for simultaneous localization and mapping (SLAM) based on central difference joint compatibility (CDJC) criterion and clustering is proposed to obtain the data association results. Firstly, CDJC criterion is designed to calculate joint Mahalanobis distance. Secondly, ordering points to identify the clustering structure is used to divide all observed features into several groups. Thirdly, CDJC branch and bound method is designed to be performed in each group. The results based on simulation data and benchmark dataset show that the proposed algorithm has low computational complexity and provide accurate association results for SLAM of mobile robot. [ABSTRACT FROM AUTHOR]

In November 2012, the eleventh Workshop on Distributed Autonomous Robotic Systems (DARS 2012) was held in Baltimore, Maryland on the campus of Johns Hopkins University. Previous DARS workshops were held in 1992, 1994, and 1996 in Riken, Wako, Japan; Karlsruhe, Germany (1998); Knoxville, Tennessee, USA (2000); Fukuoka, Japan (2002); Toulouse, France (2004); Minneapolis, Minnesota, USA (2006); Tsukuba, Ibaraki, Japan (2008); and Lausanne, Switzerland (2010). [ABSTRACT FROM AUTHOR]

Several non-holonomic Dubins-car-like robots travel over paths with bounded curvatures in a plane that contains an a priori unknown region. The robots are anonymous to one another and do not use communication facilities. Any of them has access to the current minimum distance to the region and can determine the relative positions and orientations of the other robots within a finite and given visibility range. We present a distributed navigation and guidance strategy under which every robot autonomously converges to the desired minimum distance to the region with always respecting a given safety margin, the robots do not collide with one another and do not get into clusters, and the entire team ultimately sweeps over the respective equidistant curve at a speed exceeding a given threshold, thus forming a kind of a sweeping barrier at the perimeter of the region. Moreover, this strategy provides effective sub-uniform distribution of the robots over the equidistant curve. Mathematically rigorous justification of the proposed strategy is offered; its effectiveness is confirmed by extensive computer simulations and experiments with real wheeled robots. [ABSTRACT FROM PUBLISHER]

A new motion-free iterative closest point (ICP) algorithm is proposed for building a two-dimensional (2D) map for mobile robot navigation. A laser range finder (LRF) sensor is installed on a mobile robot to scan and measure the depth data of the environment to form a 2D map during mobile robot navigation. Because the scanning and navigation motions are performed independently, the scanned data contain distortions from the motions of the mobile robot. To compensate for the distortions, the proposed motion-free ICP algorithm estimates the effects of the dynamic motions of the robot on the scanning process. That is, the motion-free algorithm compensates for the distance measurement errors related to the dynamic changes in the mobile robot's velocity. Experiments were performed with actual velocity changes of a mobile robot to demonstrate and verify the effective performance of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

We present a model of construction using iterative amorphous depositions and give a distributed algorithm to reliably build ramps in unstructured environments. The relatively simple local strategy for interacting with irregularly shaped, partially built structures gives rise to robust adaptive global properties. We illustrate the algorithm in both single robot and multi-robot cases via simulations and describe how to solve key technical challenges to implementing this algorithm via a robotic prototype. [ABSTRACT FROM AUTHOR]

We determine the shortest (minimal in length) path on a unicycle-like mobile robot in a known environment with smooth (possibly non-convex) obstacles with a constraint on curvature of their boundaries. Furthermore, we propose a new reactive randomized algorithm of robot navigation in unknown environment and prove that the robot will avoid collisions and reach a steady target with probability 1. The performance of our algorithm is confirmed by computer simulations and outdoor experiments with a Pioneer P3-DX mobile wheeled robot. [ABSTRACT FROM AUTHOR]

A new localization algorithm is proposed for a fast moving mobile robot, which utilizes only one beacon and the global features of the differential-driving mobile robot. It takes a relatively long time to localize a mobile robot with active beacon sensors, since the distance to the beacon is measured based on the traveling time of the ultrasonic signal. When the mobile robot is moving slowly, the measurement time does not yield a high error. At a higher speed, however, the localization error becomes too large for the mobile robot to be located accurately. Therefore, in high-speed mobile robot operations, instead of using two or more active beacons, only one active beacon and the global features of the mobile robot are used to localize the mobile robot. The two global features are the radius and center of the rotational motion for the differential-driving mobile robot, which generally describe the motion of, and are used for the trace prediction of, a mobile robot. In high-speed operations, the localizer finds the intersection point of this predicted trace and the circle, which is centered at the beacon whose radius is the distance between the mobile robot and the beacon. This new approach overcomes the large localization error caused by the high speed of the mobile robot. The performance of the new localization algorithm is verified through experiments with a high-speed mobile robot. [ABSTRACT FROM PUBLISHER]

The proposed algorithm integrates in a single planner the global motion planning and local obstacle avoidance capabilities. It efficiently guides the robot in a dynamic environment. This eliminates some of the traditional problems of planned architectures (model-plan-act scheme) while obtaining many of the qualities of behavior-based architectures. The computational efficiency of the method allows the planner to operate at high-rate sensor frequencies. This avoids the need for using both a collision-avoidance algorithm and a global motion planner for navigation in a cluttered environment. The method combines map-based and sensor-based planning operations to provide a smooth and reliable motion plan. Operating on a simple grid-based world model, the method uses a fast marching technique to determine a motion plan on a Voronoi extended transform extracted from the environment model. In addition to this real-time response ability, the method produces smooth and safe robot trajectories. [ABSTRACT FROM AUTHOR]