Qualitative spatial representations from task-oriented perception and exploratory behaviors

We describe motor and perceptual behaviors that have proven useful for indoor navigation of an autonomous mobile robot. These behaviors take advantage of the large amount of structure that characterizes many indoor, office-like environments. Based on pre-existing structural landmarks, a mobile robot has the ability to explore, map, and navigate one among several office buildings sharing similar structural features, while coping with slow environment variations and local dynamics. The mobile robot develops and maintains an internal spatial representation of the environment in terms of a topological and qualitative map. The types of structural features suitable as navigation landmarks largely depend upon the available sensors. Adequate navigation performance is achieved by subdividing perception and navigation into a number of behaviors layered upon a multi-threaded real-time control architecture.     

Detecting and diagnosing mistakes in vision-based navigation

Robust mobile robot navigation is an open problem. Navigational mistakes are inevitable due to the characteristics of sensors, models, actuators, and natural environments. This paper describes how to detect and diagnose mistakes that autonomous mobile robots make while navigating through large-scale space using vision. Mistakes are perceptual, cognitive, or motor events that lead a robot astray from its intended route. Detecting and diagnosing a mistake involves realizing that this has happened and determining what the mistake was and when it happened.

The approach described in this paper handles mistakes explicitly. Mistakes are detected by looking for mismatches between observations and expectations. Detailed symbolic representations of visual information support comparing observations and expectations augmented by a priori knowledge. Mistakes are diagnosed by examining knowledge from a variety of sources, including a history of the mobile robot's observations and actions. A computer program called implements this approach in simulation and provides experimental results that demonstrate the feasibility and potential of the approach.     

Learning locomotion reflexes: A self-supervised neural system for a mobile robot

This article is concerned with an artificial neural system for a mobile robot reactive navigation in an unknown, cluttered environment. Reactive navigation is a process of immediately choosing locomotion actions in response to measured spatial situations, while no planning occurs. A task of a presented system is to provide a steering angle signal letting a robot reach a goal while avoiding collisions with obstacles. Basic reactive navigation methods are briefly characterized, special attention is paid to a neural approach to the considered problem. The authors describe the system's architecture and important details of the algorithm. The main parts of the system are: the Fuzzy ART neural self-organizing classifier, performing a perceptual space partitioning, and a neural associative memory, memorizing the system's experience and superposing influences of different behaviors. Tests show that the learning process, starting from zero, is efficient, despite some initial fluctuations of its effectiveness.     

Navigation with constraints for an autonomous mobile robot

This paper describes a navigation planning algorithm for a robot capable of autonomous navigation in a structured, partially known and dynamic environment. This algorithm is applied to a discrete workspace composed of a network of places and roads. The environment specification associates temporal constraints with any element of the network, and recharge or relocalisation possibilities with places. A mission specification associates several constraints with each navigation task (energy, time, position uncertainty and distance).

The algorithm computes an optimal path for each navigation task according to the optimization criterion and constraints. We introduce the notion of efficient path applied to a new best first search algorithm solving a multiple constraints problem. The path determination relies on a state representation adapted to deal with environment constraints. We then prove that the complexity chracteristics of our algorithm are similar to those of the A* algorithm.

The planner described in this paper has been implemented on a Spare station for a Robuter mobile platform equipped with ultra-sonic range sensors and an active stereo vision system. It was developed for the MITHRA family of autonomous surveillance robots as part of project EUREKA EU 110.     

A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments

Complete coverage navigation (CCN) requires a special type of robot path planning, where the robots should pass every part of the workspace. CCN is an essential issue for cleaning robots and many other robotic applications. When robots work in unknown environments, map building is required for the robots to effectively cover the complete workspace. Real-time concurrent map building and complete coverage robot navigation are desirable for efficient performance in many applications. In this paper, a novel neural-dynamics-based approach is proposed for real-time map building and CCN of autoxnomous mobile robots in a completely unknown environment. The proposed model is compared with a triangular-cell-map-based complete coverage path planning method (Oh , 2004) that combines distance transform path planning, wall-following algorithm, and template-based technique. The proposed method does not need any templates, even in unknown environments. A local map composed of square or rectangular cells is created through the neural dynamics during the CCN with limited sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. In addition, square and rectangular cell map representations are proposed for real-time map building and CCN. Comparison studies of the proposed approach with the triangular-cell-map-based complete coverage path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free complete coverage paths in unknown environments.      

Robot navigation in the real world:: Experiments with Manchester’s FortyTwo in unmodified, large environments

Mobile robot navigation under controlled laboratory conditions is, by now, state of the art and reliably achievable. To transfer navigation mechanisms used in such small-scale environments to applications in untreated, large environments, however, is not trivial, and typically requires modifications to the original navigation mechanism: scaling up is hard.In this paper, we discuss the difficulties of mobile robot navigation in general, the various options to achieve navigation in large environments, and experiments with Manchester’s FortyTwo, which investigate how scaling up of navigational competencies can be achieved. We were particularly interested in autonomous mobile robot navigation in unmodified, large and varied environments, without the aid of pre-installed maps or supplied CAD models of the environment. This paper presents a general approach to achieve this.FortyTwo regularly travels the corridors of the Department of Computer Science at Manchester University, using topological maps, landmarks, low level “enabling behaviours” and active exploitation of features of the environment. Experimental results obtained in these environments are given in this paper.     

基于行为优先与模糊控制的移动机器人导航

由于动态未知环境下自主移动机器人的导航具有较大困难,为实现自主机器人在动态未知环境下的无碰撞运行,文中将行为优先级控制与模糊逻辑控制相结合,提出4种基本行为控制策略:目标寻找、避障、跟踪和解锁.针对'U'型和'V'型障碍物运行解锁问题,提出了行走路径记忆方法,并通过构建虚拟墙来避免机器人再次走入此类区域.仿真实验表明,所提出的控制策略可有效地运用于复杂和未知环境下自主移动机器人的导航,且具有较好的鲁棒性和适应性.     

Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory

We describe a general framework for learning perception-based navigational behaviors in autonomous mobile robots. A hierarchical behavior-based decomposition of the control architecture is used to facilitate efficient modular learning. Lower level reactive behaviors such as collision detection and obstacle avoidance are learned using a stochastic hill-climbing method while higher level goal-directed navigation is achieved using a self-organizing sparse distributed memory. The memory is initially trained by teleoperating the robot on a small number of paths within a given domain of interest. During training, the vectors in the sensory space as well as the motor space are continually adapted using a form of competitive learning to yield basis vectors that efficiently span the sensorimotor space. After training, the robot navigates from arbitrary locations to a desired goal location using motor output vectors computed by a saliency-based weighted averaging scheme. The pervasive problem of perceptual aliasing in finite-order Markovian environments is handled by allowing both current as well as the set of immediately preceding perceptual inputs to predict the motor output vector for the current time instant. We describe experimental and simulation results obtained using a mobile robot equipped with bump sensors, photosensors and infrared receivers, navigating within an enclosed obstacle-ridden arena. The results indicate that the method performs successfully in a number of navigational tasks exhibiting varying degrees of perceptual aliasing.     

Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory

We describe a general framework for learning perception-based navigational behaviors in autonomous mobile robots. A hierarchical behavior-based decomposition of the control architecture is used to facilitate efficient modular learning. Lower level reactive behaviors such as collision detection and obstacle avoidance are learned using a stochastic hill-climbing method while higher level goal-directed navigation is achieved using a self-organizing sparse distributed memory. The memory is initially trained by teleoperating the robot on a small number of paths within a given domain of interest. During training, the vectors in the sensory space as well as the motor space are continually adapted using a form of competitive learning to yield basis vectors that efficiently span the sensorimotor space. After training, the robot navigates from arbitrary locations to a desired goal location using motor output vectors computed by a saliency-based weighted averaging scheme. The pervasive problem of perceptual aliasing in finite-order Markovian environments is handled by allowing both current as well as the set of immediately preceding perceptual inputs to predict the motor output vector for the current time instant. We describe experimental and simulation results obtained using a mobile robot equipped with bump sensors, photosensors and infrared receivers, navigating within an enclosed obstacle-ridden arena. The results indicate that the method performs successfully in a number of navigational tasks exhibiting varying degrees of perceptual aliasing.     

一种基于人机交互的移动机器人反应式导航方法

针对未知环境中移动机器人的自主导航问题,提出了一种基于人机交互的反应式导航方法。在采用模糊逻辑实现机器人基本智能行为的基础上,利用基于优先级和有限状态机的混合行为协调方法建立"环境刺激-反应"机制,提高机器人的局部自主能力。提出将"人刺激-反应"机制引入机器人系统,提高机器人系统对环境的理解与决策能力。在不同环境模型中利用提出的方法对移向指定目标的机器人自主导航进行了仿真,仿真结果验证了该方法的有效性。     

Mobile robot navigation using motor schema and fuzzy context dependent behavior modulation

This paper presents a novel technique to autonomously select different motor schemas using fuzzy context dependant blending of robot behaviors for navigation. First, a set of motor schemas is formed as behaviors. Both strategic and reactive type schemas have been employed in order to facilitate both the aspects of global and local motion planning. While strategic schemas are formed using the prior knowledge of the environment, the reactive schemas are activated using current sensory data of the robot. For global path planning, a safe path is first created using a Voronoi diagram. For local planning, the Voronoi vertices are treated as immediate subgoals and are used to form schemas leading to achieve optimized traveled distance and goal oriented robot navigation. Two motor schemas are formed as reactive behaviors for obstacle avoidance. The unknown obstacles are modeled using the sensory data. The coordinated behavior is achieved while employing weighed vector summation of the schemas. The adaptation of weights are achieved through a fuzzy inference system where fuzzy rules are used to dynamically generate the weights during navigation. A novel approach is proposed for fuzzy context-dependent blending of schemas. Fuzzy rules are formed using two main criteria into account: the first criterion reasons out the context dependent activity of a schema for achieving goal and the second criterion reasons out cooperative activity of strategic schemas with high priority reactive schemas. Comprehensive results validate that the proposed technique eliminates the existing drawbacks of motor schema approaches available in literature and provides collision free goal oriented robot navigation.     

基于行为的实时路径规划

针对室内移动机器人导航要求,开发了以二维激光雷达作为探测环境的传感器,基于4个反应式行为,设计了一种简单的实时路径规划算法.避障行为使机器人穿过狭小通道,或者在某些障碍物环境下绕出狭窄区域;接近行为使机器人顺着障碍物前进直到开阔地带;搜寻行为使机器人不断朝向目标运动;线性行为使机器人到达目标点.机器人表现出很强的路径寻找能力,并且不需要定位信息.仿真实验表明该算法速度快,实时性好,路径平滑无震荡,实现了有效避障.     

Path planning of wheeled mobile robot with simultaneous free space locating capability

This paper presents a hybrid path planning algorithm for the design of autonomous vehicles such as mobile robots. The hybrid planner is based on Potential Field method and Voronoi Diagram approach and is represented with the ability of concurrent navigation and map building. The system controller (Look-ahead Control) with the Potential Field method guarantees the robot generate a smooth and safe path to an expected position. The Voronoi Diagram approach is adopted for the purpose of helping the mobile robot to avoid being trapped by concave environment while exploring a route to a target. This approach allows the mobile robot to accomplish an autonomous navigation task with only an essential exploration between a start and goal position. Based on the existing topological map the mobile robot is able to construct sub-goals between predefined start and goal, and follows a smooth and safe trajectory in a flexible manner when stationary and moving obstacles co-exist.     

Concurrent Map Building and Behavior Learning for Navigation in Unknown Environments

In this paper, we report a robust and low-cost navigation algorithm for an unknown environment based on integration of a grid-based map building algorithm with behavior learning. The study focuses on mobile robots that utilize ultrasonic sensors as their prime interface with the outside world. The proposed algorithm takes into account environmental information to augment the readings from the low angular accuracy sonar measurements for behavior learning. The environmental information is obtained by an online grid-based map learning design that is concurrently operating with the behavior learning algorithm. The proposed algorithm is implemented and tested on an in-house-built mobile robot, and its performance is verified through online navigation in an indoor environment.     

Intelligent gaze control for vision-guided humanoid walking: methodological aspects

This article presents methodological aspects of a scheme for visually guided humanoid robot navigation. The proposed approach is based on the maximization of the predicted visual information. For the information management a coupled hybrid Extended Kalman Filter is employed. Specific view direction evaluation strategies for conflicting objectives of different nature such as obstacle avoidance and self-localization, have to be weighted and pursued in parallel. A combination of both objectives shows the task dependence of the gazing strategy. A major goal of this work is to formalize and implement a decision making strategy in order to achieve an intelligent task-oriented active vision system for a biped walking robot. Simulation results based on experimental experience with real biped robots demonstrate the relevance of the approach.     

Sensor-based fuzzy navigation of an autonomous mobile robot in an indoor environment

The work presented in this paper deals with the problem of navigating a mobile robot either in an unknown indoor environment or in a partially known one. A navigation method based on the combination of elementary behaviors has been developed for an unknown environment. Most of these behaviors are achieved by means of fuzzy inference systems. The proposed navigator combines two types of obstacle avoidance behaviors, one for the convex obstacles and one for the concave ones. In the case of a partially known environment, a hybrid method is used to exploit the advantages of global and local navigation strategies. The coordination of these strategies is based on a fuzzy inference system that involves an on-line comparison between the real scene and a memorized one. Both methods have been implemented on the miniature mobile robot Khepera^® which is equipped with rough sensors. The good results obtained illustrate the robustness of a fuzzy logic approach with regard to sensor imperfections.     

基于人工神经元网络的移动机器人导航研究

本文采用人工神经元网络模型，提出了一个基于行为的移动机人导航方法，并在仿真实验系统上进行了实验研究，取得了令人满意的结果，这是一种前有前途的导航方法。     

Reactive navigation of underwater mobile robot using ANFIS approach in a manifold manner

Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot’s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system (ANFIS) has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace. An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.     

Robust omnidirectional mobile robot topological navigation system using omnidirectional vision

Robust topological navigation strategy for omnidirectional mobile robot using an omnidirectional camera is described. The navigation system is composed of on-line and off-line stages. During the off-line learning stage, the robot performs paths based on motion model about omnidirectional motion structure and records a set of ordered key images from omnidirectional camera. From this sequence a topological map is built based on the probabilistic technique and the loop closure detection algorithm, which can deal with the perceptual aliasing problem in mapping process. Each topological node provides a set of omnidirectional images characterized by geometrical affine and scale invariant keypoints combined with GPU implementation. Given a topological node as a target, the robot navigation mission is a concatenation of topological node subsets. In the on-line navigation stage, the robot hierarchical localizes itself to the most likely node through the robust probability distribution global localization algorithm, and estimates the relative robot pose in topological node with an effective solution to the classical five-point relative pose estimation algorithm. Then the robot is controlled by a vision based control law adapted to omnidirectional cameras to follow the visual path. Experiment results carried out with a real robot in an indoor environment show the performance of the proposed method.