Dynamic Obstacle Detection Based on Background Compensation in Robot’s Movement Space

Indoor localization is a research field that has drawn the attention of many researchers over the last years. In some application scenarios, for example when assisting visually impaired persons with finding their way around within indoor environments, the aspect of dynamic obstacle detection plays an important role. In this work, an optimized localization algorithm based on field strength measurements is presented. Furthermore, methods for dynamic obstacle detection are developed and evaluated. For this purpose, a low-cost infrared array sensor is used. Since most of the encountered dynamic obstacles in indoor environments are generally persons, the major advantage of the proposed concept is that the anonymity of the detected persons is preserved and there are no conflicts with data protection regulations. It will be shown that reliable and accurate indoor localization based on field strength measurements within a wireless mesh network can be carried out. Also the detection of moving persons is possible and even the movement patterns of two persons simultaneously crossing the field of view of the sensor can be displayed. The achieved results are verified by extensive measurements and descriptive statistics.

Aimed at the detection of static and dynamic obstacles in environmental mapping of mobile robot, an unsupervised clustering algorithm is presented to realize feature extraction of obstacles based on the analysis of ranging data obtained from 2D laser scanner. Considering the unknown clustering number in advance, the validation index function is introduced into the self-learning mechanism to determine the accurate clustering number automatically. At the same time, fuzzy logic is integrated into incremental data association of obstacle features to make the static or dynamic obstacles classification decision to reduce the uncertain influence. Using our office as the operating environment to implement the experiment of feature extraction and obstacles classification, the results verify the effectiveness of this approach.

Collision Avoidance Systems (CASs) are attracting a lot of attention as one of the most preferred solutions for advanced driver assistance and autonomous driving. However, scene understanding, which is an essential functionality in CASs, remains a major challenge mainly due to the need for real-time understanding of highly dynamic and complex environment. In this research, a number of robust and low complexity vision based scene understanding techniques for collision avoidance on roadway have been proposed. It has been well recognized in the literature that road surface detection in a dynamic environment is both challenging and computationally intensive. An efficient non-parametric road surface detection algorithm that exploits the depth cue is proposed to overcome the limitations of existing road surface detection methods. Unlike existing methods that attempt to fit the road surface into rigid models, the proposed method results in low computational complexity, mainly due to the reliance on four intrinsic road scene attributes observed under stereo geometry. It has been demonstrated that the proposed method is capable of detecting both planar and non-planar road surfaces. Extensive experimental results using three challenging benchmarks (i.e. enpeda, KITTI stereo/flow, and Daimler) show that the proposed road surface detection algorithm outperforms the baseline algorithms both in terms of detection accuracy (up to 23.12%) and runtime performance (up to 95.00%). Next, robust and low complexity algorithm for computing the ego-vehicle’s motion state is proposed. The proposed method estimates the ego-motion of the vehicle by first employing a novel pruning technique to reduce the computational complexity of the corner feature detection process without compromising on the quality of the extracted corner features. A robust and compute-efficient KLT tracker is proposed to facilitate the generation of the feature correspondences. Finally, an early RANSAC termination condition is introduced to the Gaussian-Newton optimization scheme to achieve rapid convergence of the motion estimation process. Evaluations based on the KITTI odometry benchmark show that the proposed visual odometry method outperforms the baseline algorithms both in terms of accuracy (up to 48.36%) and runtime performance. In addition, the proposed algorithm is placed among the top 15% when evaluated using the well-known KITTI odometry platform. Methods for robust and low complexity stereo-vision based obstacle detection and tracking are proposed. Unlike the works that focus only on the detection of vehicles or pedestrians, the proposed obstacle detection method relies on u-v disparity space to detect all obstacles in the scene. A Space of Interest (SOI) is defined to greatly reduce the search space of obstacles prior to employing adaptive hysteresis thresholding and connected component labeling techniques to segment SOI into sets of obstacles. Method for tracking obstacles across frames is also proposed by constructing a distinctive object appearance model. A number of strategies to further increase the distinctiveness and reduce the computational complexity for constructing the object model are also adopted. Finally, an online multi-object tracking framework is proposed by integrating the obstacle detection and data association modules in a robust way. Evaluations using the KITTI tracking benchmark confirm that the proposed obstacle detection and tracking method outperforms the baseline algorithm in terms of tracking accuracy by up to 51.78%. In addition, compared to the baseline algorithm that achieves about 0.23 frame per second (fps), the proposed method lends well for real-time performance with 20 fps. Finally, an efficient and robust risk assessment framework is proposed by integrating the obstacle detection and tracking, and visual odometry methods proposed in this thesis. The Extended Kalman Filter is customized to enhance the robustness of the predicted trajectory of the obstacles for assessing the collision risk. The robustness of collision prediction has been enhanced by accommodating positioning uncertainty. Evaluations based on the KITTI tracking dataset demonstrate that the proposed method are capable of robust and efficient assessment of the collision risk in diverse traffic scenarios. The proposed vision based scene understanding techniques in this research have paved the way towards realizing a real-time capable collision avoidance system that is both affordable and dependable.

Presents a new framework for detection of dynamic obstacles in the unstructured outdoor road environment by integrating binocular color image sequences. In the authors' system, color image segmentation, stereo obstacle detection, visual egomotion estimation, and moving object analysis, are all built-in task-oriented modules and hence are efficient and robust. Most of these functions can be performed in real-time. They are activated and integrated adaptively in the manner of neural parallel distributed processing (PDP). Experimental results are given to validate the authors' philosophy of so-called purposive vision, retinal mapping, adaptive integration and parallel distributed processing. >

This article presents an approach for collision-free kinematics of multiple redundant manipulators in complex environments. The approach describes a representation of task space and joint limit constraints for redundant manipulators and handles collision-free constraints by micromanipulator dynamic model and velocity obstacles. A new algorithm based on Newton-based and first-order techniques is proposed to generate collision-free inverse kinematics solutions. The present approach is applied in simulation for the redundant manipulators in a various working environments with dynamic obstacles. The physical experiments using a Baxter robot in a various working environments with dynamic obstacles are also performed. The results demonstrate the effectiveness of the proposed approach compared with existing methods regarding working environment and computational cost.

The purpose of this study is to suggest a new method that can easily detect obstacles surrounding by use of a circle line laser and fisheye camera. This system can be attached to a mobile robot, and obstacle detection can be taken in real time while moving. Conventional obstacle detection methods are widely used such as using ultrasonic sensors to detect the presence or absence of obstacles by transmitting ultrasonic waves from a transmitter and receiving the ultrasonic waves reflected by an object at the receiver. Also obstacle detection method using LiDAR uses remote sensing technology to detect obstacles and measure distances. Our proposed method is a different obstacle detection method that can detect obstacles surrounding with large range easily. We show the system construction by use of a circle line laser and fisheye camera and obstacle detection experimental results.

We present a reactive base control method that enables high performance mobile manipulation on-the-move in real-world environments with static and dynamic obstacles. Performing manipulation tasks while the mobile base remains in motion can significantly decrease the time required to perform multi-step tasks, as well as improve the gracefulness of the robot's motion. Existing approaches to manipulation on-the-move either ignore the obstacle avoidance problem or rely on the execution of planned trajectories, which is not suitable in environments with dynamic objects and obstacles. The presented controller addresses both of these deficiencies and demonstrates robust performance of pick-and-place tasks in dynamic environments. The performance is evaluated on several simulated and real-world tasks. On a mobile manipulation task with static obstacles, we outperform an existing method by 41% in terms of total task time. Further, we present real-world examples of our robot performing manipulation tasks on-the-move while avoiding a second autonomous robot in the workspace.

Monocular obstacle detection using reciprocal-polar rectification

Obstacle detection is the basis for the Advanced Driving Assistance System (ADAS) to take obstacle avoidance measures. However, it is a very essential and challenging task to detect unexpected obstacles on the road. To this end, an unexpected obstacle detection method based on computer vision is proposed. We first present two independent methods for the detection of unexpected obstacles: a semantic segmentation method that can highlight the contextual information of unexpected obstacles on the road and an open-set recognition algorithm that can distinguish known and unknown classes according to the uncertainty degree. Then, the detection results of the two methods are input into the Bayesian framework in the form of probabilities for the final decision. Since there is a big difference between semantic and uncertainty information, the fusion results reflect the respective advantages of the two methods. The proposed method is tested on the Lost and Found dataset and evaluated by comparing it with the various obstacle detection methods and fusion strategies. The results show that our method improves the detection rate while maintaining a relatively low false-positive rate. Especially when detecting unexpected long-distance obstacles, the fusion method outperforms the independent methods and keeps a high detection rate.

The increasing integration of mobile robots in various industries necessitates efficient navigation strategies amidst dynamic environments. Path planning plays a crucial role in guiding mobile robots from their starting points to target destinations, contributing to automation and enhancing human-robot collaboration. This study focuses on devising a tailored path-planning approach for a fleet of mobile robots to navigate through dynamic obstacles and reach designated trajectories efficiently. Leveraging particle swarm optimization (PSO), our methodology optimizes the path while considering real-time environmental changes. We present a simulation-based implementation of the algorithm, where each robot maintains position, velocity, cost, and personal best information to converge towards the global optimal solution. Different obstacles consist of circles, squares, rectangles, and triangles with various colors and five handle-points used. Our findings demonstrate that PSO achieves a global best cost of 5.1017, indicative of the most efficient path, minimizing overall distance traveled.

With the popularity of service robots in dynamic environments such as hospitals and shopping malls, traditional path planning algorithms have problems such as inflection points of planning paths and difficulty in effectively avoiding dynamic obstacles. In response to these problems, this paper proposes an optimization algorithm based on A* and dynamic window method (DWA). First, in the global path planning, the number of turning points is reduced by deleting redundant turning points. Secondly, by establishing the motion model of the obstacle, the range of the dynamic obstacle is updated to the static environment, and the dynamic obstacle avoidance ability of the DWA algorithm is improved. Finally, the optimized A* algorithm is combined with the DWA algorithm to realize the path planning of the service robot in a dynamic environment. Experimental results show that the algorithm reduces the global path length without disturbing dynamic obstacles, the path is smoother, and the dynamic obstacle avoidance ability is more reliable.

Robot navigation in dynamic unknown environments is a challenging issue in the field of autonomous mobile robot control. This paper presents a hybrid robust method for navigating an industrial robot in an environment that contains dynamic obstacles. The objectives are to find the shortest path, to minimize the energy consumption of robot, to make the smoothness of the generated paths and to tackle dynamic obstacles. Robots employed in industrial environments demand considerable autonomy and require high level of accuracy and manoeuvrability at the same time. Besides, no collision is tolerable along the way. A single-objective optimization method based on path criteria fails to satisfy all of the requirements. This paper proposes a hybrid algorithm including the whale optimization algorithm (WOA) for path planning, a learnable function approximation network for making smoothness of the generated paths and a fuzzy logic controller to avoid obstacle collision. In this algorithm, WOA optimizes the best path to be taken from the start to goal position. Once a sequence of points is candidate and segments of path are merged, a radial basis function is trained to provide a smooth movement path in the dynamic environment while trying to maximize the safety margin. To further improve the safety of navigation, a fuzzy-based obstacle avoidance algorithm is executed when the robot is placed in the vicinity of an obstacle. Fuzzy decisions are made based on values of distance information. The proposed hybrid method for path planning and obstacle avoidance issues was implemented and evaluated in dynamic environments including specific shaped obstacles. A GUI-based simulation platform was designed in Matlab environment for testing the proposed algorithm. Implementation results indicate that the proposed algorithm has yielded in smooth non-marginal goal-directed navigation with acceptable performance metrics. Meanwhile, collisions to dynamic obstacles were adaptively and non-rigidly avoided. Such a model-free hybrid algorithm for path planning and obstacle avoidance can improve autonomy in industrial operation and decrease computational complexity.

The recent advances in robotics has resulted in a more convenient use of mobile robots in alications such as assisting the disabled, deliveries and domestic purposes. The main challenge faced by mobile robots is navigation in a dynamic environment, which is path planning for dynamic obstacle avoidance. This paper proposes a novelty method for solving the path planning problem for mobile robots posed by dynamic obstacles based on SLAM (Simultaneous Localisation and Maing) algorithm and Reinforcement Learning. The algorithms implemented relied on the Kinect sensor for maing and rotary encoder for localisation of the robot in the map. The SLAM algorithm implemented resulted in a mean error metric of 4.07%. The modified Q-learning algorithm implemented in this paper allowed the mobile robot to avoid dynamic obstacles by re-planning the path to find another optimal path different from the previously set global optimal path. From the investigation, it was shown that it is possible for a robot to navigate in a dynamic using the Reinforcement Learning technique.

A very important challenge in many virtual applications is to plan feasible, smooth and congestion-free paths for virtual agents in dynamic and complex environments. The agents should move towards their destinations successfully while avoiding the collisions with other agents and static and dynamic obstacles. In this paper, we propose a novel approach for realistic path planning. We first create a navigation mesh for the walkable regions in a two-dimensional environment. Then an A* search on this graph determines a series of connected meshes for agents to go through from the start position to the goal position and furthermore, the walkable corridor whose radii equal to maximum clearance to the obstacles is built based on backbone path derived from the inflection point method and Catmull-Rom spline. Finally, a local collision avoidance algorithm is integrated to guarantee that agents navigating in the corridor do not collide with other agents and dynamic obstacles. Our experiments show that we can compute feasible, smooth and realistic paths for agents situated in dynamic environments in real time.

To navigate dynamic environments safely, UAVs require accurate, real time onboard perception, which relies on ego motion compensation to separate self-induced motion from external dynamics and enable reliable obstacle detection. Traditional ego-motion compensation techniques are mainly based on optimization processes and may be computationally expensive for real-time applications or lack the precision needed to handle both rotational and translational movements, leading to issues such as misidentifying static elements as dynamic obstacles and generating false positives. In this paper, we propose a novel approach that integrates an event camera-based perception pipeline with an ego-motion compensation algorithm to accurately compensate for both rotational and translational UAV motion. An enhanced warping function, integrating IMU and depth data, is constructed to compensate camera motion based on real-time IMU data to remove ego motion from the asynchronous event stream, enhancing detection accuracy by reducing false positives and missed detections. On the compensated event stream, dynamic obstacles are detected by applying a motion aware adaptive threshold to the normalized mean timestamp image, with the threshold derived from the image’s spatial mean and standard deviation and adjusted by the UAV’s angular and linear velocities. Furthermore, in conjunction with a 3D Artificial Potential Field (APF) for obstacle avoidance, the proposed approach generates smooth, collision-free paths, addressing local minima issues through a rotational force component to ensure efficient UAV navigation in dynamic environments. The effectiveness of the proposed approach is validated through simulations, and its application for UAV navigation, safety, and efficiency in environments such as warehouses is demonstrated, where real-time response and precise obstacle avoidance are essential.