Showing papers on "Obstacle published in 2014"

A Decentralized Cooperative Control Scheme With Obstacle Avoidance for a Team of Mobile Robots

Abstract: The problem of formation control of a team of mobile robots based on the virtual and behavioral structures is considered in this paper. In the virtual structure, each mobile robot is modeled by an electric charge. The mobile robots move toward a circle, and due to repulsive forces between the identical charges, regular polygon formations of the mobile robots will be realized. For swarm formation, a virtual mobile robot is located at the center of the circle, and other mobile robots follow it. In the introduced approach, each mobile robot finds its position in the formation autonomously, and the formation can change automatically in the case of change in the number of the mobile robots. This paper also proposes a technique for avoiding obstacles based on the behavioral structure. In this technique, when a mobile robot gets close to an obstacle, while moving toward its target, a rotational potential field is applied to lead the mobile robot to avoid the obstacle, without locating in local minimum positions.

Trajectory tracking with collision avoidance for nonholonomic vehicles with acceleration constraints and limited sensing

Abstract: Nowadays, autonomously operated nonholonomic vehicles are employed in a wide range of applications, ranging from relatively simple household chores (e.g. carpet vacuuming and lawn mowing) to highly sophisticated assignments (e.g. outer space exploration and combat missions). Each application may require different levels of accuracy and capabilities from the vehicles, yet, all expect the same critical outcome: to safely complete the task while avoiding collisions with obstacles and the environment. Herein, we report on a bounded control law for nonholonomic systems of unicycle-type that satisfactorily drive a vehicle along a desired trajectory while guaranteeing a minimum safe distance from another vehicle or obstacle at all times. The control law is comprised of two parts. The first is a trajectory tracking and set-point stabilization control law that accounts for the vehicle's kinematic and dynamic constraints (i.e. restrictions on velocity and acceleration). We show that the bounded tracking control law enforces global asymptotic convergence to the desired trajectory and local exponential stability of the full state vector in the case of set-point stabilization. The second part is a real-time avoidance control law that guarantees collision-free transit for the vehicle in noncooperative and cooperative scenarios independently of bounded uncertainties and errors in the obstacles' detection process. The avoidance control acts locally, meaning that it is only active when an obstacle is close and null when the obstacle is safely away. Moreover, the avoidance control is designed according to the vehicle's acceleration limits to compensate for lags in the vehicle's reaction time. The performance of the synthesized control law is then evaluated and validated via simulation and experimental tests.

Cooperative Visibility Maintenance for Leader–Follower Formations in Obstacle Environments

Abstract: Vision-based formation control of multiple agents, such as mobile robots or fully autonomous cars, has recently received great interest due to its application in robotic networks and automated highways. This paper addresses the cooperative motion coordination of leader-follower formations of nonholonomic mobile robots, under visibility and communication constraints in known polygonal obstacle environments. We initially consider the case of N = 2 agents moving in L-F fashion and propose a feedback control strategy under which L ensures obstacle avoidance for both robots, while F ensures visibility maintenance with L and intervehicle collision avoidance. The derived algorithms are based on set-theoretic methods to guarantee visibility maintenance, dipolar vector fields to maintain the formation shape, and the consideration of the formation as a tractor-trailer system to ensure obstacle avoidance. We furthermore show how the coordination and control design extends to the case of N > 2 agents, and provide simulation results, which demonstrate the efficacy of the control solutions. The proposed algorithms do not require information exchange among robots, but are instead based on information locally available to each agent. In this way, the desired tasks are executed and achieved in a decentralized manner, with each robot taking care of converging to a desired configuration, while maintaining visibility with its target.

The Most Important Topic Political Scientists Are Not Studying: Adapting to Climate Change

Abstract: Few, if any, political scientists currently study climate change adaptation or are even aware that there is a large and growing interdisciplinary field of study devoted not just to mitigating greenhouse gas emissions but to reducing our vulnerability to the now-inevitable impacts of climate change. The lack of political science expertise and research represents an obstacle for adapting to climate change, because adaptation is fundamentally political. Technical advances in adaptations for infrastructure, agriculture, public health, coastal protection, conservation, and other fields all depend on political variables for their implementation and effectiveness. For example, adaptation raises questions about political economy (adaptation costs money), political theory (adaptation involves questions of social justice), comparative politics (some countries more aggressively pursue adaptation), urban politics (some cities more aggressively pursue adaptation), regime type (democracies and authoritarian regimes may differently pursue adaptation), federalism (different levels of government may be involved), and several other fields of study including political conflict, international development, bureaucracy, migration, media, political parties, elections, civil society, and public opinion. I review the field of climate change adaptation and then explore the tremendous contributions that political scientists could make to adaptation research.

Real-time obstacle detection using stereo vision for autonomous ground vehicles: A survey

Abstract: One of the most important features for any intelligent ground vehicle is based on how is reliable and complete the perception of the environment and the capability to discriminate what an obstacle is. Obstacle Detection (OD) is one of the most widely discussed topics in literature. Many approaches have been presented for different application fields and scenarios; in last years most of them have been revisited using stereo vision or 2D/3D sensor technologies. In this paper we present a brief survey about Obstacle Detection techniques based on stereo vision for intelligent ground vehicles, describing and comparing the most interesting approaches. In order to provide a generic overview of these techniques, it has been decided to focus the study only on the algorithms that have provided a major contribution through real-time experiments in unsupervised scenarios.

Development of a Laser-Range-Finder-Based Human Tracking and Control Algorithm for a Marathoner Service Robot

Abstract: This paper presents a human detection algorithm and an obstacle avoidance algorithm for a marathoner service robot (MSR) that provides a service to a marathoner while training. To be used as a MSR, the mobile robot should have the abilities to follow a running human and avoid dynamically moving obstacles in an unstructured outdoor environment. To detect a human by a laser range finder (LRF), we defined features of the human body in LRF data and employed a support vector data description method. In order to avoid moving obstacles while tracking a running person, we defined a weighted radius for each obstacle using the relative velocity between the robot and an obstacle. For smoothly bypassing obstacles without collision, a dynamic obstacle avoidance algorithm for the MSR is implemented, which directly employed a real-time position vector between the robot and the shortest path around the obstacle. We verified the feasibility of these proposed algorithms through experimentation in different outdoor environments.

Road terrain detection: Avoiding common obstacle detection assumptions using sensor fusion

Abstract: Obstacle detection is a fundamental task for Advanced Driver Assistance Systems (ADAS) and Self-driving cars. Several commercial systems like Adaptive Cruise Controls and Collision Warning Systems depend on them to notify the driver about a risky situation. Several approaches have been presented in the literature in the last years. However, most of them are limited to specific scenarios and restricted conditions. In this paper we propose a robust sensor fusion-based method capable of detecting obstacles in a wide variety of scenarios using a minimum number of parameters. Our approach is based on the spatial-relationship on perspective images provided by a single camera and a 3D LIDAR. Experimental tests have been carried out in different conditions using the standard ROAD-KITTI benchmark, obtaining positive results.

Autonomous Visual Navigation and Laser-Based Moving Obstacle Avoidance

Abstract: Moving obstacle avoidance is a fundamental re- quirement for any robot operating in real environments, where pedestrians, bicycles and cars are present. In this paper, we propose and validate a framework for avoiding moving obstacles during visual navigation with a wheeled mobile robot. Visual navigation consists of following a path, represented as an ordered set of key images, which have been acquired by an on-board camera in a teaching phase. While following such path, our robot is able to avoid static as well as moving obstacles, which were not present during teaching, and which are sensed by an on- board lidar. The proposed approach takes explicitly into account obstacle velocities, estimated using an appropriate Kalman-based observer. The velocities are then used to predict the obstacle positions within a tentacle-based approach. Finally, our approach is validated in a series of real outdoor experiments, showing that when the obstacle velocities are considered, the robot behaviour is safer, smoother, and faster than when it is not.

LIDAR Obstacle Warning and Avoidance System for Unmanned Aircraft

Abstract: The availability of powerful eye-safe laser sources and the recent advancements in electro-optical and mechanical beam-steering components have allowed laser-based Light Detection and Ranging (LIDAR) to become a promising technology for obstacle warning and avoidance in a variety of manned and unmanned aircraft applications. LIDAR outstanding angular resolution and accuracy characteristics are coupled to its good detection performance in a wide range of incidence angles and weather conditions, providing an ideal obstacle avoidance solution, which is especially attractive in low-level flying platforms such as helicopters and small-to-medium size Unmanned Aircraft (UA). The Laser Obstacle Avoidance Marconi (LOAM) system is one of such systems, which was jointly developed and tested by SELEX-ES and the Italian Air Force Research and Flight Test Centre. The system was originally conceived for military rotorcraft platforms and, in this paper, we briefly review the previous work and discuss in more details some of the key development activities required for integration of LOAM on UA platforms. The main hardware and software design features of this LOAM variant are presented, including a brief description of the system interfaces and sensor characteristics, together with the system performance models and data processing algorithms for obstacle detection, classification and avoidance. In particular, the paper focuses on the algorithm proposed for optimal avoidance trajectory generation in UA applications.

Stereo-vision based obstacle mapping for indoor/outdoor SLAM

Abstract: The creation of local and global maps is crucial for (semi-)autonomous operation of mobile robots in previously unknown environments, e.g. during search and rescue missions. We developed an on-board stereo-vision based mapping system, thereby introducing local obstacle maps that can directly be used for fast local obstacle avoidance and path planning. In addition, we designed them to constitute a suitable input to a widely-used simultaneous localization and mapping (SLAM) algorithm. We performed experiments in unknown indoor, unstructured outdoor as well as mixed environments and demonstrated the applicability of our method to camera setups with small as well as wide field of view. In all three scenarios, we achieved a final 2D position error of less than 0.08% of the full trajectory.

Adaptive Artificial Potential Field Approach for Obstacle Avoidance Path Planning

Abstract: This paper presents an adaptive artificial potential field method for robot's obstacle avoidance path planning. Despite the obstacle avoidance path planning based on the artificial potential field method is very popular, but there is local minima problem with this approach. As a result, this paper proposes an improved obstacle potential field function model considering for the size of the robot and the obstacles and changes the weight of the obstacle potential field function adaptively to make the robot escape from the local minima. Three simulations have been done and the simulation results show: the improved algorithm can make the robot escape from the local minima and accomplish the robot collision avoidance path planning well.

Strong Stationarity for Optimal Control of the Obstacle Problem with Control Constraints

Abstract: We consider the distributed optimal control of the obstacle problem with control constraints. Since Mignot proved in 1976 the necessity of a system which is equivalent to strong stationarity, it has been an open problem whether such a system is still necessary in the presence of control constraints. Using moderate regularity of the optimal control and an assumption on the control bounds (which is implied by $u_a < 0 \le u_b$ quasi-everywhere in $\Omega$ in the case of an upper obstacle $y \le \psi$), we can answer this question in the affirmative. We also present counterexamples showing that strong stationarity may not hold if $u_a < 0$ or $0 \le u_b$ are violated. (An erratum is attached.)

Impact of a truck as an obstacle on vehicle-to-vehicle communications in rural and highway scenarios

Abstract: Shadowing from other vehicles can degrade the performance of vehicle-to-vehicle communication systems significantly. It is thus important to characterize and model the influence of common shadowing objects like trucks in a proper way. However, the scenario of a truck as an obstacle in highly dynamic rural and highway environments is not yet well understood. In this paper we analyze the distance dependent path loss and the additional shadowing loss due to a truck through dynamic measurements. We further characterize the large scale fading and the delay and Doppler spreads as a measure of the channel dispersion in the time and frequency domains. It has been found that a truck as an obstacle reduces the received power by 12 and 13 dB on average, for roof antennas, in rural and highway scenarios, respectively. Also, the dispersion in time and frequency domains is highly increased when the line-of-sight is obstructed by the truck.

Through the eyes of a bird: modelling visually guided obstacle flight.

Abstract: Various flight navigation strategies for birds have been identified at the large spatial scales of migratory and homing behaviours. However, relatively little is known about close-range obstacle ne...

Demonstrating the Impact of the Occupant on Building Performance

Abstract: Designers often cite uncertain occupant behavior as a potential obstacle to high performing buildings However, little research has been done to characterize the nature and extent of the po

An automatic COLREGs-compliant obstacle avoidance system for an unmanned surface vehicle

Abstract: Unmanned surface vehicles are becoming increasingly vital tools in a variety of maritime applications. Unfortunately, their usability is severely constrained by the lack of a reliable obstacle detection and avoidance system. In this article, one such experimental platform is proposed, which performs obstacle detection, risk assessment and path planning (avoidance) tasks autonomously in an integrated manner. The detection system is based on a vision-LIDAR (light detection and ranging) system, whereas a heuristic path planner is utilised. A unique property of the path planner is its compliance with the marine collision regulations. It is demonstrated through hardware-in-the-loop simulations that the proposed system can be useful for both uninhabited and manned vessels.

Biologically-inspired adaptive obstacle negotiation behavior of hexapod robots

Abstract: Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots It combines locomotion control, backbone joint control, local leg reflexes, and neural learning While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, eg, variable obstacle heights and different walking gaits By successfully learning the association of an early, predictive signal (conditioned stimulus, CS) and a late, reflex signal (unconditioned stimulus, UCS), both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robot's leg length in simulation and 75% in a real environment

A real-time monocular vision-based frontal obstacle detection and avoidance for low cost UAVs in GPS denied environment

Abstract: This paper presents a novel monocular vision-based realtime obstacle detection and avoidance for a low cost unmanned aerial vehicle (UAV) in an unstructured, GPS denied environment We propose a mathematical model to estimate the relative distance from the UAV's camera to an obstacle which will subsequently be used in a collision avoidance algorithm We validate our model with some real time experiments under both stationary and dynamic motion of the UAV during its flight The results show good agreement with the ground truth values with an acceptable percentage of error in estimation under 3% thus proving it can facilitate obstacle detection and avoidance for low cost and lightweight UAVs

Lorentz estimates for obstacle parabolic problems

Abstract: We prove that the spatial gradient of (variational) solutions to parabolic obstacle problems of p-Laplacian type enjoys the same regularity of the data and of the derivatives of the obstacle in the scale of Lorentz spaces.

Application of A* algorithm for real-time path re-planning of an unmanned surface vehicle avoiding underwater obstacles

Abstract: This paper describes path re-planning techniques and underwater obstacle avoidance for unmanned surface vehicle (USV) based on multi-beam forward looking sonar (FLS). Near-optimal paths in static and dynamic environments with underwater obstacles are computed using a numerical solution procedure based on an A* algorithm. The USV is modeled with a circular shape in 2 degrees of freedom (surge and yaw). In this paper, two-dimensional (2-D) underwater obstacle avoidance and the robust real-time path re-planning technique for actual USV using multi-beam FLS are developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames in the field of view of the sonar with a proper update frequency of the FLS. The performance of the proposed method was verified through simulations, and sea experiments. For simulations, the USV model can avoid both a single stationary obstacle, multiple stationary obstacles and moving obstacles with the near-optimal trajectory that are performed both in the vehicle and the world reference frame. For sea experiments, the proposed method for an underwater obstacle avoidance system is implemented with a USV test platform. The actual USV is automatically controlled and succeeded in its real-time avoidance against the stationary undersea obstacle in the field of view of the FLS together with the Global Positioning System (GPS) of the USV.

Memory-guided obstacle crossing: more failures were observed for the trail limb versus lead limb

Abstract: During adaptive locomotion, vision is used to guide the lead limb; however, the individual must rely on knowledge of obstacle height and position, termed obstacle memory, to guide the trail limb. Previous research has demonstrated that visual sampling of the obstacle during approach was adequate to provide obstacle height information, but online visual update of distance to the obstacle was required to plan and implement appropriate foot placement. Our purpose was to determine whether obstacle height memory, coupled with a visible obstacle position cue, could successfully guide the foot during obstacle crossing. Subjects first stepped over an obstacle for 25 trials; then, the obstacle was removed, but its position was marked with high-contrast tape; subjects were instructed to step over the obstacle as if it was still there (termed “virtual obstacle”) for 25 trials. No changes in foot placement were observed; therefore, the position cue provided salient online information to guide foot placement. Average failure rates (subject would have contacted the virtual obstacle if it was present) were 9 and 47 % (lead and trail limb, respectively). Therefore, action was impaired for both limbs when guided by obstacle height memory, but action was impaired to a greater extent for the trail limb. Therefore, viewing the obstacle during approach appears to facilitate the memory needed to guide obstacle crossing, particularly for the trail limb. This is likely because the lead limb is visible in the peripheral visual field during crossing, but the trail limb is not.

Obstacle detection and navigation planning for autonomous micro aerial vehicles

Abstract: Obstacle detection and real-time planning of collision-free trajectories are key for the fully autonomous operation of micro aerial vehicles in restricted environments. In this paper, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception consisting of a 3D laser scanner, two stereo camera pairs, and ultrasonic distance sensors. Detected obstacles are aggregated in egocentric local multiresolution grid maps. We generate trajectories in a multi-layered approach: from mission planning to global and local trajectory planning, to reactive obstacle avoidance. We evaluate our approach in simulation and with the real autonomous micro aerial vehicle.

Binocular vision obstacle detection method based on three-dimensional point cloud segmentation

Abstract: The invention provides a binocular vision obstacle detection method based on three-dimensional point cloud segmentation. The method comprises the steps of synchronously collecting two camera images of the same specification, conducting calibration and correction on a binocular camera, and calculating a three-dimensional point cloud segmentation threshold value; using a three-dimensional matching algorithm and three-dimensional reconstruction calculation for obtaining a three-dimensional point cloud, and conducting image segmentation on a reference map to obtain image blocks; automatically detecting the height of a road surface of the three-dimensional point cloud, and utilizing the three-dimensional point cloud segmentation threshold value for conducting segmentation to obtain a road surface point cloud, obstacle point clouds at different positions and unknown region point clouds; utilizing the point clouds obtained through segmentation for being combined with the segmented image blocks, determining the correctness of obstacles and the road surface, and determining position ranges of the obstacles, the road surface and unknown regions. According to the binocular vision obstacle detection method, the camera and the height of the road surface can be still detected under the complex environment, the three-dimensional segmentation threshold value is automatically estimated, the obstacle point clouds, the road surface point cloud and the unknown region point clouds can be obtained through segmentation, the color image segmentation technology is ended, color information is integrated, correctness of the obstacles and the road surface is determined, the position ranges of the obstacles, the road surface and the unknown regions are determined, the high-robustness obstacle detection is achieved, and the binocular vision obstacle detection method has higher reliability and practicability.

Detecting lane closures and lane shifts by an autonomous vehicle

Abstract: In an example implementation, an autonomous vehicle is configured to detect closures and lane shifts in a lane of travel. The vehicle is configured to operate in an autonomous mode and determine a presence of an obstacle substantially positioned in a lane of travel of the vehicle using a sensor. The lane of travel has a first side, a second side, and a center, and the obstacle is substantially positioned on the first side. The autonomous vehicle includes a computer system. The computer system determines a lateral distance between the obstacle and the center, compares the lateral distance to a pre-determined threshold, and provides instructions to control the autonomous vehicle based on the comparison.

Sensor-Based Motion Planning of Wheeled Mobile Robots in Unknown Dynamic Environments

Abstract: This paper presents a new sensor-based online method for generating collision-free paths for differential-drive wheeled mobile robots pursuing a moving target amidst dynamic and static obstacles. At each iteration, the set of all collision-free directions are calculated using velocity vectors of the robot relative to each obstacle, forming the Directive Circle (DC), which is the fundamental concept of our proposed method. Then, the best feasible direction close to the optimal direction to the target is selected from the DC, which prevents the robot from being trapped in local minima. Local movements of the robot are governed by the exponential stabilizing control scheme that provides a smooth motion at each step, while considering the robot's kinematic constraints. The robot is able to catch the target at a desired orientation. Extensive simulations demonstrated the efficiency of the proposed method and its success in coping with complex and highly dynamic environments with arbitrary obstacle shapes.

A laser obstacle warning and avoidance system for unmanned aircraft sense-and-avoid

Abstract: This paper presents an overview of the research activities performed to develop a new scaled variant of the Laser Obstacle Avoidance and Monitoring (LOAM) system for small-to-medium size Unmanned Aircraft (UA) platforms. This LOAM variant (LOAM+) is proposed as one of the non-cooperative sensors employed in the UA Sense-and-Avoid (SAA) system. After a brief description of the LOAM system architecture, the mathematical models developed for obstacle avoidance and calculation of alternative flight path are presented. Additionally, a new formulation is adopted for defining the uncertainty volumes associated with the detected obstacles. Simulation case studies are carried out to evaluate the performances of the avoidance trajectory generation and optimisation algorithms, which demonstrate the ability of LOAM+ to effectively detect and avoid fixed low-level obstacles in the intended path.

Obstacle Avoidance Path Planning of Space Manipulator Based on Improved Artificial Potential Field Method

Abstract: This paper proposes an approach about obstacle collision-free motion planning of space manipulator by utilizing a Configuration-Oriented Artificial Potential Field method in 3-D space environment. Firstly, the artificial potential field method, which is usually used in 2-D space, is extended to 3-D space. Secondly, improving the artificial potential field method enables to carry out obstacle avoidance planning for the configuration of entire space manipulator (including the end-effector and links). Finally, the approach is combined with the inverse kinematics calculation which is based on the Generalized Jacobian Matrix for planning a collision-free motion of space manipulator. At the end of the article, by simulating the method mentioned above, the validity of the proposed method is verified.