Abstract: Describes the technologies of cooperative driving with automated vehicles and intervehicle communications in the Demo 2000 cooperative driving. Cooperative driving, aiming at the compatibility of safety and efficiency of road traffic, means that automated vehicles drive by forming a flexible platoon over a couple of lanes with a short intervehicle distance while performing lane changing, merging, and leaving the platoon. The vehicles for the demonstration are equipped with automated lateral and longitudinal control functions with localization data by the differential global positioning system (DGPS) and the intervehicle communication function with 5.8-GHz dedicated short range communication (DSRC) designed for the dedicated use in the demonstration. In order to show the feasibility and potential of the technologies, the demonstration was held in November 2000, on a test track with five automated vehicles. The scenario included stop and go, platooning, merging, and obstacle avoidance.

Abstract: Robotic applications are expanding into dynamic, unstructured, and populated environments. Mechanisms specifically designed to address the challenges arising in these environments, such as humanoid robots, exhibit high kinematic complexity. This creates the need for new algorithmic approaches to motion generation, capable of performing task execution and real-time obstacle avoidance in high-dimensional configuration spaces. The elastic strip framework presented in this paper enables the execution of a previously planned motion in a dynamic environment for robots with many degrees of freedom. To modify a motion in reaction to changes in the environment, real-time obstacle avoidance is combined with desired posture behavior. The modification of a motion can be performed in a task-consistent manner, leaving task execution unaffected by obstacle avoidance and posture behavior. The elastic strip framework also encompasses methods to suspend task behavior when its execution becomes inconsistent with other const...

Abstract: We present an approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals. Given a geometric model of the environment and a statically-stable desired posture, we search the configuration space of the robot for a collision-free path that simultaneously satisfies dynamic balance constraints. We adapt existing randomized path planning techniques by imposing balance constraints on incremental search motions in order to maintain the overall dynamic stability of the final path. A dynamics filtering function that constrains the ZMP (zero moment point) trajectory is used as a post-processing step to transform statically-stable, collision-free paths into dynamically-stable, collision-free trajectories for the entire body. Although we have focused our experiments on biped robots with a humanoid shape, the method generally applies to any robot subject to balance constraints (legged or not). The algorithm is presented along with computed examples using both simulated and real humanoid robots.

Abstract: We consider a class of 2D optimal path-planning problems for unmanned air vehicles (UAVs) with kinematic and tactical constraints. The existence of an optimal path class satisfying the UAV kinematic constraints and vector calculus are exploited to reduce this class of optimal path-planning problems to a parameter optimization problem. Illustrative tactical constraints arising in target touring and obstacle avoidance problems are considered. A necessary condition for optimal path planning in the presence of tactical constraints is characterized.. An efficient numerical algorithm using simulated dynamics is developed to enforce the optimality criterion. The proposed optimal path planner can handle multiple tactical constraints. An illustrative numerical simulation demonstrates the efficacy of our approach.

Abstract: This paper describes new results with a Reactive Shared-Control system that enables a semi-autonomous navigation of a wheelchair in unknown and dynamic environments. The purpose of the reactive shared controller is to assist wheelchair users providing an easier and safer navigation. It is designed as a fuzzy-logic controller and follows a behaviour-based architecture. The implemented behaviours are three: intelligent obstacle avoidance, collision detection and contour following. Intelligent obstacle avoidance blends user commands, from voice or joystick, with an obstacle avoidance behaviour. Therefore, the user and the vehicle share the control of the wheelchair. The reactive shared control was tested on the RobChair powered wheelchair prototype [6] equipped with a set of ranging sensors. Experimental results are presented demonstrating the effectiveness of the controller.

Abstract: Whenever robots are installed in populated environments, they need appropriate techniques to avoid collisions with unexpected obstacles. Over the past years several reactive techniques have been developed that use heuristic evaluation functions to choose appropriate actions whenever a robot encounters an unforeseen obstacle. Whereas the majority of these approaches determines only the next steering command, some additionally consider sequences of possible poses. However, they generally do not consider sequences of actions in the velocity space. Accordingly, these methods are not able to slow down the robot early enough before it has to enter a narrow passage. In this paper we present a new approach that integrates path planning with sensor-based collision avoidance. Our algorithm simultaneously considers the robot's pose and velocities during the planning process. We employ different strategies to deal with the huge state space that has to be explored. Our method has been implemented and tested on real robots and in simulation runs. Extensive experiments demonstrate that our technique can reliably control mobile robots moving at high speeds.

Abstract: We address the problem of teleoperating a mobile robot using shared autonomy: an on-board controller performs obstacle avoidance while the operator uses the manipulandum of a haptic probe to designate the desired speed and rate of turn. Sensors on the robot are used to measure obstacle range information. We describe a strategy to convert such range information into forces, which are reflected to the operator's hand, via the haptic probe. This haptic information provides feedback to the operator in addition to imagery from a front-facing camera mounted on the mobile robot. Extensive experiments with a user population show that the added haptic feedback significantly improves operator performance in several ways (reduced collisions, increased minimum distance between the robot and obstacles) without a significant increase in navigation time.

Abstract: Applications in mobile manipulation require sophisticated motion execution skills to address issues like redundancy resolution, reactive obstacle avoidance, and transitioning between different motion behaviors. The elastic strip framework is an approach to reactive motion generation providing an integrated solution to these problems. Novel techniques within the elastic strip framework are presented, allowing task-consistent obstacle avoidance and task-consistent motion behavior. General transition criteria and methods are presented, permitting the suspension and resumption of task execution to ensure other desired motion behavior, such as obstacle avoidance. Task execution has to be suspended when kinematic constraints or changes in the environment render task-consistent motion behavior infeasible. Task execution is resumed as soon as it is consistent with other desired motion behaviors.

Abstract: A mobile robot should be designed to navigate with collision avoidance capability in the real world, flexibly coping with the changing environment. In this paper, a novel limit-cycle navigation method is proposed for a fast mobile robot using the limit-cycle characteristics of a 2nd-order nonlinear function. It can be applied to the robot operating in a dynamically changing environment, such as in a robot soccer system. By adjusting the radius of the motion circle and the direction of obstacle avoidance, the navigation method proposed enables a robot to maneuver smoothly towards any desired destination. Simulations and real experiments using a robot soccer system demonstrate the merits and practical applicability of the proposed method. 11 12 13 14 15 16 17 © 2002 Published by Elsevier Science B.V. 18

Abstract: We propose a method for learning models of people's motion behaviors in an indoor environment. As people move through their environments, they do not move randomly. Instead, they often engage in typical motion patterns, related to specific locations that they might be interested in approaching and specific trajectories that they might follow in doing so. Knowledge about such patterns may enable a mobile robot to develop improved people following and obstacle avoidance skills. This paper proposes an algorithm that learns collections of typical trajectories that characterize a person's motion patterns. Data, recorded by mobile robots equipped with laser range finders, is clustered into different types of motion using the popular expectation maximization algorithm, while simultaneously learning multiple motion patterns. Experimental results, obtained using data collected in a domestic residence and in an office building, illustrate that highly predictive models of human motion patterns can be learned.

Abstract: This paper considers the problem of a robot navigating in a crowded or congested environment. A robot operating in such an environment can get easily blocked by moving humans and other objects. To deal with this problem it is proposed to attempt to predict the motion trajectory of humans and obstacles. Two kinds of prediction are considered: short-term and long-term. The short-term prediction refers to the one-step ahead prediction and the long-term to the prediction of the final destination point of the obstacle's movement. The robot movement is controlled by a partially observable Markov decision process (POMDP). POMDPs are utilized because of their ability to model information about the robot's location and sensory information in a probabilistic manner. The solution of a POMDP is computationally expensive and thus a hierarchical representation of POMDPs is used.

Abstract: The dynamical systems theory is used here as a theoretical language and tool to design a distributed control architecture that generates navigation in formation, integrated with obstacle avoidance, for a team of three autonomous robots. In this approach the level of modeling is at the level of behaviors. A "dynamics" of behavior is defined over a state-space of behavioral variables. The environment is also modeled in these terms by representing task constraints as attractors (i.e., asymptotically stable states) or repellers (i.e., unstable states) of behavioral dynamics. For each robot attractors and repellers are combined into a vector field that governs the behavior. The resulting dynamical systems that generate the behavior of the robots are nonlinear. Computer simulations support the validity of our dynamic model architectures.

Abstract: In this paper we present an approach to obstacle avoidance and local path planning for polygonal robots. It decomposes the task into a model stage and a planning stage. The model stage accounts for robot shape and dynamics using a reduced dynamic window. The planning stage produces collision-free local paths with a velocity profile. We present an analytical solution to the distance to collision problem for polygonal robots, avoiding thus the use of look-up tables. The approach has been tested in simulation and on two non-holonomic rectangular robots where a cycle time of 10 Hz was reached under full CPU load. During a long-term experiment over 5 km travel distance, the method demonstrated its practicability.

Abstract: In this paper, a cooperative sweeping strategy of complete coverage path planning for multiple cleaning robots in a time-varying and unstructured environment is proposed using biologically inspired neural networks. Cleaning tasks require a special kind of trajectory being able to cover every unoccupied area in specified cleaning environments, which is an essential issue for cleaning robots and many other robotic applications. Multiple robots can improve the work capacity, share the cleaning tasks, and reduce the time to complete sweeping tasks. In the proposed model, the dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each cleaning robot treats the other robots as moving obstacles. Multiple cleaning robots can cooperate to achieve a common sweeping goal effectively. The robot path is autonomously generated from the dynamic activity landscape of the neural network, the previous robot location and the other robot locations. The proposed model algorithm is computationally efficient. The feasibility is validated by simulation studies on three cases of two cooperating cleaning robots.

Abstract: We propose a method for learning models of people's motion behaviors in indoor environments. As people move through their environments, they do not move randomly. Instead, they often engage in typical motion patterns, related to specific locations that they might be interested in approaching and specific trajectories that they might follow in doing so. Knowledge about such patterns may enable a mobile robot to develop improved people following and obstacle avoidance skills. This paper proposes an algorithm that learns collections of typical trajectories that characterize a person's motion patterns. Data, recorded by mobile robots equipped with laser-range finders, is clustered into different types of motion using the popular expectation maximization algorithm, while simultaneously learning multiple motion patterns. Experimental results, obtained using data collected in a domestic residence and in an office building, illustrate that highly predictive models of human motion patterns can be learned.

Abstract: In this paper we present an approach to obstacle avoidance and local path planning for polygonal robots. It decomposes the task into a model stage and a planning stage. The model stage accounts for robot shape and dynamics using a reduced dynamic window. The planning stage produces collision-free local paths with a velocity profile. We present an analytical solution to the distance to collision problem for polygonal robots, avoiding thus the use of look-up tables. The approach has been tested in simulation and on two non-holonomic rectangular robots where a cycle time of 10 Hz was reached under full CPU load. During a longterm experiment over 5 km travel distance, the method demonstrated its practicability.

Abstract: One of the basic issues in the navigation of autonomous mobile robots is the obstacle avoidance task that is commonly achieved using a reactive control paradigm where a local mapping from perceived states to actions is acquired. A control strategy with learning capabilities in an unknown environment can be obtained using reinforcement learning where the learning agent is given only sparse reward information. This credit assignment problem includes both temporal and structural aspects. While the temporal credit assignment problem is solved using core elements of the reinforcement learning agent, solution of the structural credit assignment problem requires an appropriate internal state space representation of the environment. In this paper, a discrete coding of the input space using a neural network structure is presented as opposed to the commonly used continuous internal representation. This enables a faster and more efficient convergence of the reinforcement learning process.

Abstract: Motion planning, or goal-oriented, context-sensitive, intelligent control is essential if an agent is to act in a useful manner.This paper suggests a new class of motion planners that can mark a constrained trajectory to a target zone in an environment that need not necessarily be a priori known. The novelty of the suggested planner lies in its ability to enforce region avoidance and direction satisfaction constraints jointly. To the best of the authors’ knowledge, this is the first time that directional constraints have been addressed in the motion planning literature. To build such a planner, the potential field approach is used for inducing the control action.In addition, to cope with the presence of the above constraints(in particular, the directional constraints), a new type of potential field, called the nonlinear anisotropic harmonic potential field, is suggested. The planner has applications in traffic management and operations research among others. Development of the approach, proofs of correctness, and simulation results are supplied.

Abstract: We present a stereo-based obstacle avoidance system for mobile vehicles. The system operates in three steps. First, it models the surface geometry of the supporting surface and removes the supporting surface from the scene. Next, it segments the remaining stereo disparities into connected components in image and disparity space. Finally, it projects the resulting connected components onto the supporting surface and plans a path around them. One interesting aspect of this system is that it can detect both positive and "negative" obstacles (e.g. stairways) in its path. The algorithms we have developed have been implemented on a mobile robot equipped with a real-time stereo system. We present experimental results on indoor environments with planar supporting surfaces that show the algorithms to be both fast and robust.

Abstract: An area-covering operation is a kind of complete coverage path planning, which requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum, robots, painter robots, land mine detectors, lawn mowers, and windows cleaners. In this paper, a novel biologically inspired neural network approach is proposed for complete coverage path planning with obstacle avoidance of a cleaning robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The proposed model algorithm is computationally efficient, and can also deal with changing environment. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot path.

Abstract: Robot soccer is a challenging platform for multi-agent research, involving topics such as real-time image processing and control, robot path planning, obstacle avoidance and machine learning. The robot soccer game presents an uncertain and dynamic environment for cooperating agents. Dynamic role switching and formation control are crucial for a successful game. The fuzzy logic based strategy described in the paper employs an arbiter which assigns a robot to shoot or pass the ball.

Abstract: This paper describes a new general framework for the action of an automated driver (or driver model) to provide the control of longitudinal and lateral dynamics of a road vehicle. The context of the problem is assumed to be in high-speed competitive driving, as in motor racing, where the requirement is for maximum possible speed along a track, making use of a reference path (racing line) but with the capacity for obstacle avoidance and recovery from large excursions. While not necessarily representative of a human driver, the analysis provides worthwhile insight into the nature of the driving task and offers a new approach for vehicle lateral and longitudinal control; it also has applications in less demanding applications such as Advanced Cruise Control systems. As is common in the literature, the driving task is broken down into two distinct subtasks: path planning and local feedback control. In the first of these tasks, an essentially geometric approach is taken here, which makes use of a vector field analysis. At each location x the automated driver is to prescribe a vector w for the desired vehicle mass centre velocity; the spatial distribution and global properties of w(x) provide essential information for stability analysis, as well as control reference. The resulting vector field is considered in the context of limited friction and limited mass centre accelerations, leading to constraints on �w. Provided such constraints are satisfied, and using suitable adaptation of w(x) when required, it is shown that feedback control can be applied to guarantee stable asymptotic tracking of a reference path, even under limit handling conditions. A specific implementation of the method is included, using dual non-linear SISO (single-input single-output) controllers.

Abstract: The dynamic window approach is a well known navigation scheme developed by Fox et. al. (1997) and extended by Brock and Khatib (1999). It is safe by construction and has been shown to perform very efficiently in experimental setups. However, one can construct examples where the proposed scheme fails to attain the goal configuration. What has been lacking is a theoretical treatment of the algorithm's convergence properties. A first step towards such a treatment was previously presented by the authors (2002). Here we continue that work with a computationally tractable algorithm resulting from a careful discretization of the optimal control problem of the previous paper and a way to construct a continuous navigation function. Inspired by the similarities between the dynamic window approach and the control Lyapunov function and receding horizon control synthesis put forth by Primbs et. al. (1999) we propose a version of the dynamic window approach that is tractable and provably convergent.

Abstract: An adaptive-resonance theory (ART)-based fuzzy controller is presented for the adaptive navigation of a quadruped robot in cluttered environments, by incorporating the capability of ART in stable category recognition into fuzzy-logic control for selecting the adequate rule base. The environment category and navigation mechanism are first described for the quadruped robot. The ART-based fuzzy controller, including an ART-based environment recognizer, a comparer, combined rule bases, and a fuzzy inferring mechanism, is then introduced for the purpose of the adaptive navigation of the quadruped robot. Unlike classical/conventional adaptive-fuzzy controllers, the present adaptive-control scheme is implemented by the adaptive selection of fuzzy-rule base in response to changes of the robot environment, which can be categorized and recognized by the proposed environment recognizer. The results of simulation and experiment show that the adaptive-fuzzy controller is effective.

Abstract: This paper describes the development of a genetic algorithm (GA) based path-planning software for local obstacle avoidance. The GA uses a novel encoding technique, which was developed to optimize the information content of the GA structure. Simulation results were used to further optimize the developed software and determine its optimum field of operation. The results show that the GA finds valid solutions to the path-planning problem within reasonable time and can therefore be used for real world applications.

Abstract: With the increase in the number of senior citizens, there is a growing demand for human-friendly wheelchairs as mobility aids. One of the main issues in the robotic wheelchair research is autonomous obstacle avoidance for safety. However, this is difficult because most of moving obstacles in the real world are human beings. They sometimes change their motion abruptly. This paper presents an intelligent wheelchair that can avoid collision with such human pedestrians safely and comfortably for each other. We assume that the information whether or not a pedestrian has noticed the wheelchair and which direction he/she wants to go can appear in the face direction. Thus our intelligent wheelchair is continuously observing the pedestrian's face in its front area, realizing smooth passing by changing its collision avoidance strategy based on the face information and the range data from the ultrasonic sensors. Experimental results show the effectiveness and comfortableness of the proposed method.