Presence Of Dynamic Obstacles Research Articles

Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS

Robotic autonomous navigation in dynamic environments is a complex problem, as traditional planners may fail to take dynamic obstacles and their variables into account. The Strategy-based Dynamic Object Velocity Space (S-DOVS) planner has been proposed as a solution to navigate in such scenarios. However, it has a number of limitations, such as inability to reach a goal in a large known map, avoid convex objects, or handle trap situations. In this article, we present a modified version of the S-DOVS planner that is integrated into a full navigation stack, which includes a localization system, obstacle tracker, and novel waypoint generator. The complete system takes into account robot kinodynamic constraints and is capable of navigating through large scenarios with known map information in the presence of dynamic obstacles. Extensive simulation and ground robot experiments demonstrate the effectiveness of our system even in environments with dynamic obstacles and replanning requirements, and show that our waypoint generator outperforms other approaches in terms of success rate and time to reach the goal when combined with the S-DOVS planner. Overall, our work represents a step forward in the development of robust and reliable autonomous navigation systems for real-world scenarios.

To Continue Transmission or to Explore Relays: Millimeter Wave D2D Communication in Presence of Dynamic Obstacles

Millimeter wave (mmWave) device to device (D2D) communication is highly susceptible to obstacles due to severe penetration losses. Dynamic obstacles may cause unpredictable fluctuations to D2D channel quality and hence a D2D relay initially chosen by base station (BS) might undergo failed transmissions resulting in severe packet loss and delay. This local information regarding link quality deterioration needs to be informed to the BS by the user equipments (UEs) which may result in some delay. Also, exploring a new relay on mmWave channel results in significant delay due to directional search. Hence the following optimal sequential decision must be made when packet loss occurs: whether to explore for a new relay link considering exploration cost, or to continue communication via the existing relay. We model this sequential decision problem locally at each UE as partially observable Markov decision process to capture uncertainty in D2D links while minimizing delay. We derive an optimal threshold policy for both positively and negatively correlated links. We also provide a simplified and easy to implement policy based on successive acknowledgment failures for positively correlated links. Through simulation, we validate theoretical findings and demonstrate that our approach outperforms existing state of the art algorithms.

Trajectory planning for mechanical systems in the presence of dynamic obstacles

This paper deals with an optimal motion planning method for mechanical systems subjected to velocity and acceleration constraints in the presence of dynamical geometric obstacles. Using a state parametrization method based on time polynomials the trajectory planning problem is reformulated as a constrained nonlinear optimization problem which is solved using a numerical optimization toolbox. Quadrotor motion planning in the horizontal plane is considered as an illustration example.

Non-Conservative Trajectory Planning for Automated Vehicles by Estimating Intentions of Dynamic Obstacles

Motion planning algorithms for urban automated driving must handle uncertainty due to unknown intention and future motion of Dynamic Obstacles (DOs). Considering a single future trajectory for each DO is not adequate, especially in urban frameworks where traffic participants exhibit very different behaviors. However, including multiple candidate trajectories representing different behaviors results in an excess of conservatism. We present a novel combination of the Interactive Multiple Model (IMM) algorithm and Stochastic Model Predictive Control (SMPC) that allows non-conservative safe motion planning in presence of DOs with unknown intention. We introduce a framework based on LQR and IMM to predict multiple candidate future trajectories, each interpreted as a high-level intention that the DO is pursuing, and dynamically estimate their probabilities. Then, the future trajectory of the automated vehicle is iteratively planned in an SMPC fashion, in which collision avoidance constraints are generated for multiple future trajectories of each DO, with a focus on the most likely. Our method improves safe motion planning fully exploiting the benefits of multi-modal predictions of the DOs, avoiding excessive conservatism. Advantages of the proposed method are discussed through simulations in the CARLA environment.

Efficient Spatiotemporal Graph Search for Local Trajectory Planning on Oval Race Tracks

Autonomous racing has increasingly become a research subject as it provides insights into dynamic, high-speed situations. One crucial aspect of handling these situations, especially in the presence of dynamic obstacles, is the generation of a collision-free trajectory that represents a safe behavior and is also competitive in the case of racing. We propose a local planning approach that generates such trajectories for a racing car on an oval race track by searching a spatiotemporal graph. A considerable challenge of search-based methods in a spatiotemporal domain is the curse of dimensionality. Therefore, we propose how a previously presented graph structure that is based on intervals instead of discrete values can be searched more efficiently without losing optimality by using a uniform-cost search strategy. We extend the search method to make it anytime-capable so that it can provide a suboptimal trajectory even if the search has to be terminated early. The graph-based planning approach allows us to apply a flexible cost function so that our approach can operate fully autonomously on an oval race track, including the pit lane. We present a cost function for oval racing and explain how the terms contribute to the desired behaviors. This is supported by results with a full-scale prototype.

Research on Dynamic Obstacle Avoidance Method of Manipulator Based on Binocular Vision

Background: With the development of robotics, more and more robots are being used in industrial production. However, as the production environment surrounding the robots becomes increasingly complex, there is a need for more intelligent industrial robots. For improving the intelligence of industrial robots, the most important thing is to ensure that they can carry out safe industrial production activities in a complex production environment. Therefore, studying the autonomous dynamic obstacle avoidance path planning of industrial robots in complex environments is of great significance for improving the intelligence of industrial robots and the application of human-machine collaboration. Objective: The main purpose of this paper is to improve the traditional artificial potential field method. It aims to improve the disadvantages of the traditional artificial potential field method, such as falling into the local minimum and failing to reach the target. Secondly, the background difference method, which is based on binocular vision and Kalman filtering algorithm, is used, and the environmental map containing the static and dynamic obstacles is obtained. After obtaining the information on the position of static and dynamic obstacles, the robot arm can make good use of the improved artificial potential field method to plan its own trajectory, thus realizing the dynamic obstacle avoidance of the robot arm in a complex environment. Methods: This paper proposes an improved artificial potential field method. First of all, in order to solve the problem of not being able to achieve the goal, the method of modifying the repulsion field function, as proposed by Wang Huili and others, has been cited. In the traditional repulsion function, the relative distance between the robot and the target is introduced. Due to this, the target point is always the minimum point of the potential field. It is necessary to ensure that the robot can reach the target position smoothly. For the local minimum problem, by adding a gravitational increase factor β, the gravitational force received is greater than the repulsive force, thereby breaking the balance of the resultant force. Finally, for the traditional artificial potential field method that does not consider the path planning problem in the presence of dynamic obstacles, the velocity vector of the dynamic obstacle is brought into the potential field function to improve the traditional artificial potential field method. Results: The robot easily falls into the local optimum during path planning, and the improved artificial potential field method overcomes this shortcoming. Therefore, the manipulator can perform autonomous dynamic obstacle avoidance path planning in an environment with dynamic obstacles, finally reaching the target point safely. Conclusions: The industrial production field faces an increasing demand for intelligent industrial robots. This paper improves on the traditional artificial potential field method based on binocular vision so that the mechanical arm can avoid dynamic obstacles autonomously in the presence of dynamic obstacles, thereby improving the degree of intelligence of the robotic arm, enabling the technology to meet the needs of future industrial development and contribute to the development of future industrial robot technology.

Finite‐time disturbance observer‐based trajectory tracking control for quadrotor unmanned aerial vehicle with obstacle avoidance

This paper investigates the problem of trajectory tracking control for quadrotor unmanned aerial vehicle (UAV) in the presence of dynamic obstacles and external disturbance forces/torques. More specifically, two new sliding mode disturbance observers are firstly designed to estimate the external disturbances, in which the observation errors can converge to zero in finite time. Furthermore, utilizing the observation information, a new sliding mode surface‐like variable‐based position tracking control scheme and a novel nonsingular terminal sliding mode‐based attitude synchronization control scheme are developed to drive the UAV tracking the reference trajectory with obstacle avoiding. Moreover, the tracking errors of the close‐loop control system can converge to zero within finite time by the analyses of Lyapunov methodology. Finally, the numerical simulation results are presented to illustrate the effectiveness of the proposed control schemes.

Multi-Objective Safe-Interval Path Planning With Dynamic Obstacles

Path planning among dynamic obstacles is a fundamental problem in Robotics with numerous applications. In this work, we investigate a problem called Multi-Objective Path Planning with Dynamic Obstacles (MOPPwDO), which requires finding collision-free Pareto-optimal paths amid obstacles moving along known trajectories while simultaneously optimizing multiple conflicting objectives, such as arrival time, communication robustness and obstacle clearance. Most of the existing multi-objective A*-like planners consider no dynamic obstacles, and naively applying them to address MOPPwDO can lead to large computation times. On the other hand, efficient algorithms such as Safe-Interval Path Planing (SIPP) can handle dynamic obstacles but for a single objective. In this work, we develop an algorithm called MO-SIPP by leveraging both the notion of safe intervals from SIPP to efficiently represent the search space in the presence of dynamic obstacles, and search techniques from multi-objective A* algorithms. We show that MO-SIPP is guaranteed to find the entire Pareto-optimal front, and verify MO-SIPP with extensive numerical tests with two and three objectives. The results show that the MO-SIPP runs up to an order of magnitude faster than the conventional alternates.

Combined Estimation of the Mutual Position of the Master and Slave Vehicle by Ultrasonic Receivers and Gnss-Module Data

The problem of determining the position of master vehicle when implementing a scenario of robotic vehicle as a part of caravanning driving system is studied. The mentioned scenario is expected the slave vehicle to replicate the motion trajectory of master vehicle represented by human operated vehicle, by another robotic vehicle or by human operator. To provide reliable operation of robotic vehicle within autonomous convoy a reliable and precise assessment of master vehicle position and trajectory regardless of environment is required. Implementation of ultrasonic receivers and emitters within information and measurement complex to determine master-slave vehicle mutual position allows to function at any time of the day inside and outside the premises in the presence of dynamic obstacles, smoke, precipitation, etc. Estimation of position and trajectory of master vehicle is accomplished by means of measuring arrival time of ultrasonic wave emitted by active beacon prior to spatial array of ultrasonic receivers. Similar indirect distance measurement between bacon and each receiver allowed to receive a system of equations to be solved by implementation of Kalman filter with Raugh - Tung - Striebel smoother. Major disadvantage of the approach is 20-meter range, which is enough for complex maneuvering. To increase the range of the complex operation, a combined estimation of the master's position based on measuring data of the spatial array of ultrasonic receivers and the module of global navigation satellite system is proposed.

Map-less long-term localization in complex industrial environments

PurposeThis paper aims to study the localization problem for autonomous industrial vehicles in the complex industrial environments. Aiming for practical applications, the pursuit is to build a map-less localization system which can be used in the presence of dynamic obstacles, short-term and long-term environment changes.Design/methodology/approachThe proposed system contains four main modules, including long-term place graph updating, global localization and re-localization, location tracking and pose registration. The first two modules fully exploit the deep-learning based three-dimensional point cloud learning techniques to achieve the map-less global localization task in large-scale environment. The location tracking module implements the particle filter framework with a newly designed perception model to track the vehicle location during movements. Finally, the pose registration module uses visual information to exclude the influence of dynamic obstacles and short-term changes and further introduces point cloud registration network to estimate the accurate vehicle pose.FindingsComprehensive experiments in real industrial environments demonstrate the effectiveness, robustness and practical applicability of the map-less localization approach.Practical implicationsThis paper provides comprehensive experiments in real industrial environments.Originality/valueThe system can be used in the practical automated industrial vehicles for long-term localization tasks. The dynamic objects, short-/long-term environment changes and hardware limitations of industrial vehicles are all considered in the system design. Thus, this work moves a big step toward achieving real implementations of the autonomous localization in practical industrial scenarios.

Path Planning in Dynamic Environments with Adaptive Dimensionality

Path planning in the presence of dynamic obstacles is a challenging problem due to the added time dimension in search space. In approaches that ignore the time dimension and treat dynamic obstacles as static, frequent re-planning is unavoidable as the obstacles move, and their solutions are generally sub-optimal and can be incomplete. To achieve both optimality and completeness, it is necessary to consider the time dimension during planning. The notion of adaptive dimensionality has been successfully used in high-dimensional motion planning such as manipulation of robot arms, but has not been used in the context of path planning in dynamic environments. In this paper, we apply the idea of adaptive dimensionality to speed up path planning in dynamic environments for a robot with no assumptions on its dynamic model. Specifically, our approach considers the time dimension only in those regions of the environment where a potential collision may occur, and plans in a low-dimensional state-space elsewhere. We show that our approach is complete and is guaranteed to find a solution, if one exists, within a cost sub-optimality bound. We experimentally validate our method on the problem of 3D vehicle navigation (x, y, heading) in dynamic environments. Our results show that the presented approach achieves substantial speedups in planning time over 4D heuristic-based A*, especially when the resulting plan deviates significantly from the one suggested by the heuristic.

Towards Narrowing the Search in Bounded-Suboptimal Safe Interval Path Planning

Path planning in the presence of dynamic obstacles is challenging as the time dimension has to be considered. A prominent approach to tackle this problem known to be complete and optimal is the A*-based Safe-interval Path Planning (SIPP). Bounded-suboptimal variants of SIPP employing the ideas of Weighted A* (WSIPP) and Focal Search (FocalSIPP) have been introduced recently, trading-off optimality for decreased planning time. In this paper, we revisit FocalSIPP and design several secondary heuristics for Focal Search with the intention to narrow the search in the direction of a preplanned optimal single-agent path not considering dynamic obstacles. The experimental results on various maps show that the designed heuristics generally outperform the hops-to-the-goal heuristic used in the original FocalSIPP and successfully compete with WSIPP as well.

Mobile Robot Path Planning in Dynamic Environments Through Globally Guided Reinforcement Learning

Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoiding potential conflicts with other robots or dynamic objects. In the presence of dynamic obstacles, traditional solutions usually employ re-planning strategies, which re-call a planning algorithm to search for an alternative path whenever the robot encounters a conflict. However, such re-planning strategies often cause unnecessary detours. To address this issue, we propose a learning-based technique that exploits environmental spatio-temporal information. Different from existing learning-based methods, we introduce a globally guided reinforcement learning approach ( G2RL ), which incorporates a novel reward structure that generalizes to arbitrary environments. We apply G2RL to solve the multi-robot path planning problem in a fully distributed reactive manner. We evaluate our method across different map types, obstacle densities, and the number of robots. Experimental results show that G2RL generalizes well, outperforming existing distributed methods, and performing very similarly to fully centralized state-of-the-art benchmarks.

Revisiting Bounded-Suboptimal Safe Interval Path Planning

Safe-interval path planning (SIPP) is a powerful algorithm for finding a path in the presence of dynamic obstacles. SIPP returns provably optimal solutions. However, in many practical applications of SIPP such as path planning for robots, one would like to trade-off optimality for shorter planning time. In this paper we explore different ways to build a bounded-suboptimal SIPP and discuss their pros and cons. We compare the different bounded-suboptimal versions of SIPP experimentally. While there is no universal winner, the results provide insights into when each method should be used.

High-speed autonomous quadrotor navigation through visual and inertial paths

This paper addresses the problem of autonomous quadrotor navigation within indoor spaces. In particular, we focus on the case where a visual map of the area, represented as a graph of linked images, is constructed offline (from visual and potentially inertial data collected beforehand) and used to determine visual paths for the quadrotor to follow. In addition, during the actual navigation, the quadrotor employs both wide- and short-baseline random sample consensuses (RANSACs) to efficiently determine its desired motion toward the next reference image and handle special motions, such as rotations in place. In particular, when the quadrotor relies only on visual observations, it uses the 5pt and 2pt algorithms in the wide- and short-baseline RANSACs, respectively. On the other hand, when information about the gravity direction is available, significant gains in speed are realized by using the 3pt+1 and 1pt+1 algorithms instead. Lastly, we introduce an adaptive optical-flow algorithm that can accurately estimate the quadrotor’s horizontal velocity under adverse conditions (e.g., when flying over dark, textureless floors) by progressively using information from more parts of the images. The speed and robustness of our algorithms are evaluated experimentally using a commercial-off-the-shelf quadrotor navigating in the presence of dynamic obstacles (i.e., people walking) along lengthy corridors and through tight corners, as well as across building floors via poorly lit staircases.

Modeling spatial navigation in the presence of dynamic obstacles: a differential games approach.

Obstacle circumvention strategies can be shaped by the dynamic interaction of an individual (evader) and an obstacle (pursuer). We have developed a mathematical model with predictive and emergent components, using experimental data from seven healthy young adults walking toward a target while avoiding collision with a stationary or moving obstacle (approaching head-on, or diagonally 30° left or right) in a virtual environment. Two linear properties from the predictive component enable the evader to predict the minimum distance between itself and the obstacle at all times, including the future intersection of trajectories. The emergent component uses the classical differential games model to solve for an optimal circumvention while reaching the target, wherein the locomotor strategy is influenced by the obstacle, target, and the evader velocity. Both model components were fitted to a different set of experimental data obtained from five poststroke and healthy participants to derive the minimum predicted distance (predictive component) and obstacle influence dimensions (emergent component) during circumvention. Minimum predicted distance between evader and pursuer was kept constant when the evader was closest to the obstacle in all participants. Obstacle influence dimensions varied depending on obstacle approach condition and preferred side of circumvention, reflecting differences in locomotor strategies between poststroke and healthy individuals. Additionally, important associations between model outputs and observed experimental outcomes were found. The model, supported by experimental data, suggests that both predictive and emergent processes can shape obstacle circumvention strategies in healthy and poststroke individuals. NEW & NOTEWORTHY Obstacle circumvention during goal-directed locomotion is modeled with a new mathematical approach comprising both predictive and emergent elements. The major novelty is using differential games solutions to illustrate the dynamic interactions between the individual as an evader and the approaching obstacle as a pursuer. The model is supported by experimental evidence that explains the behavior along the continuum of locomotor adaptation displayed by healthy subjects and individuals with stroke.

CFTLB: a novel cross-layer fault tolerant and load balancing protocol for WMN

ABSTRACTWireless mesh network (WMN) forms a wireless backbone framework for multi-hop transmission among the routers and clients in the extensible coverage area. To improve the throughput of WMNs with multiple gateways (GWs), several issues related to GW selection, load balancing and frequent link failures due to the presence of dynamic obstacles and channel interference should be addressed. This paper presents a novel cross-layer fault tolerant and load balancing (CFTLB) protocol to overcome the issues in WMN. Initially, the neighbour GW is searched and channel load is calculated. The GW having least channel load is selected which is estimated during the arrival of the new node. The proposed algorithm finds the alternate GWs and calculates the channel availability under high loading scenarios. If the current load in the GW is high, another GW is found and channel availability is calculated. Besides, it initiates the channel switching and establishes the communication with the mesh client effectively. The utilisation of hashing technique in proposed CFTLB verifies the status of the packets and achieves better performance in terms of router average throughput, throughput, average channel access time and lower end-to-end delay, communication overhead and average data loss in the channel compared to the existing protocols.

Probabilistic Approach for Visual Homing of a Mobile Robot in the Presence of Dynamic Obstacles

An autonomous mobile robot returns back to a reference (home) position using just the visual information in the visual homing task. Challenges in this autonomous task involve efficient information extraction from images and executing motion based on this information. This paper aims to develop visual homing technique by extracting coarse location information with respect to the reference position using a novel bit encoding approach. Coarse information is used in probabilistic framework to build stronger beliefs on the robot's location with successive movement of the robot. The changes in visual information due to the presence of dynamic obstacles are accounted in likelihood information using bit encoding. Novel information encoding and proposed probability distribution develop sensor model. The action model aims for building stronger beliefs with successive movement, when limited number of commands are used. Experiments are performed using Fire Bird XI robot equipped with a multiple camera system for capturing panoramic view at the robot's location. Multiple experiments show that the robot reaches the reference position without the knowledge of initial location and changing environment conditions. This paper also reports experimental results in the presence of dynamic obstacles.

Feature Detection for Vehicle Localization in Urban Environments Using a Multilayer LIDAR

Localization is an important component of autonomous vehicles, as it enables the accomplishment of tasks, such as path planning and navigation. Although vehicle position can be obtained by GNSS devices, they are susceptible to errors and satellite signal unavailability in urban scenarios. Several map-aided localization solution methods have been proposed in the literature, but mostly for indoor environments. Maps used for localization store relevant environmental features that are extracted by a detection method. However, many feature detection methods do not consider the presence of dynamic obstacles or occlusions in the environment, which can impair the localization performance. In order to detect curbs even in occluding scenes, we developed a method based on ring compression analysis and least trimmed squares. For road marking detection, we developed a modified version of the Otsu thresholding method to segment road painting from road surfaces. Finally, the feature detection methods were integrated with a Monte Carlo localization method to estimate the vehicle position. Experimental tests in urban streets have been used to validate the proposed approach with favorable results.

Efficient ABC Algorithm for Dynamic Path Planning

path planning of mobile robot is an important issue in the field of robotics. Many algorithms have been designed to solve the path planning problem, including classical as well as intelligent approaches. The main aim of path planning is to construct collision free path from a specified start position to the target position. Moreover, the path should be optimal in some context such as distance, time or processing. This paper presents an efficient algorithm which is a variation of artificial bee colony algorithm. The environment modelled is in the form of grid consisting of obstacles. The algorithm works on two problems- the first problem is to find collision free path in the presence of static obstacles and the second problem is to determine shortest collision free path in the presence of dynamic obstacles. Keywordsath planning, efficient, dynamic, optimal