Obstacle Scenarios Research Articles

Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots

Coal Mine Mobile Robots (CMRRs) are generally large in size and inertia, while narrow laneway space and bumpy terrain pose great challenges to CMRR’s planning and control. Aiming at the trajectory planning and tracking problems of CMRR, a new trajectory class MINCO is derived in detail based on the properties of differential flat systems. A trajectory planning method based on MINCO trajectory and safety corridor constraints constructed with underground environmental constraints is further proposed. A trajectory tracking method based on model predictive control (MPC) is further proposed. The prediction model of MPC is constructed by a kinematics model and transformed into a standard quadratic programming problem according to the cost function of a trajectory tracking target. Finally, large quantities of field tests were carried out for the proposed approaches. The results show that the proposed planning algorithm based on MINCO trajectory can achieve good avoidance effects within 10 planning attempts in different obstacle scenarios, and the trajectory is smoother compared to the Fast-Planner algorithm, with shorter trajectory length and less planning time. The tracking error of MPC is always less than 0.05 m in different underground scenarios, having a more adaptable trajectory tracking effect than PID.

Hybrid Model Predictive Control with Penalty Factor Based on Image-Based Visual Servoing for Constrained Mobile Robots

For the constrained mobile robot automatic parking system, the hybrid model predictive control with a penalty factor based on image-based visual servoing (IBVS) is proposed to address the problem of feature point loss and emergency braking in dynamic obstacle scenarios caused by excessive target bias gain when using traditional IBVS control methods. The traditional IBVS control is transformed into an optimization problem with constraints in the finite time domain, by defining the optimization function based on the mobile robot’s positional deviation and image feature point deviation, while using actuator saturation and speed limit as constraints. Based on this, a convex optimization function with penalty factors is defined and combined with incremental model predictive control. This control strategy could ensure the emergency braking performance of the mobile robot when the image feature points are massively obscured by obstacles in dynamic scenes, while improving the accuracy and real-time of its trajectory tracking control. Finally, simulation comparisons are conducted to verify the effectiveness of the proposed control method.

A numerical investigation of pedestrian dynamics based on rational behaviour in different density scenarios

In this work, we extend a microscopic rational behaviour pedestrian model and investigate it and the related macroscopic model numerically. A coupling with an eikonal equation is performed to include more complex geometries and pedestrian behaviour. We compare the microscopic and macroscopic models to classical social force type models. We observe that the more complex rational behaviour approach gives physically more reasonable results in low-density cases. However, in higher density scenarios, an additional social force type term is required to avoid nonphysical overcrowding. We present numerical experiments and a numerical comparison with social force type models for a variety of different physical scenarios, including low and high density situations and flow problems with and without obstacles. The numerical simulations are realised by solving the model equations in a Lagrangian form using a mesh-free particle method. In this numerical context, the model is easily extended, for example, to moving obstacle scenarios, as presented in a final example.

A Double-Layer Model Predictive Control Approach for Collision-Free Lane Tracking of On-Road Autonomous Vehicles

This paper proposes a double-layer model predictive control (MPC) algorithm for the integrated path planning and trajectory tracking of autonomous vehicles on roads. The upper module is responsible for generating collision-free lane trajectories, while the lower module is responsible for tracking this trajectory. A simplified vehicle model based on the friction cone is proposed to reduce the computation time for trajectory planning in the upper layer module. To achieve dynamic and accurate collision avoidance, a polygonal distance-based dynamic obstacle avoidance method is proposed. A vertical load calculation method for the tires is introduced to design the anti-rollover constraint in the lower layer module. Numerical simulations, with static and dynamic obstacle scenarios, are conducted on the MATLAB platform and compared with two state-of-the-art MPC algorithms. The results demonstrate that the proposed algorithm outperforms the other two algorithms regarding computation time and collision avoidance efficiency.

SaCHBA_PDN: Modified honey badger algorithm with multi-strategy for UAV path planning

The honey badger algorithm (HBA) is a meta-heuristic optimization algorithm that simulates the foraging behavior of honey badgers. Since the algorithm is prone to premature convergence when solving complex optimization problems. To improve the overall optimization performance of the basic HBA, this paper develops a modified HBA named SaCHBA_PDN based on the Bernoulli shift map, piecewise optimal decreasing neighborhood, and horizontal crossing with strategy adaptation and applies it to solve the unmanned aerial vehicle (UAV) path planning problem. Firstly, the Bernoulli shift map is invoked to the HBA algorithm to change its initialization process, thus increasing the diversity of the population and speeding up the convergence speed. Secondly, a new piecewise optimal decreasing neighborhood strategy (PODNS) is proposed to address the shortcomings of unbalanced convergence of the traditional optimal neighborhood strategy. The proposed PODNS increases the optimization efficiency of HBA and enhances the local search ability to avoid falling into the local optimum. Finally, a novel horizontal crossing with strategy adaptation is introduced to balance exploration and exploitation and enhance the global optimization ability. These strategies collaborate to enhance HBA in accelerating overall performance. The superiority of SaCHBA_PDN is comprehensively verified by comparing it with the original HBA and numerous celebrated and newly developed algorithms on the well-known 23 classical benchmark functions and IEEE CEC2017 test suite, respectively. Experimental results show that SaCHBA_PDN has a better performance than other optimization algorithms. Furthermore, SaCHBA_PDN is used to solve a UAV path planning problem based on the threat source model and applied to circular and irregular obstacle scenarios as well as two-dimensional grid maps. Simulation results show that SaCHBA_PDN can obtain more feasible and efficient paths in different obstacle environments.

An Improved Bald Eagle Search Algorithm for Global Path Planning of Unmanned Vessel in Complicated Waterways

The path planning of unmanned ships in complex waters using heuristics usually suffers from problems such as being prone to fall into the local optimum, slow convergence, and instability in global path planning. Given this, this paper proposes a Self-Adaptive Hybrid Bald Eagle Search (SAHBES) Algorithm by incorporating adaptive factors into the traditional BES in order to enhance the early global searching ability of the BES algorithm. Moreover, Pigeon-Inspired Optimization (PIO) is introduced to overcome the disadvantage of traditional BES algorithms: that it is easy for them to fall into local optimization. This study improves the fitness function by adding a distance between the ships’ path corners. The obstacle is based on the calculation of the path length. The curve optimization module is applied to smooth the obtained path to generate more rational path planning results, which means the path is the shortest and avoids collision successfully. A simulation test of the SAHBES algorithm on the path planning under different obstacle scenarios is conducted by using the MATLAB platform. The results show that SAHBES can generate the shortest safe, smooth path in different complex water environments, considering the limitations of fundamental ship maneuvering operations compared to other algorithms, thus verifying the feasibility and efficiency of the proposed SAHBES algorithm.

Intelligent Motion Planning for Collision Free Autonomous Docking of Satellite Emulation Platform Using Reinforcement Learning

A reinforcement learning (RL) enabled intelligent motion planning for collision-free autonomous docking manoeuvre explicitly designed for a robotic floating satellite emulation platform is presented in this article. The Twin Delayed Deep Deterministic Policy Gradient-based RL algorithm involving deep neural network architecture in the actor-critic framework is considered to obtain the collision-free safe docking policy. The RL agents have been trained to perform a resilient target acquisition, ensuring its terminal position and velocity requirements while enabling the capability to avoid both static and dynamic obstacles. The resulting optimal policy is implemented as a feedback control law to enable computationally efficient onboard reactive motion planning for autonomous safe docking of the robotic floating satellite platform in a complex dense debris environment. The efficacy of the proposed motion planning scheme is validated with numerous simulation studies, where it is depicted that the trained RL-based planner has the potential to address the target acquisition with a sufficient degree of accuracy in the presence of both static and dynamic obstacle scenarios.

Real-time multi-quadrotor trajectory generation via distributed receding architecture and hierarchical planning in complex environments

Generation of multi-quadrotor trajectories in real-time in complex three-dimensional environments remains a grand challenge. Trajectory planning becomes computationally prohibitive as the number of quadrotors and obstacles increases. This paper proposes the distributed receding architecture-based hierarchical trajectory planning method (drHTP) to tackle this issue. The distributed receding architecture is established to formulate and solve a series of single-quadrotor short-horizon planning problems for reducing the computation complexity. In distributed planning, the time-heuristic priority mechanism is devised to assign a reasonable planning sequence to enhance the convergence performance. The hierarchical planning, including front-end initial trajectory generation and back-end trajectory optimization, is introduced for the single-quadrotor in each short horizon to further reduce the computation time. The sparse A* search algorithm is modified to only consider adjacent obstacles for obtaining the initial trajectory rapidly. The convergence of drHTP is analyzed theoretically. Numerical simulations with moving and dense obstacle scenarios are carried out to verify the effectiveness of drHTP. The comparative simulation results demonstrate that drHTP outperforms the state-of-the-art distributed sequential convex programming and distributed model predictive control methods in terms of computational efficiency. drHTP is also validated by the physical experiment in an indoor testbed.

A Multi-Population Mean-Field Game Approach for Large-Scale Agents Cooperative Attack-Defense Evolution in High-Dimensional Environments

As one of the important issues of multi-agent collaboration, the large-scale agents’ cooperative attack–defense evolution requires a large number of agents to make stress-effective strategies to achieve their goals in complex environments. Multi-agent attack and defense in high-dimensional environments (3D obstacle scenarios) present the challenge of being able to accurately control high-dimensional state quantities. Moreover, the large scale makes the dynamic interactions in the attack and defense problems increase dramatically, which, using traditional optimal control techniques, can cause a dimensional explosion. How to model and solve the cooperative attack–defense evolution problem of large-scale agents in high-dimensional environments have become a challenge. We jointly considered energy consumption, inter-group attack and defense, intra-group collision avoidance, and obstacle avoidance in their cost functions. Meanwhile, the high-dimensional state dynamics were used to describe the motion of agents under environmental interference. Then, we formulated the cooperative attack–defense evolution of large-scale agents in high-dimensional environments as a multi-population high-dimensional stochastic mean-field game (MPHD-MFG), which significantly reduced the communication frequency and computational complexity. We tractably solved the MPHD-MFG with a generative-adversarial-network (GAN)-based method using the MFGs’ underlying variational primal–dual structure. Based on our approach, we carried out an integrative experiment in which we analytically showed the fast convergence of our cooperative attack–defense evolution algorithm by the convergence of the Hamilton–Jacobi–Bellman equation’s residual errors. The experiment also showed that a large number of drones can avoid obstacles and smoothly evolve their attack and defense behaviors while minimizing their energy consumption. In addition, the comparison with the baseline methods showed that our approach is advanced.

Path planning in unmanned aerial vehicles: An optimistic overview

SummaryWith the development in the technology, a rapid increase in the use of unmanned aerial vehicle (UAV) is observed. UAVs are executing tasks that were previously performed by humans or manned aircraft, thus reducing the man workload and saving time. Path planning of UAVs is one of the most researched topics these days. Researchers are investigating more about UAVs and path planning to make it more feasible and economical. The major issue faced while planning a feasible path is to detect and avoid the obstacles encountered during a mission. This paper presents a detailed analysis of path planning in UAVs. Important aspects of path planning, that is, environment, dimensions, obstacles, and type and number of UAVs used in path planning, are also being discussed. Obstacle scenarios which UAV may come across during a mission and constraints which UAV has to follow for successful mission are briefly mentioned. Optimization techniques used to optimize the UAV path are also presented. In addition, future challenges and issues are also discussed.

Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management

In view of the low formation redundancy in the traditional rigid formation algorithm and its difficulty in dynamically adapting to the external environment, this study considers the use of the CVT (centroidal Voronoi tessellation) algorithm to control multiple robots to form the desired formation. This method significantly increases the complexity of the multi-robot system, its structural redundancy, and its internal carrying capacity. First, we used the CVT algorithm to complete the Voronoi division of the global map, and then changed the centroid position of the Voronoi cell by adjusting the density function. When the algorithm converged, it could ensure that the position of the generated point was the centroid of each Voronoi cell and control the robot to track the position of the generated point to form the desired formation. The use of traditional formations requires less consideration of the impact of the actual environment on the health of robots, the overall mission performance of the formation, and the future reliability. We propose a health optimization management algorithm based on minor changes to the original framework to minimize the health loss of robots and reduce the impact of environmental restrictions on formation sites, thereby improving the robustness of the formation system. Simulation and robot formation experiments proved that the CVT algorithm could control the robots to quickly generate formations, easily switch formations dynamically, and solve the formation maintenance problem in obstacle scenarios. Furthermore, the health optimization management algorithm could maximize the life of unhealthy robots, making the formation more robust when performing tasks in different scenarios.

Three-Dimensional Autonomous Obstacle Avoidance Algorithm for UAV Based on Circular Arc Trajectory

This paper proposes an innovative and efficient three-dimensional (3D) autonomous obstacle algorithm for unmanned aerial vehicles (UAVs) which works by generating circular arc trajectories to avoid obstacles. Firstly, information on irregular obstacles is obtained by an onboard detection system; this information is then transformed into standard convex bodies, which are used to generate circular arc avoidance trajectories, and the obstacle avoidance problem is turned into a trajectory tracking strategy. Then, on the basis of the geometric relationship between a UAV and obstacle modeling, the working mechanism of the avoidance algorithm is developed. The rules of obstacle detection, avoidance direction, and the criterion of avoidance success are defined for different obstacle types. Finally, numerical simulations of different obstacle scenarios show that the proposed algorithm can avoid static and dynamic obstacles effectively and can implement obstacle avoidance missions for UAVs well.

Neural network-based source tracking of chemical leaks with obstacles

The leakage of hazardous gases poses a significant threat to public security and causes environmental damage. The effective and accurate source term estimation (STE) is necessary when a leakage accident occurs. However, most research generally assumes that no obstacles exist near the leak source, which is inappropriate in practical applications. To solve this problem, we propose two different frameworks to emphasize STE with obstacles based on artificial neural network (ANN) and convolutional neural network (CNN). Firstly, we build a CFD model to simulate the gas diffusion in obstacle scenarios and construct a benchmark dataset. Secondly, we define the structure of ANN by searching, then predict the concentration distribution of gas using the searched model, and optimize source term parameters by particle swarm optimization (PSO) with well-performed cost functions. Thirdly, we propose a one-step STE method based on CNN, which establishes a link between the concentration distribution and the location of obstacles. Finally, we propose a novel data processing method to process sensor data, which maps the concentration information into feature channels. The comprehensive experiments illustrate the performance and efficiency of the proposed methods.

A Distributed Model Predictive Control Strategy for Constrained Multi-Vehicle Systems Moving in Unknown Environments

In this paper, we propose a distributed model predictive control strategy for a team of vehicles moving in an unknown obstacle scenario. In particular, we combine receding horizon control arguments with leader-follower formations in order to design a flexible architecture where the vehicle topology, if needed, can be rearranged to better cope with the presence of obstacles. Each vehicle is equipped with an ad-hoc dual-mode controller in charge of tracking along the leader-follower chain the predecessor vehicle (namely the target for the leader) and ensuring absence of collisions. Moreover, whenever the leader recognizes that the obstacle scenario obstructs its navigation, the current formation is properly reconfigured by preserving recursive feasibility and obstacle avoidance properties. Final numerical results on a team of seven point mobile robots are instrumental to show the main features of the proposed distributed model predictive control algorithm.

Improved Obstacle Mitigation and Localization Accuracy in Narrowband Ultrasonic Localization Systems Using RoBCUL Algorithm

This article develops a method for mitigating the negative effects of obstacles in narrowband, time-division multiple access (TDMA), ultrasonic localization systems. The method builds upon the robust Bayesian classifier for ultrasonic localization (RoBCUL) algorithm that utilizes an iteratively reweighted least-squares (IRLS) scheme. This algorithm has the advantage of low computational cost but loses performance in the presence of obstacles. The improved version of the RoBCUL algorithm presented in this article uses a statistical test applied after each iteration of the regression, using a weighted-residual vector calculated from the weight matrix and residual vector. The technique was tested using experimental data with its performance being quantified by its ability to correctly classify all the signals received during a single TDMA cycle. The extended version performed significantly better in all obstacle scenarios than the original, correctly classifying 100% of TDMA cycles in the scenarios with no obstacles, 99.9% with one obstacle, and 98.7% with two obstacles.

UAV Group Formation Collision Avoidance Method Based on Second-Order Consensus Algorithm and Improved Artificial Potential Field

The problem of collision avoidance of an unmanned aerial vehicle (UAV) group is studied in this paper. A collision avoidance method of UAV group formation based on second-order consensus algorithm and improved artificial potential field is proposed. Based on the method, the UAV group can form a predetermined formation from any initial state and fly to the target position in normal flight, and can avoid collision according to the improved smooth artificial potential field method when encountering an obstacle. The UAV group adopts the “leader–follower” strategy, that is, the leader UAV is the controller and flies independently according to the mission requirements, while the follower UAV follows the leader UAV based on the second-order consensus algorithm and formations gradually form during the flight. Based on the second-order consensus algorithm, the UAV group can achieve formation maintenance easily and the Laplacian matrix used in the algorithm is symmetric for an undirected graph. In the process of obstacle avoidance, the improved artificial potential field method can solve the jitter problem that the traditional artificial potential field method causes for the UAV and avoids violent jitter. Finally, simulation experiments of two scenarios were designed to verify the collision avoidance effect and formation retention effect of static obstacles and dynamic obstacles while the two UAV groups fly in opposite symmetry in the dynamic obstacle scenario. The experimental results demonstrate the effectiveness of the proposed method.

Collision Avoidance for Underactuated Marine Vehicles Using the Constant Avoidance Angle Algorithm

Avoiding collisions is a crucial ability for unmanned vehicles. In this paper, we present the constant avoidance angle algorithm, a reactive method for collision avoidance. It can be used to avoid both static and moving obstacles by making the vehicle keep an avoidance angle between itself and the obstacle edge. Unlike many other algorithms, it requires neither knowledge of the complete obstacle shape nor that the vehicle follows a desired speed trajectory. Instead, safe vehicle headings are provided at the current vehicle speed. Thus, the speed can be used as an input to the algorithm, which provides flexibility and makes the approach suitable for a wide range of vehicles, including vehicles with a limited speed envelope or high acceleration cost. We demonstrate this by applying the algorithm to a marine vehicle described by a full kinematic and dynamic model in three degrees of freedom. We specifically consider vehicles with underactuated sway dynamics, where the vehicle velocity contains a component that cannot be directly controlled. Such dynamics can be highly detrimental to the performance of collision avoidance algorithms and need to be included in the design and analysis of control systems for such vehicles. In this paper, we compensate for the underactuation by including these dynamics in the underlying analysis and control design. We provide a mathematical analysis of sparse obstacle scenarios, where we derive conditions under which safe avoidance is guaranteed, even for underactuated vehicles. We furthermore show how the modular nature of the algorithm enables it to be combined both with a target reaching and a path following guidance law. Finally, we validate the results both through numerical simulations and through full-scale experiments aboard the R/V Gunnerus.

Vision-Based Guidance and Switching-Based Sliding Mode Controller for a Mobile Robot in the Cyber Physical Framework

This paper proposes a vision-based guidance strategy for safe navigation of a nonholonomic mobile robot in unknown indoor environments. The proposed switching-based sliding mode control (SMC) law makes the robot follow the desired trajectory as given by the guidance law. The guidance strategy uses the centroid of the depth map of an obstacle as obtained from the Red Green Blue -Depth (RGB-D) sensor to generate the desired angular velocity. The fuzzy rule-based guidance is developed to generate the desired linear velocity command. The analysis of guidance strategy is done for an infinite length obstacle. The proposed SMC is shown to be asymptotically stable using Krasovskii Method. The finite time convergence of robot navigation has been shown using Poincare Map method. The stability of the proposed SMC under burst losses has also been established. Experiments on the Pioneer P3-DX robot in different obstacle scenarios show that the robot safely navigates in presence of communication channel burst losses.

Towards Debris Information Analysis and Abstraction for Wi-Fi Radar Edge in Collapsed Structures

Structural collapses are widespread, owing to a surge in climatic changes, earthquakes, and terrorism. Therefore, there are some technological rescue methods in practice that involve sensors, radars, cameras, microphones, and robots. However, deployment of these techniques faces at least one issue amongst cost, availability, and technical expertise, which limits their application in developing countries. So, there is a dire need for a low-cost and easy deployable rescue method. Recently, we witnessed an increasing trend of using Wi-Fi radios as sensing modality for various applications, including breathing detection and localization, thus leading to device-free communication. Based on this, we may envisage having a Wi-Fi rescue solution. However, Wi-Fi signals cannot easily penetrate through collapsed structures due to the multilayered obstacle scenario. So, in this study, we focus our research on the proper information analysis and abstraction of debris and also present the possible methodology to have better coverage for Wi-Fi signals using Wi-Fi radar edge. We define two objectives of this work; 1) debris information analysis and 2) the Wi-Fi signal propagation mechanism, respectively. We achieve our first goal by conducting site surveys of earthquake-hit areas that enable us to analyze the causes and types of structural collapses followed by debris concept selection model. We employ a bijective soft set approach to accurately select the debris based on the complexity and nature of structural engineering followed. Moreover, we use the Wi-Fi Halow radar edge for wireless signal propagation and perform extensive simulations at low power. Finally, we compare both methods and discuss prospects.

Overview of Path-Planning and Obstacle Avoidance Algorithms for UAVs: A Comparative Study

Unmanned aerial vehicles (UAVs) have recently attracted the attention of researchers due to their numerous potential civilian applications. However, current robot navigation technologies need further development for efficient application to various scenarios. One key issue is the “Sense and Avoid” capability, currently of immense interest to researchers. Such a capability is required for safe operation of UAVs in civilian domain. For autonomous decision making and control of UAVs, several path-planning and navigation algorithms have been proposed. This is a challenging task to be carried out in a 3D environment, especially while accounting for sensor noise, uncertainties in operating conditions, and real-time applicability. Heuristic and non-heuristic or exact techniques are the two solution methodologies that categorize path-planning algorithms. The aim of this paper is to carry out a comprehensive and comparative study of existing UAV path-planning algorithms for both methods. Three different obstacle scenarios test the performance of each algorithm. We have compared the computational time and solution optimality, and tested each algorithm with variations in the availability of global and local obstacle information.