Showing papers on "Obstacle avoidance published in 2015"

Optimization‐based autonomous racing of 1:43 scale RC cars

Abstract: Summary This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoiding opponents. Two different control formulations are presented. The first controller employs a two-level structure, consisting of a path planner and a nonlinear model predictive controller (NMPC) for tracking. The second controller combines both tasks in one nonlinear optimization problem (NLP) following the ideas of contouring control. Linear time varying models obtained by linearization are used to build local approximations of the control NLPs in the form of convex quadratic programs (QPs) at each sampling time. The resulting QPs have a typical MPC structure and can be solved in the range of milliseconds by recent structure exploiting solvers, which is key to the real-time feasibility of the overall control scheme. Obstacle avoidance is incorporated by means of a high-level corridor planner based on dynamic programming, which generates convex constraints for the controllers according to the current position of opponents and the track layout. The control performance is investigated experimentally using 1:43 scale RC race cars, driven at speeds of more than 3 m/s and in operating regions with saturated rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on embedded computing platforms, demonstrating the real-time feasibility and high performance of optimization-based approaches for autonomous racing. Copyright © 2014 John Wiley & Sons, Ltd.

Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization

Abstract: Accurately estimating a robot's pose relative to a global scene model and precisely tracking the pose in real-time is a fundamental problem for navigation and obstacle avoidance tasks. Due to the computational complexity of localization against a large map and the memory consumed by the model, state-of-the-art approaches are either limited to small workspaces or rely on a server-side system to query the global model while tracking the pose locally. The latter approaches face the problem of smoothly integrating the server's pose estimates into the trajectory computed locally to avoid temporal discontinuities. In this paper, we demonstrate that large-scale, real-time pose estimation and tracking can be performed on mobile platforms with limited resources without the use of an external server. This is achieved by employing map and descriptor compression schemes as well as efficient search algorithms from computer vision. We derive a formulation for integrating the global pose information into a local state estimator that produces much smoother trajectories than current approaches. Through detailed experiments, we evaluate each of our design choices individually and document its impact on the overall system performance, demonstrating that our approach outperforms state-of-the-art algorithms for localization at scale.

Multivehicle Cooperative Driving Using Cooperative Perception: Design and Experimental Validation

Abstract: In this paper, we present a multivehicle cooperative driving system architecture using cooperative perception along with experimental validation. For this goal, we first propose a multimodal cooperative perception system that provides see-through, lifted-seat, satellite and all-around views to drivers. Using the extended range information from the system, we then realize cooperative driving by a see-through forward collision warning, overtaking/lane-changing assistance, and automated hidden obstacle avoidance. We demonstrate the capabilities and features of our system through real-world experiments using four vehicles on the road.

Efficient mixed-integer planning for UAVs in cluttered environments

Abstract: We present a new approach to the design of smooth trajectories for quadrotor unmanned aerial vehicles (UAVs), which are free of collisions with obstacles along their entire length. To avoid the non-convex constraints normally required for obstacle-avoidance, we perform a mixed-integer optimization in which polynomial trajectories are assigned to convex regions which are known to be obstacle-free. Prior approaches have used the faces of the obstacles themselves to define these convex regions. We instead use IRIS, a recently developed technique for greedy convex segmentation [1], to pre-compute convex regions of safe space. This results in a substantially reduced number of integer variables, which improves the speed with which the optimization can be solved to its global optimum, even for tens or hundreds of obstacle faces. In addition, prior approaches have typically enforced obstacle avoidance at a finite set of sample or knot points. We introduce a technique based on sums-of-squares (SOS) programming that allows us to ensure that the entire piecewise polynomial trajectory is free of collisions using convex constraints. We demonstrate this technique in 2D and in 3D using a dynamical model in the Drake toolbox for Matlab [2].

Model Predictive Control for Spacecraft Rendezvous and Docking: Strategies for Handling Constraints and Case Studies

Abstract: This paper presents a strategy and case studies of spacecraft relative motion guidance and control based on the application of linear quadratic model predictive control (MPC) with dynamically reconfigurable constraints. The controller is designed to transition between the MPC guidance during a spacecraft rendezvous phase and MPC guidance during a spacecraft docking phase, with each phase having distinct requirements, constraints, and sampling rates. Obstacle avoidance is considered in the rendezvous phase, while a line-of-sight cone constraint, bandwidth constraints on the spacecraft attitude control system, and exhaust plume direction constraints are addressed during the docking phase. The MPC controller is demonstrated in simulation studies using a nonlinear model of spacecraft orbital motion. The implementation uses estimates of spacecraft states derived from relative angle and range measurements, and is robust to estimator dynamics and measurement noise.

StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation.

Abstract: General obstacle detection is a key enabler for obstacle avoidance in mobile robotics and autonomous driving. In this paper we address the task of detecting the closest obstacle in each direction from a driving vehicle. As opposed to existing methods based on 3D sensing we use a single color camera. The main novelty in our approach is the reduction of the task to a column-wise regression problem. The regression is then solved using a deep convolutional neural network (CNN). In addition, we introduce a new loss function based on a semi-discrete representation of the obstacle position probability to train the network. The network is trained using ground truth automatically generated from a laser-scanner point cloud. Using the KITTI dataset, we show that the our monocularbased approach outperforms existing camera-based methods including ones using stereo. We also apply the network on the related task of road segmentation achieving among the best results on the KITTI road segmentation challenge.

Mixed Integer Quadratic Program trajectory generation for a quadrotor with a cable-suspended payload

Abstract: In this paper, we present a trajectory planning method to navigate a quadrotor with a cable-suspended payload through known obstacle-filled environments. We model the system as a hybrid dynamical system and formulate the trajectory generation problem as a Mixed Integer Quadratic Program (MIQP). Specifically, we address two novel challenges. First, we plan for a multi-body system, and obstacle avoidance must be guaranteed for the quadrotor, load, and the cable. Second, our method accommodates transitions between subsystems of the hybrid dynamical system, allowing for maneuvers that would otherwise be infeasible if the cable were constrained to remain taut. Numerical and experimental results validate the proposed approach for the full hybrid system.

Obstacle Avoidance Method for Wheeled Mobile Robots Using Interval Type-2 Fuzzy Neural Network

Abstract: This paper proposes an obstacle avoidance method in the position stabilization of the wheeled mobile robots using interval type-2 fuzzy neural network (IT2FNN). Previously, we have proposed the unified strategies of obstacle avoidance and shooting method of the robot soccer system using type-1 fuzzy neural network (T1FNN). Even though the previous T1FNN method can achieve the required tasks, the performance of the previous T1FNN method is not satisfactory in the following sense. The previous T1FNN cannot reduce the influence of uncertainties effectively because it uses the crisp set as the membership values. In addition, it can result in the large oscillation behavior during the obstacle avoidance. Accordingly, we should design the IT2FNN method to improve the performance with smoother behavior as well as improved obstacle avoidance. The proposed IT2FNN method has the fuzzy neural network structure different from the T1FNN. Since the IT2FNN uses the fuzzy set instead of the crisp set as the membership values and it is robust against uncertainties, the performance of the robot behavior can be significantly improved especially in the presence of obstacles. Both simulation and experimental results using the actual wheeled mobile robot with the vision information are provided to show the validity and the advantages of the proposed method.

Path Planning for Single Unmanned Aerial Vehicle by Separately Evolving Waypoints

Abstract: Evolutionary algorithm-based unmanned aerial vehicle (UAV) path planners have been extensively studied for their effectiveness and flexibility. However, they still suffer from a drawback that the high-quality waypoints in previous candidate paths can hardly be exploited for further evolution, since they regard all the waypoints of a path as an integrated individual. Due to this drawback, the previous planners usually fail when encountering lots of obstacles. In this paper, a new idea of separately evaluating and evolving waypoints is presented to solve this problem. Concretely, the original objective and constraint functions of UAVs path planning are decomposed into a set of new evaluation functions, with which waypoints on a path can be evaluated separately. The new evaluation functions allow waypoints on a path to be evolved separately and, thus, high-quality waypoints can be better exploited. On this basis, the waypoints are encoded in a rotated coordinate system with an external restriction and evolved with JADE, a state-of-the-art variant of the differential evolution algorithm. To test the capabilities of the new planner on planning obstacle-free paths, five scenarios with increasing numbers of obstacles are constructed. Three existing planners and four variants of the proposed planner are compared to assess the effectiveness and efficiency of the proposed planner. The results demonstrate the superiority of the proposed planner and the idea of separate evolution.

Reactive avoidance using embedded stereo vision for MAV flight

Abstract: High speed, low latency obstacle avoidance is essential for enabling Micro Aerial Vehicles (MAVs) to function in cluttered and dynamic environments. While other systems exist that do high-level mapping and 3D path planning for obstacle avoidance, most of these systems require high-powered CPUs on-board or off-board control from a ground station.

The obstacle detection and obstacle avoidance algorithm based on 2-D lidar

Abstract: Obstacle avoidance ability is the significant embodiment of the ground mobile robot, and the basic guarantee of the ground mobile robot to perform various tasks. Obstacle avoidance technologies are divided into two kinds, one is based on the global map and another is based on sensors respectively. This paper mainly aims at the local obstacle avoidance method based on sensors. The study of obstacle detection and obstacle avoidance are two inseparable parts in the research of obstacle avoidance ability. This paper proposes an efficient obstacle detection and obstacle avoidance algorithm based on 2-D lidar. A method is proposed to get the information of obstacles by filtering and clustering the laser-point cloud data. Also, this method generates the forward angle and velocity of robot based on the principle of minimum cost function. The obstacle detection and obstacle avoidance algorithm has advantages of a simple mathematical model and good real-time performance. The effectiveness of the proposed algorithm is verified on MATLAB simulation platform.

Continuous humanoid locomotion over uneven terrain using stereo fusion

Abstract: For humanoid robots to fulfill their mobility potential they must demonstrate reliable and efficient locomotion over rugged and irregular terrain. In this paper we present the perception and planning algorithms which have allowed a humanoid robot to use only passive stereo imagery (as opposed to actuating a laser range sensor) to safely plan footsteps to continuously walk over rough and uneven surfaces without stopping. The perception system continuously integrates stereo imagery to build a consistent 3D model of the terrain which is then used by our footstep planner which reasons about obstacle avoidance, kinematic reachability and foot rotation through mixed-integer quadratic optimization to plan the required step positions. We illustrate that our stereo imagery fusion approach can measure the walking terrain with sufficient accuracy that it matches the quality of terrain estimates from LIDAR. To our knowledge this is the first such demonstration of the use of computer vision to carry out general purpose terrain estimation on a locomoting robot — and additionally to do so in continuous motion. A particular integration challenge was ensuring that these two computationally intensive systems operate with minimal latency (below 1 second) to allow re-planning while walking. The results of extensive experimentation and quantitative analysis are also presented. Our results indicate that a laser range sensor is not necessary to achieve locomotion in these challenging situations.

Obstacle detection for self-driving cars using only monocular cameras and wheel odometry

Abstract: Mapping the environment is crucial to enable path planning and obstacle avoidance for self-driving vehicles and other robots. In this paper, we concentrate on ground-based vehicles and present an approach which extracts static obstacles from depth maps computed out of multiple consecutive images. In contrast to existing approaches, our system does not require accurate visual inertial odometry estimation but solely relies on the readily available wheel odometry. To handle the resulting higher pose uncertainty, our system fuses obstacle detections over time and between cameras to estimate the free and occupied space around the vehicle. Using monocular fisheye cameras, we are able to cover a wider field of view and detect obstacles closer to the car, which are often not within the standard field of view of a classical binocular stereo camera setup. Our quantitative analysis shows that our system is accurate enough for navigation purposes of self-driving cars and runs in real-time.

Localization, obstacle avoidance planning and control of a cooperative cable parallel robot for multiple mobile cranes

Abstract: This paper addresses the cooperative problems in terms of localization, obstacle avoidance planning and automatic leveling control for a cable parallel robot for multiple mobile cranes (CPRMCs). The design model of the CPRMCs is elaborated on. The three-dimensional grid map method is utilized to plot the environment map based on the operation environment model. By combining the relative localization method with the absolute localization method, a cooperative localization scheme of the CPRMCs is developed, and an improved localization algorithm is designed on the basis of multilateration method. Then, according to the grid-based artificial potential field method, a global path planning of the CPRMCs is performed. Considering the possible collision of the single mobile crane, the sensor technology is applied to the cooperative obstacle avoidance. In addition, a four-point collaborative leveling method is adopted for automatic leveling control of the platform of the CPRMCs. Finally, the effectiveness of the CPRMCs system is verified through simulations. Cable parallel robots for multiple mobile cranes are designed.Localization analysis of the robots are reported.Obstacle avoidance planning and automatic leveling control are performed.Illustrative simulation studies highlight the performance of the robots.

Virtual proxemics: Locomotion in the presence of obstacles in large immersive projection environments

Abstract: In this paper, we investigate obstacle avoidance behavior during real walking in a large immersive projection setup. We analyze the walking behavior of users when avoiding real and virtual static obstacles. In order to generalize our study, we consider both anthropomorphic and inanimate objects, each having his virtual and real counterpart. The results showed that users exhibit different locomotion behaviors in the presence of real and virtual obstacles, and in the presence of anthropomorphic and inanimate objects. Precisely, the results showed a decrease of walking speed as well as an increase of the clearance distance (i. e., the minimal distance between the walker and the obstacle) when facing virtual obstacles compared to real ones. Moreover, our results suggest that users act differently due to their perception of the obstacle: users keep more distance when the obstacle is anthropomorphic compared to an inanimate object and when the orientation of anthropomorphic obstacle is from the profile compared to a front position. We discuss implications on future large shared immersive projection spaces.

Real-time multisensor people tracking for human-robot spatial interaction

Abstract: All currently used mobile robot platforms are able to navigate safely through their environment, avoiding static and dynamic obstacles. However, in human populated environments mere obstacle avoidance is not sufficient to make humans feel comfortable and safe around robots. To this end, a large community is currently producing human-aware navigation approaches to create a more socially acceptable robot behaviour. Amajorbuilding block for all Human-Robot Spatial Interaction is the ability of detecting and tracking humans in the vicinity of the robot. We present a fully integrated people perception framework, designed to run in real-time on a mobile robot. This framework employs detectors based on laser and RGB-D data and a tracking approach able to fuse multiple detectors using different versions of data association and Kalman filtering. The resulting trajectories are transformed into Qualitative Spatial Relations based on a Qualitative Trajectory Calculus, to learn and classify different encounters using a Hidden Markov Model based representation. We present this perception pipeline, which is fully implemented into the Robot Operating System (ROS), in a small proof of concept experiment. All components are readily available for download, and free to use under the MIT license, to researchers in all fields, especially focussing on social interaction learning by providing different kinds of output, i.e. Qualitative Relations and trajectories.

Keep it brief: Scalable creation of compressed localization maps

Abstract: Robust, scalable localization unlocks path-planning, obstacle avoidance as well as manipulation and thus is a core competency for many robotic applications. However, as we leave the lab and move out in the world, models of the environment no longer span distances of meters but kilometers in length. Now, gigabytes instead of megabytes of memory are required to hold the model of the environment required for localization. Discarding data and keeping the map representation compact is thus essential for any meaningful application. This paper presents and evaluates a map compression algorithm that approaches this data-reduction as an constrained optimization problem. At the core of the algorithm is the concept of a Summary Map, a reduced map representation that includes only the landmarks that are deemed most useful for place recognition. To assign landmarks to the map we have to satisfy the conflicting goals of map coverage and localizability as well as our tight memory budget. While using an optimization approach for compression is not novel, in this paper we propose adaptations to drastically reduce the computational requirements. Our approach improves scalability from trajectories of a few tens of meters manageable by the state of the art to virtually unlimited dataset sizes in our system. We evaluate the performance of various compression levels as well as several methods for selecting the best localization landmarks from outdoor datasets.

A survey on unmanned aerial vehicle collision avoidance systems

Abstract: Collision avoidance is a key factor in enabling the integration of unmanned aerial vehicle into real life use, whether it is in military or civil application. For a long time there have been a large number of works to address this problem; therefore a comparative summary of them would be desirable. This paper presents a survey on the major collision avoidance systems developed in up to date publications. Each collision avoidance system contains two main parts: sensing and detection, and collision avoidance. Based on their characteristics each part is divided into different categories; and those categories are explained, compared and discussed about advantages and disadvantages in this paper.

Safe Positively Invariant Sets for Spacecraft Obstacle Avoidance

Abstract: This paper presents an obstacle avoidance method for spacecraft relative motion control. In this approach, a connectivity graph is constructed for a set of relative frame points, which form a virtu...

UAV obstacle avoidance using potential field under dynamic environment

Abstract: In this paper, the potential field principle is applied to several UAVs (Unmanned Aerial Vehicles) for optimal path planning. Each UAV has its own goals and it is used the attractive potential field to reach the goals. On the contrary, each UAV is considered as obstacle for other UAVs that must be avoided. In this research, there are two types of obstacles, i.e the static and dynamic. The repulsive potential field principle is used to avoid for both static and dynamic obstacles. The whole method is implemented into Parrot AR Drone 2.0 Quadcopter model of UAV and simulated in Gazebo Simulator by Robot Operating System (ROS). The results of this research are to control the thrust of quadcopter so that it is in flying position, the value of value of roll (α) or pitch (β) must set to not equal or approaching 90° or −90° and not between −180° and 180° or between −90° and 90°. Based on the dynamic obstacle performance test with parameter tuning, the optimal avoidance is when the η value is 7.8 while when in static and dynamic test with parameter tuning, the optimal avoidance is when the η value is 7.9 noted by the fastest time and the shortest path.

High precision landing site mapping and rover localization for Chang’e-3 mission

Abstract: This paper presents the comprehensive results of landing site topographic mapping and rover localization in Chang’e-3 mission. High-precision topographic products of the landing site with extremely high resolutions (up to 0.05 m) were generated from descent images and registered to CE-2 DOM. Local DEM and DOM with 0.02 m resolution were produced routinely at each waypoint along the rover traverse. The lander location was determined to be (19.51256°W, 44.11884°N, −2615.451 m) using a method of DOM matching. In order to reduce error accumulation caused by wheel slippage and IMU drift in dead reckoning, cross-site visual localization and DOM matching localization methods were developed to localize the rover at waypoints; the overall traveled distance from the lander is 114.8 m from cross-site visual localization and 111.2 m from DOM matching localization. The latter is of highest accuracy and has been verified using a LRO NAC image where the rover trajeactory is directly identifiable. During CE-3 mission operations, landing site mapping and rover localization products including DEMs and DOMs, traverse maps, vertical traverse profiles were generated timely to support teleoperation tasks such as obstacle avoidance and rover path planning.

A new hybrid optimization algorithm for multiple mobile robots navigation based on the CS-ANFIS approach

Abstract: In this article, a novel hybrid navigation technique is discussed for multiple mobile robots in an unknown clustered environment. This new hybrid navigational methodology is based on the cuckoo search algorithm for training the premise part and the least square estimation method for training the consequent parameters of the adaptive neuro-fuzzy inference system. The proposed hybrid optimal path planner is developed based upon a reference motion, direction, distances between the robot and the obstacles, and distances between the robot and the target, to calculate the suitable steering angle. In order to avoid collision against one another, a set of collision prevention rules are embedded into each robot controller, using the Petri Net model. The effectiveness of the algorithm has been demonstrated through a series of simulation experiments. The experimental results are conducted in the laboratory, using a real mobile robot to validate the versatility and effectiveness of the proposed hybrid technique. A comparison of the simulation and experimental results showed, that there is good agreement between them. The results obtained from the proposed hybrid technique are validated by comparison with the results from other intelligent techniques. Finally, it is concluded that the proposed hybrid methodology is efficient and robust in the sense, that it can be implemented in the robot for navigation in any complex terrain.

Distributed graph-based convoy control for networked intelligent vehicles

Abstract: This paper presents an approach for formation control of multi-lane vehicular convoys in highways. We extend a Laplacian graph-based, distributed control law such that networked intelligent vehicles can join or leave the formation dynamically without jeopardizing the ensemble's stability. Additionally, we integrate two essential control behaviors for lane-keeping and obstacle avoidance into the controller. To increase the performance of the convoy controller in terms of formation maintenance and fuel economy, the parameters of the controller are optimized in realistic scenarios using Particle Swarm Optimization (PSO), a powerful metaheuristic optimization method well-suited for large parameter spaces. The performances of the optimized controllers are evaluated in high-fidelity multi-vehicle simulations outlining the efficiency and robustness of the proposed strategy.