Integrating active localization into high-level robot control systems

High-level control systems are designed to enable mobile robots to successfully perform complex missions such as office delivery and survillance tasks. For that purpose they have to control, coordinate, and monitor different kinds of subtasks like navigation, manipulation, and perception. An important aspect of the effectiveness of high-level control systems is the ability to cope with failures that occur during the execution of such subtaks. In this paper we focus on the particular subtask of estimating the position of the robot and show how to achieve its robust integration into the high-level control system. The principle of this integration is to monitor the certainty of the position estimation and to autonomously relocalize the robot whenever the uncertainly grows too large. We present a localization approach which accurately and efficiently keeps track of the robot's position. Furthermore, it provides a measure for detecting localization failures and it is able to autonomously relocalize the robot in such situations. In addition to this, we introduce structured reactive plans, which can be interrupted by such active localization processes at any point in time and allow the robot to complete its mission afterwards. Our method has been implemented and shown to be robust in long-term experiments involving a typical office delivery scenario.     

Distributed algorithms for partitioning a swarm of autonomous mobile robots

A number of recent studies address systems of mobile autonomous robots from a distributed computing point of view. Although such systems employ robots that are relatively weak and simple (i.e., dimensionless, oblivious and anonymous), they are nevertheless expected to have strong fault tolerance capabilities as a group. This paper studies the partitioning problem, where n$n$ robots must divide themselves into k$k$ size-balanced groups, and examines the impact of common orientation on the solvability of this problem. First, deterministic crash-fault-tolerant algorithms are given for the problem in the asynchronous full-compass and semi-synchronous half-compass models, and a randomized algorithm is given for the semi-synchronous no-compass model. Next, the role of common orientation shared by the robots is examined. Necessary and sufficient conditions for the partitioning problem to be solvable are given in the different timing models. Finally, the problem is proved to be unsolvable in the no-compass synchronous model.     

High-speed laser localization for mobile robots

This paper describes a novel, laser-based approach for tracking the pose of a high-speed mobile robot. The algorithm is outstanding in terms of accuracy and computation time. The efficiency is achieved by a closed-form solution for the matching of two laser scans, the use of natural scan features and fast linear filters. The implemented algorithm is evaluated with the high-speed robot Kurt3D (4 m/s), and compared to standard scan matching methods in indoor and outdoor environments.     

Corridor navigation and wall-following stable control for sonar-based mobile robots

In this paper, a mobile robot control law for corridor navigation and wall-following, based on sonar and odometric sensorial information is proposed. The control law allows for stable navigation avoiding actuator saturation. The posture information of the robot travelling through the corridor is estimated by using odometric and sonar sensing. The control system is theoretically proved to be asymptotically stable. Obstacle avoidance capability is added to the control system as a perturbation signal. A state variables estimation structure is proposed that fuses the sonar and odometric information. Experimental results are presented to show the performance of the proposed control system.     

Reliable Monte Carlo localization for mobile robots

Reliability is a key factor for realizing safety guarantee of fully autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick relocalization, simultaneously. The presented method can be implemented using a similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables a robot to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick relocalization from a failure state can be realized while mitigating noisy influence of global localization. We conduct three types of experiments using wheeled mobile robots equipped with a two-dimensional LiDAR. Results show that reliable MCL that performs robust localization, self-failure detection, and quick failure recovery can be realized.     

Guaranteed interval analysis localization for mobile robots

This paper presents a set membership method (named Interval Analysis Localization (IAL)) to deal with the global localization problem of mobile robots. By using a LIDAR (LIght Detection And Ranging) range sensor, the odometry and a discrete map of an indoor environment, a robot has to determine its pose (position and orientation) in the map without any knowledge of its initial pose. In a bounded error context, the IAL algorithm searches a set of boxes (interval vector), with a cardinality as small as possible that includes the robot’s pose. The localization process is based on constraint propagation and interval analysis tools, such as bisection and relaxed intersection. The proposed method is validated using real data recorded during the CAROTTE challenge, organized by the French ANR (National Research Agency) and the French DGA (General Delegation of Armament). IAL is then compared with the well-known Monte Carlo Localization showing weaknesses and strengths of both algorithms. As it is shown in this paper with the IAL algorithm, interval analysis can be an efficient tool to solve the global localization problem.     

Active visual localization for multiple inspection robots

In the routine inspection of industrial or other areas, teams of robots with various sensors could operate together to great effect, but require reliable, accurate and flexible localization capabilities to be able to move around safely. We demonstrate accurate localization for an inspection team consisting of a robot with stereo active vision and its blind companion with an active lighting system, and show that in this case a single sensor can be used for measuring the position of known or unknown scene features, measuring the relative location of the two robots and actually carrying out an inspection task.     

Models of computation for reactive control of autonomous mobile robots

The paper studies computation models for tasks performed by autonomous mobile robots. Such tasks can be accomplished by reactive control algorithms. Reactive control systems can be described using different models of computation which have as distinguishing feature the abstraction level of time. Thus, three computation models are defined: the untimed model, the synchronous model and the timed model. It is shown that the clocked-synchronous model of computation is more appropriate for describing the controller for a parallel parking task.     

Emerging motor behaviors: Learning joint coordination in articulated mobile robots

In this paper, we analyze the insights behind the common approach to the assessment of robot motor behaviors in articulated mobile structures with compromised dynamic balance. We present a new approach to this problem and a methodology that implements it for motor behaviors encapsulated in rest-to-rest motions. As well as common methods, we assume the availability of kinematic information about the solution to the task, but reference is not made to the workspace, allowing the workspace to be free of restrictions. Our control framework, based on local control policies at the joint acceleration level, attracts actuated degrees of freedom (DOFs) to the desired final configuration; meanwhile, the resulting final states of the unactuated DOFs are viewed as an indirect consequence of the profile of the policies. Dynamical systems are used as acceleration policies, providing the actuated system with convenient attractor properties. The control policies, parameterized around imposed simple primitives, are deformed by means of changes in the parameters. This modulation is optimized, by means of a stochastic algorithm, in order to control the unactuated DOFs and thus carry out the desired motor behavior.     

Evolution of neural control structures: some experiments on mobile robots

From perception to action and from action to perception, all elements of an autonomous agent are interdependent and need to be strongly coherent. The final behavior of the agent is the result of the global activity of this loop and every weakness or incoherence of a single element has strong consequences on the performances of the agent. We think that, for the purpose of building autonomous robots, all these elements need to be developed together in continuous interaction with the environment. We describe the implementation of a possible solution (artificial neural networks and genetic algorithms) on a real mobile robot through a set of three different experiments. We focus our attention on three different aspects of the control structure: perception, internal representation and action. In all the experiments these aspects are not considered as single processing elements, but as part of an agent. For every experiment, the advantages and disadvantages of this approach are presented and discussed. The results show that the combination of genetic algorithms and neural networks is a very interesting technique for the development of control structures in autonomous agents. The time necessary for evolution, on the other hand, is a very important limitation of the evolutionary approach.     

基于单应性矩阵的移动机器人编队跟随控制

本文针对以领航跟随模型为代表的移动机器人编队系统提出了一种基于单应性的编队跟随控制方案,在给定理想队形间隔距离和理想期望图像的前提下,利用单应性矩阵构造可反映理想队形中跟随机器人实时位姿的虚拟机器人,将原先的编队问题转化为对虚拟机器人的轨迹跟踪问题.编队跟随过程中,领航机器人的速度采用估计的方式,利用单应性与速度之间的关系模型以及跟随机器人的实时速度能较为准确的估计领航速度,从而避免采用局部通信的方式,节省了编队实验成本.最后本文进行的半实物仿真以及实物实验均可验证所提出的编队跟随算法包括速度估计方法的实际有效性.     

Integrated architecture for industrial robot programming and control

As robot control systems are traditionally closed, it is difficult to add supplementary intelligence. Accordingly, as based on a new notion of user views, a layered system architecture is proposed. Bearing in mind such industrial demands as computing efficiency and simple factory-floor operation, the control layers are parameterized by means of functional operators consisting of pieces of compiled code that can be passed as parameters between the layers. The required interplay between application-specific programs and built-in motion control is thereby efficiently accomplished. The results from experimental evaluation and several case studies suggest the architecture to be very useful also in an industrial context.     

Switching control approach for stable navigation of mobile robots in unknown environments

This paper presents a stable switching control strategy for the parking problem of non-holonomic mobile robots. First, it is proposed a positioning-orientation switching controller for the parking problem. With this strategy robot backwards motions are avoided and the robot heading is always in the direction of the goal point facilitating the obstacle handling. Second, the avoidance of unexpected obstacles is considered in a reactive way by following the contour of the obstacles. Next, the stability of the switching parking/obstacle-avoider controller is analyzed showing stability under reasonable conditions. Finally, the good performance and the feasibility of this approach are shown through several experimental results.     

Dealing with internal and external perturbations on walking robots

Up to now, walking robots have been working outdoors under favorable conditions and using very large stability margins to cope with natural environments and intrinsic robot dynamics that can cause instability in these machines when they use statically-stable gaits. The result has been very slow robots prone to tumble down in the presence of perturbations. This paper proposes a novel gait-adaptation method based on the maximization of the Normalized Dynamic Energy Stability Margin. This method enables walking-machine gaits to adapt to internal (robot dynamics) and external (environmental) perturbations, including the slope of the terrain, by finding the gait parameters that maximize robot stability. The adaptation method is inspired in the natural gait adaptation carried out by humans and animals to balance external forces or the effect of sloping terrain. Experiments with the SILO4 quadruped robot are presented and show how robot stability is more robust when the proposed approach is used for different external forces and sloping terrains. Using the proposed gait-adaptation approach the robot is able to withstand external forces up to 58% the robot weight and 25-degree slopes.

Getting to know each other—Artificial social intelligence for autonomous robots

This paper proposes a research direction to study the development of ‘artificial social intelligence’ of autonomous robots which should result in ‘individualized robot societies’. The approach is highly inspired by the ‘social intelligence hypothesis’, derived from the investigation of primate societies, suggesting that primate intelligence originally evolved to solve social problems and was only later extended to problems outside the social domain. We suggest that it might be a general principle in the evolution of intelligence, applicable to both natural and artificial systems. Arguments are presented why the investigation of social intelligence for artifacts is not only an interesting research issue for the study of biological principles, but may be a necessary prerequisite for those scenarios in which autonomous robots are integrated into human societies, interacting and communicating both with humans and with each other. As a starting point to study experimentally the development of robots' ‘social relationships’, the investigation of collection and use of body images by means of imitation is proposed. A specific experimental setup which we use to test the theoretical considerations is described. The paper outlines in what kind of applications and for what kind of robot group structures social intelligence might be advantageous.     

Leader–follower formation control of nonholonomic mobile robots with input constraints

The paper deals with leader–follower formations of nonholonomic mobile robots, introducing a formation control strategy alternative to those existing in the literature. Robots’ control inputs are forced to satisfy suitable constraints that restrict the set of leader possible paths and admissible positions of the follower with respect to the leader. A peculiar characteristic of the proposed strategy is that the follower position is not rigidly fixed with respect to the leader but varies in proper circle arcs centered in the leader reference frame.     

Fast self-localization method for mobile robots using multiple omnidirectional vision sensors

This paper presents a method for estimating position and orientation of multiple robots from a set of azimuth angles of landmarks and other robots which are observed by multiple omnidirectional vision sensors. Our method simultaneously performs self-localization by each robot and reconstruction of a relative configuration between robots. Even if it is impossible to identify correspondence between each index of the observed azimuth angles and those of the robots, our method can reconstruct not only a relative configuration between robots using `triangle and enumeration constraints' but also an absolute one using the knowledge of landmarks in the environment. In order to show the validity of our method, this method is applied to multiple mobile robots each of which has an omnidirectional vision sensor in simulation and the real environment. The experimental results show that the result of our method is more precise and stabler than that of self-localization by each robot and our method can handle the combinatorial explosion problem. Correspondence to:T. Nakamura (e-mail: ntakayuk@sys.wakayama-u.ac.jp)     

Model-based sonar localisation for mobile robots

We describe a sonar localisation system for autonomous mobile robot navigation in a known environment, which tries to extract as much information as possible from the sensors by building a detailed probabilistic model of each sonar event. It takes account of multiple hypotheses about the source of each signal and uses a probabilistic sensor fusion technique to merge the results into a single location update. The system is designed to run under our decentralised, highly parallel vehicle architecture, and we discuss some of the implementation techniques required to achieve this. The results of some initial simulations are presented.     

Positioning device for outdoor mobile robots using optical sensors and lasers

We propose a novel method for positioning a mobile robot in an outdoor environment using lasers and optical sensors. Position estimation via a noncontact optical method is useful because the information from the wheel odometer and the global positioning system in a mobile robot is unreliable in some situations. Contact optical sensors such as computer mouse are designed to be in contact with a surface and do not function well in strong ambient light conditions. To mitigate the challenges of an outdoor environment, we developed an optical device with a bandpass filter and a pipe to restrict solar light and to detect translation. The use of two devices enables sensing of the mobile robot’s position, including posture. Furthermore, employing a collimated laser beam allows measurements against a surface to be invariable with the distance to the surface. In this paper, we describe motion estimation, device configurations, and several tests for performance evaluation. We also present the experimental positioning results from a vehicle equipped with our optical device on an outdoor path. Finally, we discuss an improvement in postural accuracy by combining an optical device with precise gyroscopes.