Showing papers on "Obstacle avoidance published in 1997"

Where and when do we look as we approach and step over an obstacle in the travel path

Abstract: Spatio-temporal gaze behaviour patterns were analysed as normal participants wearing a mobile eye tracker approached and stepped over obstacles of varying height in the travel path. We examined the frequency and duration of three types of gaze fixation with respect to the participants' stepping patterns: obstacle fixation (ObsFix); travel fixation (TravFix) (when the gaze is stable and travelling at the speed of whole body) and fixation in the 4-6m region (Fix4-6). During the approach phase to the obstacle, participants fixated on the obstacle for approximately 20% of the travel time. Only Fix4-6 duration was modulated as a function of obstacle height by regulating the frequency and reflected the increased time needed for detection of the small low contrast obstacle in the travel path. Frequency of ObsFix increased significantly as a function of obstacle height and reflected visuo-motor transformation needed for limb elevation control. Participants did not fixate on the obstacle as they were stepping over, but did the planning in the steps before. TravFix duration and frequency was constant while Fix4-6 duration was higher in the step before and step over the obstacle reflecting visual search of the landing area for the lead limb following obstacle avoidance. These results clearly show that obstacle information provided by vision is used in a feed-forward rather than on-line control mode to regulate locomotion. Information about self-motion acquired from optic flow during TravFix can be used to control velocity of locomotion.

The autonomous mobile robot SENARIO: a sensor aided intelligent navigation system for powered wheelchairs

Abstract: The SENARIO project is develoing a sensor-aided intelligent navigation system that provides high-level navigational aid to users of powered wheelchairs. The authors discuss new and improved technologies developed within SENARIO concerning task/path planning, sensing and positioning for indoor mobile robots as well as user interface issues. The autonomous mobile robot SENARIO, supports semi- or fully autonomous navigation. In semi-autonomous mode the system accepts typical motion commands through a voice-activated or standard joystick interface and supports robot motion with obstacle/collision avoidance features. Fully autonomous mode is a superset of semi-autonomous mode with the additional ability to execute autonomously high-level go-to-goal commands. At its current stage, the project has succeeded in fully supporting semi-autonomous navigation, while experiments on the fully autonomous mode are very encouraging.

Visually-guided obstacle avoidance in unstructured environments

Abstract: This paper presents an autonomous vision-based obstacle avoidance system. The system consists of three independent vision modules for obstacle detection, each of which is computationally simple and uses a different criterion for detection purposes. These criteria are based on brightness gradients, RGB (red, green, blue) color, and HSV (hue, saturation, value) color, respectively. Selection of which modules are used to command the robot proceeds exclusively from the outputs of the modules themselves. The system is implemented on a small monocular mobile robot and uses very lour resolution images. It has been tested for over 200 hours in diverse environments.

Obstacle avoidance and a perturbation sensitivity model for motor planning

Abstract: A novel obstacle avoidance paradigm was used to investigate the planning of human reaching movements. We explored whether the CNS plans arm movements based entirely on the visual space kinematics of the movements, or whether the planning process incorporates specific details of the biomechanical plant to optimize the trajectory plan. Participants reached around an obstacle, the tip of which remained fixed in space throughout the experiment. When the obstacle and the start and target locations were rotated about the tip of the obstacle, the visually specified task constraints retained a rotational symmetry. If movements are planned in visual space, as indicated from a variety of studies on planar point-to-point movements, the resulting trajectories should also be rotationally symmetric across trials. However, systematic variations in movement path were observed as the orientation of the obstacle was changed. These path asymmetries can be accounted for by a class of models in which the planner reduces the likelihood of collision with the obstacle by taking into account the anisotropic sensitivity of the arm to external perturbations or uncertainty in joint level control or proprioception. The model that best matches the experimental results uses planning criteria based on the inertial properties of the arm.

Limb coordination system for interactive computer animation of articulated characters with blended motion data

Abstract: On-line computational methods are used for animating limb movements of articulated characters by solving associated forward and inverse kinematics problems in real time subject to multiple goals and constraints. The methods use fully interactive goal-directed behaviors, such as bipedal walking, through simultaneous satisfaction of position, alignment, posture, balance, obstacle avoidance, and joint limitation constraints. Goal-based motion primitives, called synergies (22, 24, 26, 28, 30), coordinate sets of joint movements which separately attempt to satisfy each of the above constraints (18). Recorded motion data is combined with interactive control techniques to manipulate the animation of articulated figures. Non-interactive motion capture and keyframe data, representing examples of desired character movements, are accommodated in the present animation system.

A path space approach to nonholonomic motion planning in the presence of obstacles

Abstract: This paper presents an algorithm for finding a kinematically feasible path for a nonholonomic system in the presence of obstacles. We first consider the path planning problem without obstacles by transforming it into a nonlinear least squares problem in an augmented space which is then iteratively solved. Obstacle avoidance is included as inequality constraints. Exterior penalty functions are used to convert the inequality constraints Into equality constraints. Then the same nonlinear least squares approach is applied. We demonstrate the efficacy of the approach by solving some challenging problems, including a tractor-trailer and a tractor with a steerable trailer backing in a loading dock. These examples demonstrate the performance of the algorithm in the presence of obstacles and steering and jackknife angle constraints.

Minimum effort inverse kinematics for redundant manipulators

Abstract: This paper investigates the use of an infinity norm in formulating the optimization measures for computing the inverse kinematics of redundant arms. The infinity norm of a vector is its maximum absolute value component and hence its minimization implies the determination of a minimum effort solution as opposed to the minimum-energy criterion associated with the Euclidean norm. In applications where individual magnitudes of the vector components are of concern, this norm represents the physical requirements more closely than does the Euclidean norm. We first study the minimization of the infinity-norm of the joint velocity vector itself, and discuss its physical interpretation. Next, a new method of optimizing a subtask criterion, defined using the infinity-norm, to perform additional tasks such as obstacle avoidance or joint limit avoidance is introduced. Simulations illustrating these methods and comparing the results with the Euclidean norm solutions are presented.

God Save the Red Queen! Competition in Co-Evolutionary Robotics

Abstract: In the simplest scenario of two co-evolving populations in competition with each other, fitness progress is achieved at disadvantage of the other population's fitness. The everchanging fitness landscape caused by the competing species (named the "Red Queen effect") makes the system dynamics more complex, but it also provides a set of advantages with respect to single-population evolution. Here we present results from an experiment with two mobile robots, a predator equipped with vision and a much faster prey with simpler sensors. Without any effort in fitness design, a set of interesting behaviors emerged in relatively short time, such as obstacle avoidance, straight navigation, visual tracking, object discrimination (robot vs. wall), object following, and others. Although such experiments cannot yet be performed in real-time on populations of robots for technical reasons, the approach seems promising.

Interactive motion data animation system

Abstract: Recorded motion data is combined with interactive control techniques to manipulate the animation of articulated figures. The methods enable computer animated characters to produce fully interactive goal-directed behaviors, such as bipedal walking, through simultaneous satisfaction of position, alignment, posture, balance, obstacle avoidance, and joint limitation constraints while retaining qualitative characteristics of the original non-interactive motion data. Goal-based motion primitives, called synergies, are used to coordinate sets of joint movements that attempt to satisfy each of the above constraints. Non-interactive motion capture and keyframe data, representing examples of desired character movements, are accommodated in the present animation system in three ways: 1) direct approach--non-interactive motion data used directly to specify desired body posture synergy goals as a function of time, 2) hybrid approach--non-interactive motion data and program control commands blended to specify elements of desired position, alignment and/or balance synergy goals as a function of time, and 3) template approach--non-interactive motion data used to auto-tune adjustable parameters, enabling program control commands to generate fully interactive movements that qualitatively resemble the non-interactive motion data. The disclosed methods allow libraries of pre-configured goal-directed behaviors, such as reaching, sitting, walking, jumping, etc., to be constructed and used to animate a wide variety of characters.

A wall-following method for escaping local minima in potential field based motion planning

Abstract: A wall-following method for escaping local minima encountered by the potential field based motion planning method used in real-time obstacle avoidance is presented. The new algorithm switches to a wall-following control mode when the robot falls into a local minimum. A switches back to the potential field guided control mode when a certain condition is met. A simple switch condition derived from monitoring the distance from the robot's position to the goal position is shown to be effective in escaping local minima in typical laboratory environments. A provision is built into the algorithm, allowing the robot to follow a wall in a different direction if the first attempt fails. The new algorithm is implemented on a Nomad 200 mobile robot. Simulation and experimental results are presented to demonstrate the usefulness of the method.

Obstacle detection system

Abstract: A system for alerting a driver of a vehicle of the presence of an obstacle in a track of the vehicle, comprising a sensor mounted on the vehicle for producing at least one sensor signal representative of a predetermined field of view of the track in front of the vehicle, and an obstacle detection device coupled to the sensor for processing the at least one sensor signal produced thereby so as to detect an obstacle in the track and produce an obstacle detect signal consequent thereto. An obstacle avoidance device is mounted in the vehicle and coupled to the obstacle detection device and is responsive to the obstacle detect signal for producing an obstacle avoidance signal. According to a preferred embodiment, the track is a rail track, the vehicle is a railway engine and the sensor includes a video camera for imaging the track. The resulting image is processed so as to detect a potential obstacle on the tracks allowing the brakes to be applied either manually or automatically.

Obstacle avoidance processing method for vehicles using an automated highway system

Abstract: A method for use in vehicles using lanes of an automated highway to avoid collisions with obstacles. A set of decision rules are embodied in a processor that are used in each vehicle to determine whether affected vehicles should change lanes or brake to minimize the expected impact with an obstacle. The presence of an obstacle in one of the lanes is detected by a lead vehicle or by sensors on the highway. The processor in the lead vehicle, or at a traffic management facility, probabilistically estimates the position of the obstacle at each of a plurality of times subsequent to detection. At each of the times, a course of action for all affected vehicles is determined by the processor in the lead vehicle based upon the best current estimate of position of the obstacle, knowledge of any previous action that has been taken, positions and velocities of the affected vehicles, and the geometry of the highway. At each of the times, each of the affected vehicles is commanded to perform an avoidance maneuver that is coordinated with the other affected vehicles, wherein affected vehicles brake and change lanes to avoid collision with the obstacle and with other vehicles. At each of the times, commands are sent to each affected vehicle that cause them to perform hard braking, light braking, maintain speed, resume previous speed, perform a lane change, abort a lane change or create a gap, so as to minimize the impact of the vehicles with the obstacle.

A genetic approach to motion planning of redundant mobile manipulator systems considering safety and configuration

Abstract: This paper presents a genetic algorithm approach to multi-criteria motion planning of a mobile manipulator system considering position and configuration optimisation. Travelling distance and path safety are considered in planning the motion of the mobile system. A wave front expansion algorithm is used to build the numerical potential fields for both the goal and obstacles by representing the workspace as a grid. The unsafeness of a grid point is defined as the numerical potential produced by obstacles. For multi-criteria position and configuration optimisation, obstacle avoidance, least torque norm, manipulability and torque distribution are considered. The emphasis is put on using genetic algorithms to search for global optimum and solve the minimax problem for torque distribution. Various simulation results from two examples show that the proposed genetic algorithm approach performs better than conventional methods.

Xavier: experience with a layered robot architecture

Abstract: Office delivery robots have to perform many tasks such as picking up and delivering mail or faxes, returning library books, and getting coffee. They have to determine the order in which to visit locations, plan paths to those locations, follow paths reliably, and avoid static and dynamic obstacles in the process. Reliability and efficiency are key issues in the design of such autonomous robot systems. They must deal reliably with noisy sensors and actuators and with incomplete knowledge of the environment. They must also act efficiently, in real time, to deal with dynamic situations. To achieve these objectives, we have developed a robot architecture that is composed of four layers: obstacle avoidance, navigation, path planning, and task planning. The layers are independent, communicating processes that are always active, processing sensory data and status information to update their decisions and actions. A version of our robot architecture has been in nearly daily use in our building since December 1995. As of January 1997, the robot has traveled more than 110 kilometers (65 miles) in service of over 2500 navigation requests that were specified using our World Wide Web interface.

Obstacle avoidance inverse kinematics solution of redundant robots by neural networks

Abstract: This paper investigates the neural network approach to solve the inverse kinematics problem of redundant robot manipulators in an environment with obstacles. The solution technique proposed requires only the knowledge of the robot forward kinematics functions and the neural network is trained in the inverse modeling manner. Training algorithms for both the obstacle free case and the obstacle avoidance case are developed. For the obstacle free case, sample points can be selected in the work space as training patterns for the neural network. For the obstacle avoidance case, the training algorithm is augmented with a distance penalty function. A ball-covering object modeling technique is employed to calculate the distances between the robot links and the objects in the work space. It is shown that this technique is very computationally efficient. Extensive simulation results are presented to illustrate the success of the proposed solution schemes. Experimental results performed on a PUMA 560 robot manipulator is also presented.

Adaptive navigation of mobile robots with obstacle avoidance

Abstract: A local navigation technique with obstacle avoidance, called adaptive navigation, is proposed for mobile robots in which the dynamics of the robot are taken into consideration. The only information needed about the local environment is the distance between the robot and the obstacles in three specified directions. The navigation law is a first-order differential equation and navigation to the goal and obstacle avoidance are achieved by switching the direction angle of the robot. The effectiveness of the technique is demonstrated by means of simulation examples.

Limb coordination system for interactive computer animation of articulated characters

Abstract: A method and apparatus for interactively controlling and coordinating the limb movements of computer-generated articulated characters with an arbitrary number of joints. On-line computational methods are used for animating limb movements of articulated characters by solving associated forward and inverse kinematics problems in real time subject to multiple goals and constraints. The methods provide computer animated characters with fully interactive goal-directed behaviors, such as bipedal walking, through simultaneous satisfaction of position, alignment, posture, balance, obstacle avoidance, and joint limitation constraints. Goal-based motion primitives, called synergies, coordinate sets of joint movements which separately attempt to satisfy each of the above constraints. The present methods adapt character movements on-line to accommodate uneven terrain, body modifications, or changes in the environment by automatically transforming and producing joint rotations relative to the instantaneous point of contact of the body with the world. Libraries of pre-configured goal-directed behaviors, such as reaching, sitting, walking, jumping, etc., can be constructed and used to animate a wide variety of characters.

Genetic algorithm based path planning and dynamic obstacle avoidance of mobile robots

Abstract: A simple path planning scheme is proposed for navigation of mobile robots while avoiding obstacles. In generating the goal directed dynamic path, the path planning scheme uses a genetic search algorithm whose coding technique speeds up the execution of genetic search for fast path generation. The fitness value of the generated paths is evaluated in terms of the safety from the obstructing dynamic objects and the distance to the goal position by the genetic algorithm. The execution time of genetic search is further accelerated by projecting the two dimensional data to one dimensional ones to reduce the size of search space.

An Approach to Rough Terrain Autonomous Mobility

Abstract: Off-road autonomous navigation is one of the most difficult automation challenges from the point of view of constraints on mobility, speed of motion, lack of environmental structure, density of hazards, and typical lack of prior information. This paper describes an autonomous navigation software system for outdoor vehicles which includes perception, mapping, obstacle detection and avoidance, and goal seeking. It has been used on sev- eral vehicle testbeds including autonomous HMMWV's and planetary rover prototypes. To date, it has achieved speeds of 15 km/hr and excursions of 15 km. We introduce algorithms for optimal processing and computational stabilization of range imagery for terrain map- ping purposes. We formulate the problem of trajectory generation as one of predictive control searching trajectories in command space. We also formulate the problem of goal arbitration in local autonomous mobility as an optimal control problem. We emphasize the modeling of vehicles in state space form. The resulting high fidelity models sta- bilize coordinated control of a high speed vehicle for both obstacle avoidance and goal seeking purposes. An intermediate predictive control layer is introduced between the typical high-level strategic or artificial intelli- gence layer and the typical low-level servo control layer. This layer incorporates some deliberation, and some envi- ronmental mapping as do deliberative AI planners, yet it also emphasizes the real-time aspects of the problem as do minimalist reactive architectures.

Vision-based navigation of mobile robot with obstacle avoidance by single camera vision and ultrasonic sensing

Abstract: This paper describes a vision-based navigation method in an indoor environment for an autonomous mobile robot which can avoid obstacles. In this method, the self-localization of the robot is done with a mode-based vision system, and a non-stop navigation is realized by a retroactive position correction system. Stationary obstacles are avoided with single-camera vision and moving obstacles are detected with ultrasonic sensors. We report on experiments in a hallway using the YAMABICO robot.

Robust path following control for wheeled robots via sliding mode techniques

Abstract: The techniques of filtering and merging data coming from several sensors allow to localize a mobile robot in its environment with a precision which can be evaluated. However, as the localization error cannot be neglected, the design of robust closed-loop controller for wheeled robots constitutes a difficult problem. We present here a path following feedback controller robust with respect to localization error. The model is a dynamic extension of the usual kinematic model of a car, in the sense that the path curvature error is considered as a new state variable. The control inputs are respectively the linear velocity and the derivative of the curvature. We determine a variable structure control with sliding mode to stabilize the vehicle's motion around the reference path in the nominal case. Then, we prove that the system remains stable when the state feedback is computed from the estimated values instead of the exact ones. We show that the regulation error is contained in a compact attractive domain when the system has reached its steady state. From this domain, one can easily compute a security margin to guarantee obstacle avoidance during the path following process. Experimental results are presented at the end of the paper.

An obstacle avoidance scheme for hyper-redundant manipulators-global motion planning in posture space

Abstract: A hyper redundant manipulator has a very large or infinite degree of kinematic redundancy, thus it is possessed of unconventional features such as the ability to enter a narrow space while avoiding obstacles. We propose a novel obstacle avoidance technique for the hyper redundant manipulator to perform a payload location task from point to point while avoiding existing static obstacles in the environment. The scheme is based on analysis in the defined posture space, where three parameters were used to determine the hyper redundant manipulator configurations. The scheme is verified by computer simulation in case of using the model of the developed Hyper-R Arm. It shows that our method works perfect and the obstacles are well avoided globally.

Real-time robot navigation in unstructured environments using a 3D laser rangefinder

Abstract: In this paper a technique for real-time robot navigation is presented. Off-line planned trajectories and motions are modified in real-time to avoid obstacles, using a reactive behaviour. The information about the environment is provided to the control system of the robot by a rotating 3D laser sensor which have two degrees of freedom. Using this sensor, three dimensional information can be obtained, and can be used simultaneously for obstacle avoidance, robot self-localization and for 3D local map building. In this work we focus the attention in the obstacle avoidance problem with this kind of sensor. Experimental results in different indoor environments are reported, which show the versatility of the proposed technique to carry out typical tasks such as wall following, door crossing and motion in a room with several obstacles.

Design of emergency manoeuvres for automated highway system: obstacle avoidance problem

Abstract: We analyze the problem of obstacle avoidance in an automated highway system (AHS). For a given scenario (traffic state, obstacle location, etc.), we synthesize the best possible avoidance manoeuvre for each vehicle. Our aim is to obtain a distributed strategy so that the obstacle avoidance manoeuvres can be executed by vehicle based controllers (with some inter-vehicle communication) as opposed to a roadside controller making decisions and communicating it to the individual vehicles.

Adaptive obstacle avoidance with a neural network for operant conditioning: experiments with real robots

Abstract: Gaudiano et al. (1996) have shown that a neural network model of classical and operant conditioning can be trained to control the movements of a wheeled mobile robot. The neural network learns to avoid obstacles as the robot moves around without supervision in a cluttered environment. The neural network does not require any knowledge about the quality or configuration of the sensors. In this article we report results using our neural network with the real mobile robot Khepera.