Showing papers on "Obstacle avoidance published in 2010"

Wearable Obstacle Avoidance Electronic Travel Aids for Blind: A Survey

Abstract: The last decades a variety of portable or wearable navigation systems have been developed to assist visually impaired people during navigation in known or unknown, indoor or outdoor environments. There are three main categories of these systems: electronic travel aids (ETAs), electronic orientation aids (EOAs), and position locator devices (PLDs). This paper presents a comparative survey among portable/wearable obstacle detection/avoidance systems (a subcategory of ETAs) in an effort to inform the research community and users about the capabilities of these systems and about the progress in assistive technology for visually impaired people. The survey is based on various features and performance parameters of the systems that classify them in categories, giving qualitative-quantitative measures. Finally, it offers a ranking, which will serve only as a reference point and not as a critique on these systems.

Behavioral dynamics of steering, obstacle avoidance, and route selection

Abstract: The authors investigated the dynamics of steering and obstacle avoidance, with the aim of predicting routes through complex scenes. Participants walked in a virtual environment toward a goal (Experiment 1) and around an obstacle (Experiment 2) whose initial angle and distance varied. Goals and obstacles behave as attractors and repellers of heading, respectively, whose strengths depend on distance. The observed behavior was modeled as a dynamical system in which angular acceleration is a function of goal and obstacle angle and distance. By linearly combining terms for goals and obstacles, one could predict whether participants adopt a route to the left or right of an obstacle to reach a go (Experiment 3). Route selection may emerge from on-line steering dynamics, making explicit path planning unnecessary.

Robot motor skill coordination with EM-based Reinforcement Learning

Abstract: We present an approach allowing a robot to acquire new motor skills by learning the couplings across motor control variables. The demonstrated skill is first encoded in a compact form through a modified version of Dynamic Movement Primitives (DMP) which encapsulates correlation information. Expectation-Maximization based Reinforcement Learning is then used to modulate the mixture of dynamical systems initialized from the user's demonstration. The approach is evaluated on a torque-controlled 7 DOFs Barrett WAM robotic arm. Two skill learning experiments are conducted: a reaching task where the robot needs to adapt the learned movement to avoid an obstacle, and a dynamic pancake-flipping task.

Cooperative Path Planning of Unmanned Aerial Vehicles

Abstract: About the Authors. Series Preface. Preface. Acknowledgements. List of Figures. List of Tables. Nomenclature. 1. Introduction. 1.1 Path Planning Formulation. 1.2 Path Planning Constraints. 1.3 Cooperative Path Planning and Mission Planning. 1.4 Path Planning - An Overview. 1.5 The Road Map Method. 1.6 Probabilistic Methods. 1.7 Potential Field. 1.8 Cell Decomposition. 1.9 Optimal Control. 1.10 Optimization Techniques. 1.11 Trajectories for Path Planning. 1.12 Outline of the Book. References. 2. Path Planning in Two Dimensions. 2.1 Dubins Paths. 2.2 Designing Dubins Path using Analytical Geometry. 2.3 Existence of Dubins Paths. 2.4 Length of Dubins Paths. 2.5 Design of Dubins Paths using Principles of Differential Geometry. 2.6 Path of Continuous Curvature. 2.7 Producing Flyable Clothoid Paths. 28 Producing Flyable Pythagorean Hodograph Paths (2D). References. 3. Path Planning in Three Dimensions. 3.1 Dubins Paths in Three Dimensions Using Differential Geometry. 3.2 Path Length - Dubins 3D. 3.3 Pythagorean Hodograph Paths - 3D. 3.4 Design of Flyable Paths Using PH Curves. References. 4. Collision Avoidance. 4.1 Research into Obstacle Avoidance. 4.2 Obstacle Avoidance for Mapped Obstacles. 4.3 Obstacle Avoidance of Unmapped Static Obstacles. 4.4 Algorithmic Implementation. References. 5. Path-Following Guidance. 5.1 Path Following the Dubins Path. 5.2 Linear Guidance Algorithm. 5.3 Nonlinear Dynamic Inversion Guidance. 5.4 Dynamic Obstacle Avoidance Guidance. References. 6. Path Planning for Multiple UAVs. 6.1 Problem Formulation. 6.2 Simultaneous Arrival. 6.3 Phase I: Producing Flyable Paths. 6.4 Phase II: Producing Feasible Paths. 6.5 Phase III: Equalizing Path Length. 6.6 Multiple Path Algorithm. 6.7 Algorithm Application for Multiple UAVs. 6.8 2D Pythagorean Hodograph Paths. 6.9 3D Dubins Paths. 6.10 3D Pythagorean Hodograph Paths. References. Appendix A Differential Geometry. Appendix B. Pythagorean Hodograph. Index.

Predictive Control of Autonomous Ground Vehicles With Obstacle Avoidance on Slippery Roads

Abstract: Two frameworks based on Model Predictive Control (MPC) for obstacle avoidance with autonomous vehicles are presented. A given trajectory represents the driver intent. An MPC has to safely avoid obstacles on the road while trying to track the desired trajectory by controlling front steering angle and differential braking. We present two different approaches to this problem. The first approach solves a single nonlinear MPC problem. The second approach uses a hierarchical scheme. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model in order to avoid an obstacle. At the low-level an MPC controller computes the vehicle inputs in order to best follow the high level trajectory based on a nonlinear vehicle model. This article presents the design and comparison of both approaches, the method for implementing them, and successful experimental results on icy roads.Copyright © 2010 by ASME

Path Planning for Virtual Human Motion Using Improved A* Star Algorithm

Abstract: Calculating and generating optimal motion path automatically is one of the key issues in virtual human motion path planning. To solve the point, the improved A* algorithm has been analyzed and realized in this paper, we modified the traditional A* algorithm by weighted processing of evaluation function, which made the searching steps reduced from 200 to 80 and searching time reduced from 4.359s to 2.823s in the feasible path planning. The artificial searching marker, which can escape from the barrier trap effectively and quickly, is also introduced to avoid searching the invalid region repeatedly, making the algorithm more effective and accurate in finding the feasible path in unknown environments. We solve the issue of virtual human's obstacle avoidance and navigation through optimizing the feasible path to get the shortest path.

Planning Smooth and Obstacle-Avoiding B-Spline Paths for Autonomous Mining Vehicles

Abstract: We study the problem of automatic generation of smooth and obstacle-avoiding planar paths for efficient guidance of autonomous mining vehicles. Fast traversal of a path is of special interest. We consider fourwheel four-gear articulated vehicles and assume that we have an a priori knowledge of the mine wall environment in the form of polygonal chains. Computing quartic uniform B-spline curves, minimizing curvature variation, staying at least at a proposed safety margin distance from the mine walls, we plan high speed paths. We present a study where our implementations are successfully applied on eight path-planning cases arising from real-world mining data provided by the Swedish mining company Luossavaara-Kiirunavaara AB (LKAB). The results from the study indicate that our proposed methods for computing obstacle-avoiding minimum curvature variation B-splines yield paths that are substantially better than the ones used by LKAB today. Our simulations show that, with an average 32.13%, the new paths are faster to travel along than the paths currently in use. Preliminary results from the production at LKAB show an overall 5%-10% decrease in the total time for an entire mining cycle. Such a cycle includes both traveling, ore loading, and unloading.

Graph-based segmentation for colored 3D laser point clouds

Abstract: We present an efficient graph-theoretic algorithm for segmenting a colored laser point cloud derived from a laser scanner and camera. Segmentation of raw sensor data is a crucial first step for many high level tasks such as object recognition, obstacle avoidance and terrain classification. Our method enables combination of color information from a wide field of view camera with a 3D LIDAR point cloud from an actuated planar laser scanner. We extend previous work on robust camera-only graph-based segmentation to the case where spatial features, such as surface normals, are available. Our combined method produces segmentation results superior to those derived from either cameras or laser-scanners alone. We verify our approach on both indoor and outdoor scenes.

Bilateral Teleoperation of Multiple Mobile Agents: Coordinated Motion and Collision Avoidance

Abstract: This paper presents theoretical and experimental results on bilateral teleoperation of multiple mobile slave agents coupled to a single master robot. We first design a passifying proportional-derivative (PD) controller to enforce motion tracking and formation control of master and slave vehicles under constant, bounded communication delays. Then, we incorporate avoidance functions to guarantee collision-free transit through obstructed spaces. The unified control framework is validated by experiments with two coaxial helicopters as slave agents and a haptic device as the master robot.

A multi-objective PSO-based algorithm for robot path planning

Abstract: In this paper a novel method is presented for robot motion planning with respect to two objectives, the shortest and smoothest path criteria A Particle Swarm Optimization (PSO) algorithm is employed for global path planning, while the Probabilistic Roadmap method (PRM) is used for obstacle avoidance (local planning) The two objective functions are incorporated in the PSO equations in which the path smoothness is measured by the difference of the angles of the hypothetical lines connecting the robot's two successive positions to its goal The PSO and PRM are combined by adding good PSO particles as auxiliary nodes to the random nodes generated by the PRM The proposed algorithm is compared in path length and runtime with the mere PRM method searched by Dijkstra's algorithm, and the results showed that the generated paths are shorter and smoother and are calculated in less time

Pedestrian Detection and Tracking in an Urban Environment Using a Multilayer Laser Scanner

Abstract: Pedestrians are the most vulnerable participants in urban traffic. The first step toward protecting pedestrians is to reliably detect them in a real-time framework. In this paper, a new approach is presented for pedestrian detection in urban traffic conditions using a multilayer laser sensor mounted onboard a vehicle. This sensor, which is placed on the front of a vehicle, collects information about the distance distributed according to four planes. Like a vehicle, a pedestrian constitutes, in the vehicle environment, an obstacle that must be detected, and located and then identified and tracked if necessary. To improve the robustness of pedestrian detection using a single laser sensor, a detection system based on the fusion of information located in the four laser planes is proposed. The method uses a nonparametric kernel-density-based estimation of pedestrian position of each laser plane. The resulting pedestrian estimations are then sent to a decentralized fusion according to the four planes. Temporal filtering of each object is finally achieved within a stochastic recursive Bayesian framework (particle filter), allowing a closer observation of pedestrian random movement dynamics. Many experimental results are given and validate the relevance of our pedestrian-detection algorithm with regard to a method using only a single-row laser-range scanner.

Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths

Abstract: As mobile robots venture into more difficult environments, more complex state-space paths are required to move safely and efficiently. The difference between mission success and failure can be determined by a mobile robots capacity to effectively navigate such paths in the presence of disturbances. This paper describes a technique for mobile robot model predictive control that utilizes the structure of a regionalmotion plan to effectively search the local continuum for an improved solution. The contribution, a receding horizon model-predictive control (RHMPC) technique, specifically addresses the problem of path following and obstacle avoidance through geometric singularities and discontinuities such as cusps, turn-in-place, and multi-point turn maneuvers in environments where terrain shape and vehicle mobility effects are non-negligible. The technique is formulated as an optimal controller that utilizes a model-predictive trajectory generator to relax parameterized control inputs initialized from a regional motion planner to navigate safely through the environment. Experimental results are presented for a six-wheeled skid-steered field robot in natural terrain.

Teleoperation of a Mobile Robot Using a Force-Reflection Joystick With Sensing Mechanism of Rotating Magnetic Field

Abstract: Operators' intelligent and skillful decisions are necessary for the teleoperation of a mobile robot when there are many scattered obstacles. Among the sensors used for environment recognition, the camera is the most popular and powerful. However, there are several limitations in the camera-based teleoperation of a mobile robot. For example, shadowed and curved areas cannot be viewed using a narrow view-angle camera, especially in an environment with bad illumination and several obstacles. Therefore, it is necessary to have other sensory information for reliable teleoperations. In this study, 16 ultrasonic sensors are attached around a mobile robot in a ring pattern to measure the distances to the obstacles and a collision vector is introduced as a new tool for obstacle avoidance, which is defined as the normal vector from an obstacle to the mobile robot. Based on this collision vector, a virtual reflection force is generated to avoid the obstacles and then the reflection force is transferred to the operator who is holding the joystick used to control the mobile robot. Based on this reflection force, the operator can control the mobile robot more smoothly and safely. For this bidirectional teleoperation, a master joystick system using a two-axis hall sensor was designed to eliminate the nonlinear region, which exists in a general joystick with two motors and potentiometers. The effectiveness of the collision vector and force-reflection joystick is verified by comparing two vision-based teleoperation experiments, with and without force reflection.

Autonomous Navigation in Three-Dimensional Urban Environments Using Wide-Field Integration of Optic Flow

Abstract: In this paper, a control theoretic framework is introduced to analyze an information extraction approach from patterns of optic flow based on analogs to wide-field motion-sensitive interneurons in the insect visuomotor system. An algebraic model of optic flow is developed, based on a parameterization of three-dimensional urban environments. It is shown that estimates of proximity and speed, relative to these environments, can be extracted using weighted summations of the instantaneous patterns of optic flow. Small perturbation techniques are then applied to link weighting patterns to outputs, which are applied as feedback to facilitate stability augmentation and perform local obstacle avoidance and terrain following. Additive noise and environment uncertainties are incorporated into an offline procedure for determination of optimal weighting functions. Stability is proven via local asymptotic analysis and the resulting approach demonstrated in simulation using a micro helicopter in a three-dimensional urbanlike environment.

Probabilistic Autonomous Robot Navigation in Dynamic Environments with Human Motion Prediction

Abstract: This paper considers the problem of autonomous robot navigation in dynamic and congested environments The predictive navigation paradigm is proposed where probabilistic planning is integrated with obstacle avoidance along with future motion prediction of humans and/or other obstacles Predictive navigation is performed in a global manner with the use of a hierarchical Partially Observable Markov Decision Process (POMDP) that can be solved on-line at each time step and provides the actual actions the robot performs Obstacle avoidance is performed within the predictive navigation model with a novel approach by deciding paths to the goal position that are not obstructed by other moving objects movement with the use of future motion prediction and by enabling the robot to increase or decrease its speed of movement or by performing detours The robot is able to decide which obstacle avoidance behavior is optimal in each case within the unified navigation model employed

A biologically inspired method for robot navigation in a cluttered environment

Abstract: The problem of wheeled mobile robot (WMR) navigation toward an unknown target in a cluttered environment has been considered. The biologically inspired navigation algorithm is the equiangular navigation guidance (ENG) law combined with a local obstacle avoidance technique. The collision avoidance technique uses a system of active sensors which provides the necessary information about obstacles in the vicinity of the robot. In order for the robot to avoid collision and bypass the enroute obstacles, the angle between the instantaneous moving direction of the robot and a reference point on the surface of the obstacle is kept constant. The performance of the navigation strategy is confirmed with computer simulations and experiments with ActivMedia Pioneer 3-DX wheeled robot.

A novel potential field method for obstacle avoidance and path planning of mobile robot

Abstract: This paper presents a novel artificial potential field method for obstacle avoidance and path planning of mobile robots. By analyzing the shortcoming of the artificial potential field methods for robot path planning, we propose an obstacle avoidance method based on gravity chain. Suppose that there is a rubber band which connects with the beginning and the ending in the obstacle potential field space. As the rubber band will be the role of potential field power, we can build a model to simulate the shape of the rubber band. Then this method will generate a steer angle tangent to the rubber band instead of the angle of artificial potential field. By putting effective obstacle avoidance information into potential field through gravity chain, we solve the problems that the artificial potential field method often converges to local minima, as well as it hardly reach the ending and oscillatory movement. The Simulation results show that the method proposed is correct and effective.

Multifunctional Intelligent Autonomous Parking Controllers for Carlike Mobile Robots

Abstract: An increasing number of carlike mobile robot (CLMR) studies have addressed the issues of autonomous parking and obstacle avoidance. An autonomous parking controller can provide convenience to a novice driver. However, if the controller is not designed adequately, it may endanger the car and the driver. Therefore, this paper presents a novel multifunctional intelligent autonomous parking controller that is capable of effectively parking the CLMR in an appropriate parking space, by integrating sensor data capable of obtaining the surrounding data of the robot. An ultrasonic sensor array system has been developed with group-sensor firing intervals. A binaural approach to the CLMR has been adopted for providing complete contactless sensory coverage of the entire workspace. The proposed heuristic controller can obtain the posture of a mobile robot in a parking space. In addition, the controller can ensure the ability of the CLMR to withstand collision to guarantee safe parking. Moreover, the CLMR can recognize the parking space and the obstacle position in dynamic environment. Therefore, the proposed controller installed in a car could ensure safe driving. Finally, practical experiments demonstrate that the proposed multifunctional intelligent autonomous parking controllers are feasible and effective.

Real-time path planning for a robot arm in changing environments

Abstract: We present a practical strategy for real-time path planning for articulated robot arms in changing environments by integrating PRM for Changing Environments with 3D sensor data. Our implementation on Care-O-Bot 3 identifies bottlenecks in the algorithm and introduces new methods that solve the overall task of detecting obstacles and planning a path around them in under 100 ms. A fast planner is necessary to enable the robot to react to quickly changing human environments. We have tested our implementation in real-world experiments where a human subject enters the manipulation area, is detected and safely avoided by the robot. This capability is critical for future applications in automation and service robotics where humans will work closely with robots to jointly perform tasks.

Robotic omniwheel vehicle

Abstract: A robotic omniwheel vehicle includes autonomous drive logic with laser Radar, GPS for vehicle self guidance, obstacle avoidance and range finding location and also to control the omniwheel assemblies having multi-directional steering, extension and lift actuation via a universal joint and a transmission rod that also supports the chassis while traveling at speeds ranging from low to high velocity on and off road, and on rails. The navigational system control methods use manual driving, a cell phone controller comprising satellite telecommunication with voice command, and a touch screen control panel, an omnichair which can rise up and swivel on point, and may also include electromagnetic coupling devices to connect omnivehicles together.

Attractor dynamics approach to formation control: theory and application

Abstract: In this paper we show how non-linear attractor dynamics can be used as a framework to control teams of autonomous mobile robots that should navigate according to a predefined geometric formation. The environment does not need to be known a priori and may change over time. Implicit to the control architecture are some important features such as establishing and moving the formation, split and join of formations (when necessary to avoid obstacles). Formations are defined by a formation matrix. By manipulating this formation matrix it is also possible to switch formations at run time. Examples of simulation results and implementations with real robots (teams of Khepera robots and medium size mobile robots), demonstrate formation switch, static and dynamic obstacle avoidance and split and join formations without the need for any explicit coordination scheme. Robustness against environmental perturbations is intrinsically achieved because the behaviour of each robot is generated as a time series of asymptotically stable states, which contribute to the asymptotic stability of the overall control system.

Smooth Flight Trajectory Planning in the Presence of No-Fly Zones and Obstacles

Abstract: This paper describes a novel procedure to generate continuously differentiable optimal flight trajectories in the presence of arbitrarily shaped no-fly zones and obstacles having a fixed position in time. The operational flight scenario is first discretized with a finite dimensional grid of positions-directions pairs. A weighted and oriented graph is then defined for which the nodes are the earlier mentioned grid points and for which the arcs correspond to minimum length trajectories compliant with obstacle avoidance constraints. Arcs are obtained via solving convex quadratic programming optimization problems that can also account for geometrical constraints such as trajectory curvature limitations. The problem of finding an optimal trajectory be tween two nodes of the so-called core paths graph is then solved via a minimum cost path search algorithm. In a real-time application perspective, the generation of the core paths graph is computationally cumbersome. Moreover, the aircraft position and direction rarely coincide with one of the graph nodes. However, if the graph is built offline and stored, the definition of an optimal trajectory connecting any points of the space domain requires a reduced computational effort. The particular case of piecewise polynomial trajectories minimizing a flight path's length, compliant with constraints on curvature and flight-path angles, is fully developed. Two- and three-dimensional examples are discussed to show the applicability as well as the effectiveness of the technique.

HyperNEAT for locomotion control in modular robots

Abstract: In an application where autonomous robots can amalgamate spontaneously into arbitrary organisms, the individual robots cannot know a priori at which location in an organism they will end up If the organism is to be controlled autonomously by the constituent robots, an evolutionary algorithm that evolves the controllers can only develop a single genome that will have to suffice for every individual robot However, the robots should show different behaviour depending on their position in an organism, meaning their phenotype should be different depending on their location In this paper, we demonstrate a solution for this problem using the HyperNEAT generative encoding technique with differentiated genome expression We develop controllers for organism locomotion with obstacle avoidance as a proof of concept Finally, we identify promising directions for further research

Dynamic path planning for mobile robot based on genetic algorithm in unknown environment

Abstract: In this paper, a dynamic path planning scheme based on genetic algorithm (GA) is presented for navigation and obstacle avoidance of mobile robot under unknown environment. The real coding, fitness function and specific genetic operators are devised in the algorithm. The unique coding technique decreases the conventional computational complexity of genetic algorithm. It also speeds up the execution of searching by projecting two dimensional data to one dimensional data, which reduce the size of search space. The fitness function of genetic algorithm takes full consideration of three factors: the collision avoidance path, the shortest distance and smoothness of the path. The specific genetic operators are also selected to make the genetic algorithm more effective. The simulation experiments are made under the VC++ 6.0 environment. The simulation results verify that the genetic algorithm is high effective under various complex dynamic environments.

Detecting anomalies in unmanned vehicles using the Mahalanobis distance

Abstract: The use of unmanned autonomous vehicles is becoming more and more significant in recent years. The fact that the vehicles are unmanned (whether autonomous or not), can lead to greater difficulties in identifying failure and anomalous states, since the operator cannot rely on its own body perceptions to identify failures. Moreover, as the autonomy of unmanned vehicles increases, it becomes more difficult for operators to monitor them closely, and this further exacerbates the difficulty of identifying anomalous states, in a timely manner. Model-based diagnosis and fault-detection systems have been proposed to recognize failures. However, these rely on the capabilities of the underlying model, which necessarily abstracts away from the physical reality of the robot. In this paper we propose a novel, model-free, approach for detecting anomalies in unmanned autonomous vehicles, based on their sensor readings (internal and external). Experiments conducted on Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) demonstrate the efficacy of the approach by detecting the vehicles deviations from nominal behavior.

Maximum load-carrying capacity of autonomous mobile manipulator in an environment with obstacle considering tip over stability

Abstract: In order to increase the efficiency of wheeled mobile manipulators (WMM), it is preferred to carry the maximum payload, but in the case of a small-sized platform, which is desirable in applications, it may cause dangerous tip over, especially in the presence of obstacles. So, it is necessary to consider stability constraint in the determination of robot motion planning. This paper presents a novel approach for the determination of the maximum payload-carrying capacity of a coordinated mobile manipulator in an environment with presence of obstacle, based on stability. The proposed method considers the tip over stability on zero moment point criterion, which must be considered when the path of the end-effector is predefined but the position of the mobile platform is free, because tipping over in this condition is probable. Hence, the full dynamic model of WMM (including the coordinating vehicle and manipulator) is used, the obstacle avoidance scheme is implied based on potential functions, and the maximum payload path for a specified payload is generated using the optimal control approach. Then, an iterative method based on the stability criterion and motor restriction, including torque and jerk, was implied to calculate the maximum payload capacity. To the best of our knowledge, this is the first paper reporting on some simulation results as well as successful experiments of implementing the algorithm. The proposed approach has been implemented and tested on a nonholonomic WMM consisting of a differential-drive mobile base and a robotic arm to demonstrate the efficiency and effectiveness of the proposed approach.

Closest Gap based (CG) reactive obstacle avoidance Navigation for highly cluttered environments

Abstract: A new reactive collision avoidance approach for mobile robots moving in cluttered and complex environments was developed and implemented. The novelty of this approach lies in the creation of a new method for analyzing openings in front of the robot that highly reduces their number when compared with the Nearness-Diagram Navigation (ND) technique, particularly in complex scenarios. Moreover, the angular width of the chosen (selected) gap with respect to the robot vision is taken into consideration. Consequently, oscillations are alleviated, the computational complexity is reduced and a smoother behavior will be achieved. Our technique adjusts the motion law proposed in the Smooth Nearness-Diagram Navigation (SND) method to generate safer paths for the robot by considering the ratio of threats on its sides and applying stricter deviation against an obstacle as it gets closer to the robot. Hence, the problem of deadlock occurring in narrow corridors, with high threats on one side and low threats on the other, is solved without affecting the smoothness behavior. Simulation and experimental results demonstrate the power of the proposed approach.