Obstacle Motion Research Articles

Agoraphilic navigation algorithm in dynamic environment with obstacles motion tracking and prediction

AbstractThis paper presents a new algorithm to navigate robots in dynamically cluttered environments. The proposed algorithm uses basic concepts of space attraction (hence the term Agoraphilic) to navigate robots through dynamic obstacles. The new algorithm in this paper is an advanced development of the original Agoraphilic navigation algorithm that was only able to navigate robots in static environments. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for a free space (solutions) to follow. Therefore, it is also described as an optimistic navigation algorithm. This algorithm uses only one attractive force created by the available free space. The free-space concept allows the Agoraphilic algorithm to overcome inherited challenges of general navigation algorithms. However, the original Agoraphilic algorithm has the limitation in navigating robots only in static, not in dynamic environments. The presented algorithm was developed to address this limitation of the original Agoraphilic algorithm. The new algorithm uses a developed object tracking module to identify the time-varying free spaces by tracking moving obstacles. The capacity of the algorithm was further strengthened by the new prediction module. Future space prediction allowed the algorithm to make decisions considering future growing/diminishing free spaces. This paper also includes a bench-marking study of the new algorithm compared with a recently published APF-based algorithm under a similar operating environment. Furthermore, the algorithm was validated based on experimental tests and simulation tests.

Predicted Composite Signed-Distance Fields for Real-Time Motion Planning in Dynamic Environments

We present a novel framework for motion planning in dynamic environments that accounts for the predicted trajectories of moving objects. We explore the use of composite signed-distance fields in motion planning and detail how they can be used to generate signed-distance fields (SDFs) in real-time to incorporate predicted obstacle motions; to achieve this, we introduce the concept of predicted signed-distance fields. We benchmark our approach of using composite SDFs against performing exact SDF calculations on the workspace occupancy grid. Our proposed technique generates predictions substantially faster and typically exhibits an 81-97% reduction in time for subsequent predictions. We integrate our framework with GPMP2 to demonstrate a full implementation of our approach in real-time, enabling a 7-DoF Panda manipulator to smoothly avoid a moving obstacle in simulation and hardware experiments.

General equations for landslide-debris impact and their application to debris-flow flexible barrier

The runup height and impact load of landslide debris are the key parameters for design of debris hazard mitigation structures. However, the movement of obstacle (e.g., deformation of flexible barrier) still cannot be quantitatively considered in the existing analytical models. This study proposes general equations for landslide-debris impact under vertical jet and momentum jump modes. Especially, the influence of flow regime and obstacle movement (in the form of a modified Froude number), state of granular material, compressibility (density change), and flow resistance within the jump volume are explicitly considered in the momentum jump model. Through comparison between model prediction and experimental results, it is found that the vertical jet model prediction agrees well with the physical measurements. While the discrepancy between the momentum jump model and experimental results mainly comes from the inappropriate characterization of the unsteady flow properties (velocity and flow depth). Systematic parametric study demonstrates that the functionality of debris-flow flexible barrier primarily relies on the deflection of barrier, and the state of granular material also affects the impact load of dry granular flows.

Constructing paths to avoid multiple obstacles

This article deals with the issues of modeling the trajectory of an object moving on a plane. Its trajectory may intersect with obstacles (circles). The author proposes an algorithm for modifying the initial trajectory of the object taking into account the movement of obstacles. At the beginning, we describe how to divide the initial trajectory into sections. Then we describe how to modify the line on each segment in two cases, depending on the angle between the projection direction and the direction of movement of the obstacle center.

Real-Time Avoidance Strategy of Dynamic Obstacles via Half Model-Free Detection and Tracking With 2D Lidar for Mobile Robots

Avoidance is a necessary capability for a mobile robot to perform tasks, such as delivering objects in household or industrial scenarios. The existing avoidance strategy based on timed elastic band local planner and cost-map provided by robotics operating system cannot realize the excellent performance when a robot and an obstacle both move. In this article, we present a real-time, simple, and reliable approach to detecting and tracking obstacles via a two-dimensional lidar in dynamic scenarios where the mobile robot and the obstacle are moving. Obstacles are represented by a set of points against their outlines and the information of obstacles is initialized and updated via the raw laser measurement. First, the obstacle is detected by three main steps: preprocessing, segmentation, and merging, classification of consequent measurements. Second, we use a hierarchical method to realize data associations for figuring out the corresponding matches among obstacles with the consecutive time. Last, after doing the data association, we need to estimate the motion of the dynamic obstacle for being tracked by the Kalman filter. Extensive experiments performed in the simulation and practical scenarios indicate that the proposed method enables a mobile robot to perform dynamic avoidances efficiently [Real-time Avoidance Strategy of Dynamic Obstacles via Half Model-free Detection and Tracking (T-MECH)].

Dynamic Path Planning for Bevel-Tip Flexible Needle Insertion into Soft Tissue Based on a Real-Time Finite Element Model

This paper presents a novel dynamic path planning methodology for needle steering into the soft tissue. A real-time finite element model is used to simulate the procedure of a flexible needle into the homogeneous soft tissue, which provides the dynamic deformation information for the path planning. The relationship between needle base and tip is formulated as the transformations of homogeneous matrix with quasi-static assumptions. Based on the reachability of the flexible needle, the real-time motions of obstacles and target are considered through the dynamic needle-tissue interactions. A testbed including a XY linear stage, one rotator, and a CCD camera is constructed, and the experiments are designed to validate the proposed method. The 23G PTC needle was inserted into the PVA phantom with markers, and the CCD camera was utilized to record the needle trajectories and motions of target and obstacles. The targeting errors between the experimental and planned paths are less than 1.20 mm, and the distance from the obstacle to needle is not smaller than 1.16 mm. The results demonstrate that the proposed algorithm is effective for online planning the paths in the needle-tissue interactive environment.

Improved Artificial Potential Field Method Applied for AUV Path Planning

With the topics related to the intelligent AUV, control and navigation have become one of the key researching fields. This paper presents a concise and reliable path planning method for AUV based on the improved APF method. AUV can make the decision on obstacle avoidance in terms of the state of itself and the motion of obstacles. The artificial potential field (APF) method has been widely applied in static real-time path planning. In this study, we present the improved APF method to solve some inherent shortcomings, such as the local minima and the inaccessibility of the target. A distance correction factor is added to the repulsive potential field function to solve the GNRON problem. The regular hexagon-guided method is proposed to improve the local minima problem. Meanwhile, the relative velocity method about the moving objects detection and avoidance is proposed for the dynamic environment. This method considers not only the spatial location but also the magnitude and direction of the velocity of the moving objects, which can avoid dynamic obstacles in time. So the proposed path planning method is suitable for both static and dynamic environments. The virtual environment has been built, and the emulation has been in progress in MATLAB. Simulation results show that the proposed method has promising feasibility and efficiency in the AUV real-time path planning. We demonstrate the performance of the proposed method in the real environment. Experimental results show that the proposed method is capable of avoiding the obstacles efficiently and finding an optimized path.

Path planning of UAV for oilfield inspections in a three-dimensional dynamic environment with moving obstacles based on an improved pigeon-inspired optimization algorithm

In recent years, uninhabited aerial vehicles (UAV) have been used for oilfield inspections in many enterprises which can realize oilfield inspections by fewer workers. Path planning is one indispensable element in oilfield inspections through UAVs and it is also a complicated optimal problem. Now, although many researches have been focused on it, they are mainly discussed based on two-dimension planes. In practices, oilfields are complex three-dimensional spaces with many targeted points and moving obstacles between the starting and the ending point, which bring current methods some difficulties. In order to solve this problem, a three-dimensional environment model for oilfields is established for the first time, which includes: a static oil-well equipment, moving obstacles, and so on. Then, a cost function is defined to evaluate the best path, which includes: total length, average height, total time, and total electricity consumption. Finally, an improved pigeon-inspired optimization algorithm is proposed to solve problems about path planning in a three-dimensional dynamic environment of oilfields, which is named PIOFOA. In the PIOFOA, a pigeon-inspired optimization (PIO) algorithm is used to optimize the initial path and a fruit fly optimization algorithm (FOA) is used to continue local optimizations, so as to search the best path after movements of obstacles. Compared with some other methods, simulation results show that the proposed PIOFOA method is more effective.

Autonomous car decision making and trajectory tracking based on genetic algorithms and fractional potential fields

This article deals with the issue of trajectory optimization of autonomous terrestrial vehicles on a specific range handled by the human driver. The main contributions of this paper are a genetic algorithm-potential field combined method for optimized trajectory planning, the definition of the multi-criteria optimization problem by including a time variable, dynamical vehicle constraints, obstacle motion for collision avoidance, and improvements on the attractive and repulsive potential field definitions. The main interests of the proposed method are its efficiency even in only arc-connected spaces with holes, trajectory optimality thanks to the genetic algorithm that minimizes multi-criteria optimization, reactivity thanks to the potential field through the consideration for nature and motion of obstacles, its orientation toward situations a human driver would consider, and finally the inclusion of constraints to avoid danger and to take into account vehicle dynamics. The global trajectory, optimized through genetic algorithm, is used as a reference in a fractional potential field, which is a reactive local path planning method. The repulsive potential field is made safer by adding fractional orders to the obstacles, and the attractive potential field is improved by creating a dynamical optimal target seen from a robust control point of view. This target replaces the unique attractive potential field point and avoids its drawbacks such as local minima. Autonomous car simulation results are given for a crossroad and an overtaking scenarios.

A 3D reactive collision avoidance algorithm for underactuated underwater vehicles

AbstractAvoiding collisions is an essential goal of the control system of autonomous vehicles. This paper presents a reactive algorithm for avoiding obstacles in a three‐dimensional space, and shows how the algorithm can be applied to an underactuated underwater vehicle. The algorithm is based on maintaining a constant avoidance angle to the obstacle, which ensures that a guaranteed minimum separation distance is achieved. The algorithm can thus be implemented without knowledge of the obstacle shape. The avoidance angle is designed to compensate for obstacle movement, and the flexibility of operating in 3D can be utilized to implement traffic rules or operational constraints. We exemplify this by incorporating safety constraints on the vehicle pitch and by making the vehicle seek to move behind the obstacle, while also minimizing the required control effort. The underactuation of the vehicle induces a sway and heave movement while turning. To avoid uncontrolled gliding into the obstacle, we account for this movement using a Flow frame controller, which controls the direction of the vehicle's velocity rather than just the pitch and yaw. We derive conditions under which it is ensured that the resulting maneuver is safe, and these results are verified trough simulations and through full‐scale experiments on the Hugin HUS autonomous underwater vehicle. The latter demonstrates the performance of the proposed algorithm when applied to a case with unmodeled disturbances and sensor noise, and shows how the modular nature of the collision avoidance algorithm allows it to be applied on top of a commercial control system.

Wind and obstacle motion affect honeybee flight strategies in cluttered environments.

Bees often forage in habitats with cluttered vegetation and unpredictable winds. Navigating obstacles in wind presents a challenge that may be exacerbated by wind-induced motions of vegetation. Although wind-blown vegetation is common in natural habitats, we know little about how the strategies of bees for flying through clutter are affected by obstacle motion and wind. We filmed honeybees Apis mellifera flying through obstacles in a flight tunnel with still air, headwinds or tailwinds. We tested how their ground speeds and centering behavior (trajectory relative to the midline between obstacles) changed when obstacles were moving versus stationary, and how their approach strategies affected flight outcome (successful transit versus collision). We found that obstacle motion affects ground speed: bees flew slower when approaching moving versus stationary obstacles in still air but tended to fly faster when approaching moving obstacles in headwinds or tailwinds. Bees in still air reduced their chances of colliding with obstacles (whether moving or stationary) by reducing ground speed, whereas flight outcomes in wind were not associated with ground speed, but rather with improvement in centering behavior during the approach. We hypothesize that in challenging flight situations (e.g. navigating moving obstacles in wind), bees may speed up to reduce the number of wing collisions that occur if they pass too close to an obstacle. Our results show that wind and obstacle motion can interact to affect flight strategies in unexpected ways, suggesting that wind-blown vegetation may have important effects on foraging behaviors and flight performance of bees in natural habitats.

On-demand distance vector refinement-based car networking stable routing

Due to the high-speed movement of vehicles and obstacles in urban scenes, the communication paths between vehicles become extremely fragile. Specific to the routing problem of VANETs, a path criterion-based on-demand distance vector PA-AODV routing algorithm is proposed. The PA-AODV routing algorithm makes full use of the characteristics of AODV routing, and improves its routing decision. By calculating the path criterion weight and preferentially selecting the path with small weight for data transmission, the path stability is thus improved. The experimental data show that the proposed PA-AODV routing reduces the data packets loss rate and also shortens the end-to-end transmission delay.

Reactive Collision Avoidance Algorithm for UAV Using Bounding Tube Against Multiple Moving Obstacles

A reactive collision avoidance algorithm is proposed to enable safe autonomous flight in the presence of multiple dynamic obstacles. A position-controlled hexacopter equipped with a visual sensor obtaining obstacle information is considered as an Unmanned Aerial Vehicle (UAV) platform. The proposed method centers on the concept of bounding tube which intrinsically extends the static bounding box to incorporate forthcoming movement of the obstacles into the collision avoidance framework. The processing pipeline consists of separate components for each of the sequential tasks in obstacle sensing and tracking. Computation of a spherical bounding box for each obstacle is followed by discrete-time Kalman filtering for prediction of obstacle trajectory to detect potential collision. If the current course of UAV turns out highly likely to end in collision with any of the obstacles, the vehicle steers to an aiming point chosen from among the bundle of candidates produced by constructing a bounding tube that takes account of predicted obstacle motion. The bounding-tube-based aiming point generation extends seamlessly to the case with multiple moving obstacles through running in series with multi-obstacle track management that combines hierarchical clustering of sensory data points for obstacle identification and a simple geometric method for data association. Numerical simulations are conducted to verify the performance of the proposed collision avoidance algorithm.

Towards Integrated Perception and Motion Planning with Distributionally Robust Risk Constraints

Abstract Safely deploying robots in uncertain and dynamic environments requires a systematic accounting of various risks, both within and across layers in an autonomy stack from perception to motion planning and control. Many widely used motion planning algorithms do not adequately incorporate inherent perception and prediction uncertainties, often ignoring them altogether or making questionable assumptions of Gaussianity. We propose a distributionally robust incremental sampling-based motion planning framework that explicitly and coherently incorporates perception and prediction uncertainties. We design output feedback policies and consider moment-based ambiguity sets of distributions to enforce probabilistic collision avoidance constraints under the worst-case distribution in the ambiguity set. Our solution approach, called Output Feedback Distributionally Robust RRT (OFDR-RRT ), produces asymptotically optimal risk-bounded trajectories for robots operating in dynamic, cluttered, and uncertain environments, explicitly incorporating mapping and localization error, stochastic process disturbances, unpredictable obstacle motion, and uncertain obstacle locations. Numerical experiments illustrate the effectiveness of the proposed algorithm.

Multiquery Motion Planning in Uncertain Spaces: Incremental Adaptive Randomized Roadmaps

Abstract Sampling-based motion planning is a powerful tool in solving the motion planning problem for a variety of different robotic platforms. As its application domains grow, more complicated planning problems arise that challenge the functionality of these planners. One of the main challenges in the implementation of a sampling-based planner is its weak performance when reacting to uncertainty in robot motion, obstacles motion, and sensing noise. In this paper, a multi-query sampling-based planner is presented based on the optimal probabilistic roadmaps algorithm that employs a hybrid sample classification and graph adjustment strategy to handle diverse types of planning uncertainty such as sensing noise, unknown static and dynamic obstacles and an inaccurate environment map in a discrete-time system. The proposed method starts by storing the collision-free generated samples in a matrix-grid structure. Using the resulting grid structure makes it computationally cheap to search and find samples in a specific region. As soon as the robot senses an obstacle during the execution of the initial plan, the occupied grid cells are detected, relevant samples are selected, and in-collision vertices are removed within the vision range of the robot. Furthermore, a second layer of nodes connected to the current direct neighbors are checked against collision, which gives the planner more time to react to uncertainty before getting too close to an obstacle. The simulation results for problems with various sources of uncertainty show a significant improvement compared with similar algorithms in terms of the failure rate, the processing time and the minimum distance from obstacles. The planner is also successfully implemented and tested on a TurtleBot in four different scenarios with uncertainty.

Multi-Behavior Fusion Based Potential Field Method for Path Planning of Unmanned Surface Vessel

The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.

Phone messages affect the detection of approaching pedestrians in healthy young and older adults immersed in a virtual community environment.

BackgroundMobile phones are increasingly associated with accidents while walking. Little is known, however, about the impact of phone messaging on the actual perception of other pedestrians. This study aimed to investigate the extent to which the detection of approaching pedestrians is affected by the sensory modality (text or audio) of phone messages in young vs. older adults.MethodsEighteen healthy young (24 ± 2.9 years) and 15 older adults (68 ± 4.2 years) performed a phone message deciphering task, an obstacle detection task, and a dual-task condition combining both tasks. Participants were tested while seated and viewing a virtual subway station (VE) in a helmet mounted display. As they were passively moved within the VE one of three virtual pedestrians randomly approached them from the center (0°), right (+40°) or left (+40°). When present, phone message conditions were delivered at obstacle movement onset and presented either as (1) text messages on the screen of a virtual phone or (2) audio messages delivered through earphones. Participants were instructed to press a joystick button as soon as they detected the approaching virtual pedestrian and to report the message content at the end of the trials.ResultsYoung and older participants showed delayed obstacle detection times with vs. without text messages. Older adults further showed reduced accuracy of message report for texts compared to audio messages. In both groups, audio messages yielded no difference in obstacle detection time or accuracy of message report compared to the no message condition.ConclusionsFindings indicate that text messages prolong the detection of approaching pedestrians, suggesting that they compromise safe ambulation in community environments. Older adults, who show larger deteriorations on the obstacle detection and message deciphering tasks, may be at even greater risk of collision. Audio messages could be a safer alternative for on-the-go communication.

VUNet: Dynamic Scene View Synthesis for Traversability Estimation Using an RGB Camera

We present VUNet, a novel view(VU) synthesis method for mobile robots in dynamic environments, and its application to the estimation of future traversability. Our method predicts future images for given virtual robot velocity commands using only RGB images at previous and current time steps. The future images result from applying two types of image changes to the previous and current images: first, changes caused by different camera pose. Second, changes due to the motion of the dynamic obstacles. We learn to predict these two types of changes disjointly using two novel network architectures, SNet and DNet. We combine SNet and DNet to synthesize future images that we pass to our previously presented method GONet [N. Hirose, A. Sadeghian, M. Vazquez, P. Goebel, and S. Savarese, “Gonet: A semi-supervised deep learning approach for traversability estimation,” in Proc. IEEE International Conference on Intelligent Robots and Systems, 2018, pp. 3044–3051] to estimate the traversable areas around the robot. Our quantitative and qualitative evaluation indicate that our approach for view synthesis predicts accurate future images in both static and dynamic environments. We also show that these virtual images can be used to estimate future traversability correctly. We apply our view synthesis-based traversability estimation method to two applications for assisted teleoperation.

КОНСТРУИРОВАНИЕ ТРАЕКТОРИЙ ОБХОДА НЕСКОЛЬКИХ ПРЕПЯТСТВИЙ

This article deals with the issues of modeling the trajectory of an object moving on a plane, on the path of which there may be obstacles. The authors propose an algorithm for changing the initial trajectory of the object taking into account the movement of obstacles. At the beginning, we describe how to divide the initial trajectory into sections. Then we describe how to modify the line on each segment in two cases, depending on the angle between the projection direction and the direction of movement of the center of the obstacle.

Bearing-Only Obstacle Avoidance Based on Unknown Input Observer and Angle-Dependent Artificial Potential Field.

This paper presents the problem of obstacle avoidance with bearing-only measurements in the case that the obstacle motion is model-free, i.e., its acceleration is absolutely unknown, which cannot be dealt with by the mainstream Kalman-like schemes based on the known motion model. First, the essential reason of the collision caused by local minimum problem in the standard artificial potential field method is proved, and hence a revised method with angle dependent factor is proposed. Then, an unknown input observer is proposed to estimate the position and velocity of the obstacle. Finally, the numeric simulation demonstrates the effectiveness in terms of estimation accuracy and terminative time.