Velocity Obstacle Research Articles

Development and Optimization of Formation Flying for Unmanned Aerial Vehicles Using Particle Swarm Optimization Based on Reciprocal Velocity Obstacles

Development and Optimization of Formation Flying for Unmanned Aerial Vehicles Using Particle Swarm Optimization Based on Reciprocal Velocity Obstacles

Velocity Estimation and Cost Map Generation for Dynamic Obstacle Avoidance of ROS Based AMR

In the past few years, due to the growth of the open-source community and the popularity of perceptual computing resources, the ROS (Robotic Operating System)Ecosystem has been widely shared and used in academia, industrial applications, and service fields. With the advantages of reusability of algorithms and system modularity, service robot applications are flourishing via the released ROS navigation framework. In the ROS navigation framework, the grid cost maps are majorly designed for path planning and obstacle avoidance with range sensors. However, the robot will often collide with dynamic obstacles since the velocity information is not considered within the navigation framework in time. This study aims to improve the feasibility of high-speed dynamic obstacle avoidance for an ROS-based mobile robot. In order to enable the robot to detect and estimate dynamic obstacles from a first-person perspective, vision tracking and a laser ranger with an Extend Kalman Filter (EKF) have been applied. In addition, an innovative velocity obstacle layer with truncated distance is implemented for the path planner to analyze the performances between the simulated and actual avoidance behavior. Finally, via the velocity obstacle layer, as the robot faces the high-speed obstacle, safe navigation can be achieved.

Collision-Avoidance Decision System for Inland Ships Based on Velocity Obstacle Algorithms

Due to the complex hydrology and narrow channels of inland rivers, ship collision accidents occur frequently. The traditional collision-avoidance algorithms are often aimed at sea areas, and not often at inland rivers. To solve the problem of inland-ship collision avoidance, this paper proposes an inland-ship collision-avoidance decision system based on the velocity obstacle algorithm. The system is designed to assist ships in achieving independent collision-avoidance operations under the limitation of maneuverability while meeting inland-ship collision-avoidance regulations. First, the paper improves the Maneuvering Modeling Group (MMG) model suitable for inland rivers. Then, it improves velocity obstacle algorithms based on the dynamic ship domain, which can deal with different obstacles and three encounter situations (head-on, crossing, and overtaking situations). In addition, this paper proposes a method to deal with close-quarters situations. Finally, the simulation environment built by MATLAB software is used to simulate the collision avoidance of inland ships against different obstacles under different situations with a decision-making time of less than 0.1 s. Through the analysis of the simulation results, the effectiveness and practicability of the system are verified, which can provide reasonable collision-avoidance decisions for inland ships.

Towards real-time ship collision risk analysis: An improved R-TCR model considering target ship motion uncertainty

Collision between ships is one of the major contributors to ship accidents. To facilitate the development of the real-time collision risk analysis model for collision avoidance, in this research, an improved Rule-aware Time-varying Collision risk Model is proposed, which considers the estimation of target ship motion and the corresponding uncertainty in the risk analysis process. To make the collision risk model more in line with the actual situation, ship maneuverability, the COLREGs, and good seamanship are considered and integrated into the framework of the TCR model. Firstly, the Gaussian process is used to predict the potential trajectories of the target ship. Secondly, the Probabilistic Velocity Obstacle (PVO) model is utilized to integrate the uncertainty of the target ship's motion into the collision risk model. The collision risk is therefore formulated as a ratio of available maneuvers leading to a collision to all available maneuvers. To verify the effectiveness of collision risk, two actual target ships and six groups of collision risk detection experiments under different encounter scenarios were carried out. Compared with the traditional collision risk index model and original R-TCR model, the collision risk detected by the Improved R-TCR model is closer to the actual situation.

A Novel Decision Support Methodology for Autonomous Collision Avoidance Based on Deduction of Manoeuvring Process

In the last few years, autonomous ships have attracted increasing attention in the maritime industry. Autonomous ships with an autonomous collision avoidance capability are the development trend for future ships. In this study, a ship manoeuvring process deduction-based dynamic adaptive autonomous collision avoidance decision support method for autonomous ships is presented. Firstly, the dynamic motion parameters of the own ship relative to the target ship are calculated by using the dynamic mathematical model. Then the fuzzy set theory is adopted to construct collision risk models, which combine the spatial collision risk index (SCRI) and time collision risk index (TCRI) in different encountered situations. After that, the ship movement model and fuzzy adaptive PID method are used to derive the ships’ manoeuvre motion process. On this basis, the feasible avoidance range and the optimal steering angle for ship collision avoidance are calculated by deducting the manoeuvring process and the modified velocity obstacle (VO) method. Moreover, to address the issue of resuming sailing after the ship collision avoidance is completed, the Line of Sight (LOS) guidance system is adopted to resume normal navigation for the own ship in this study. Finally, the dynamic adaptive autonomous collision avoidance model is developed by combining the ship movement model, the fuzzy adaptive PID control model, the modified VO method and the resume-sailing model. The results of the simulation show that the proposed methodology can effectively avoid collisions between the own ship and the moving TSs for situations involving two or multiple ships, and the own ship can resume its original route after collision avoidance is completed. Additionally, it is also proved that this method can be applied to complex situations with various encountered ships, and it exhibits excellent adaptability and effectiveness when encountering multiple objects and complex situations.

Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method.

Navigating safely in complex marine environments is a challenge for submarines because proper path planning underwater is difficult. This paper decomposes the submarine path planning problem into global path planning and local dynamic obstacle avoidance. Firstly, an artificial potential field ant colony algorithm (APF-ACO) based on an improved artificial potential field algorithm and improved ant colony algorithm is proposed to solve the problem of submarine underwater global path planning. Compared with the Optimized ACO algorithm proposed based on a similar background, the APF-ACO algorithm has a faster convergence speed and better path planning results. Using an inflection point optimization algorithm greatly reduces the number and length of inflection points in the path. Using the Clothoid curve fitting algorithm to optimize the path results, a smoother and more stable path result is obtained. In addition, this paper uses a three-dimensional dynamic obstacle avoidance algorithm based on the velocity obstacle method. The experimental results show that the algorithm can help submarines to identify threatening dynamic obstacles and avoid collisions effectively. Finally, we experimented with the algorithm in the submarine underwater semi-physical simulation system, and the experimental results verified the effectiveness of the algorithm.

Decision-making method for multi-ship collision avoidance based on improved extensive game model

In the case of multi-ship encounter situations, most collision accidents are caused by not having timely and coordinated collision avoidance behaviors. Therefore, it is necessary to introduce an automatic collision avoidance method in the process of collision avoidance to quickly generate multi-ship collision avoidance decisions based on encounter situations. Multi-ship collision avoidance is a dynamic game used among officers under situations of multi-ship encounters. Considering the defects of the classic extensive game method in ship collision avoidance decision-making, the improved extensive game method is proposed, based on the velocity obstacle method. The feasible region of velocity is taken as the optional action range of the game to narrow down the action set. The simulation results show that the proposed method can effectively transform the multi-ship collision avoidance game problem into a 2-ship collision avoidance game problem, reduce the optional action range of a collision avoidance action set, and improve decision-making speed, while ensuring the safety of multi-ship navigation.

End-to-end human inspired learning based system for dynamic obstacle avoidance

As a first, the paper proposes modelling and learning of specific behaviors for dynamic obstacle avoidance in end-to-end motion planning. In the literature many end-to-end methods have been used in simulators to drive a car and to apply the learnt strategies to avoid the obstacles using the lane changing, following the vehicle as per the traffic rules, driving in-between the lane boundaries, and many more behaviors. The proposed method is designed to avoid obstacles in the scenarios where a dynamic obstacle is headed directly towards the robot from different directions. To avoid the critical encounter of the dynamic obstacles, we trained a novel deep neural network (DNN) with two specific behavioral obstacle avoidance strategies, namely “head-on collision avoidance” and “stop and move”. These two strategies of obstacle avoidance come from the human behavior of obstacle avoidance. Looking at the current frame only, for a very similar visual display of the scenario, the two strategies have contrasting outputs and overall outcomes that makes learning very difficult. A random data recording over general simulations is unlikely to record the corner cases of both behaviors that rarely occur, and a behavior-specific training used in this paper intensifies the same cases for a better learning of the robot in such corner cases. We calculate the intention of the obstacle, whether it will move or not. This proposed method is compared with three state-of-the-art methods of motion planning, namely Timed-Elastic Band, Dynamic Window Approach and Nonlinear Probabilistic Velocity Obstacle. The proposed method beats all the state-of-the-art methods used for comparisons.

Inert and mobile agents navigation interaction using reciprocal velocity obstacles for collisions avoidance

Reciprocal velocity obstacles (RVO) is a method used for multiagents navigation that enables collision and oscillation-free avoidance against other mobile agents. Despite its ability in collision avoidance between agents, RVO has a hard time dealing with static obstacle avoidance. This problem has led to a tendency to use RVO only for agents avoidance and use other methods to handle static obstacles avoidance. In this paper, we present our new approach for interaction between mobile agents against static obstacles in the RVO based collision avoidance. We propose a concept called inert agents that interact as static obstacles. This inert agent is stand firm as static obstacles should be, while the inert agent also able to satisfy reactive collision avoidance nature of RVO to produce better avoidance result. We conduct an experiment to compare the performance of avoidance in a certain scenario. Our method shows better results when compared with generic static obstacles.

Socially-inspired fully decentralized robot coordination

The necessity of the collision-free determination of movement trajectory can be observed among both animals and people. In a completely autonomous way, unrelated units make decisions on choosing their movement trajectory while moving in a common space. There is no explicit communication between the individual participants of the movement, and there is no central planner coordinating their activities. Nevertheless, large groups of unrelated movement participants are able to coordinate their actions, taking the trajectories of other road users into account and performing the task that is entrusted to them. These observations have inspired us to create a decentralized algorithm for coordinating the movement of mobile robots based on a model of the behavior of moving people. The need for the autonomous coordination of many objects and their management sets high requirements in terms of stability, scalability, and flexibility. The use of simple reactive controllers is a remedy that meets the limitations that are imposed on the algorithm. The solution provides methods for defining a collision-free path of movement based on the current observation of a robot’s environment. The behaviors that are modeled in the controller have been inspired by natural phenomena such as respect for those who stand higher in the social hierarchy, giving priority to people leaving a room, or generally known traffic rules. This paper presents algorithms for mobile robot coordination that are inspired by such phenomena. The algorithms were tested in both real and virtual environments in several settings, such as open space, a narrow passage, and a narrow corridor, and the results are presented. The experimental results were compared with the results of the recognized reciprocal velocity obstacles (RVO) (Van den Berg et al., 2008) method. Moreover, additional video materials are available online; these show the real-life robots in action when running the proposed route-planning algorithms.

Dynamic adaptive intelligent navigation decision making method for multi-object situation in open water

The intelligent navigation system of autonomous ships aims at making fully autonomous navigation decisions to replace the work of seafarers. Realizing intelligent navigation decision-making in open water is the first stage of a fully autonomous navigation system. Through real-time sampling, rapid update of the own ship's motion parameters, and traffic situation information, an open-water intelligent navigation decision-making method that can dynamically adapt to the residual error in the system and the target ship's random manoeuvre is proposed. A digital traffic environment model comprising different target types is built as the basis of navigation decision-making. An optimal feasible plans of the own ship, which will ensure all targets pass safely, is calculated using the velocity obstacle (VO) theory, and dynamic collision avoidance mechanism to construct an autonomous manoeuvring model. The simulation experiments proved that the combined results of this and previous research could realize autonomous navigation in an open water environment having multiple static and dynamic objects under the premise of satisfying the International Regulations for preventing Collisions at Sea, 1972 (COLREGs) and the good seamanship. This research will provide basic theories and methods to realize the autonomous navigation decision-making of ships in an open water environment with multiple targets.

Cooperative Obstacle Avoidance for Multiple UAVs Using Spline_VO Method.

In order to solve multiple unmanned aerial vehicle (UAV) dynamic collision avoidance, a cooperative obstacle avoidance algorithm considering UAV’s kinematic constraints has been developed. In the proposed algorithm, the useful information of UAVs is screened out by a Heartbeat information filtering mechanism and fused by the user datagram protocol (UDP) communication method, which improves communication performance among UAVs. In addition, the velocity obstacle (VO) method combined with cubic uniform B-spline curve is used to avoid obstacles and generate smooth paths, which can be applied to practical scenes. Finally, dynamic and static obstacle avoidance simulations are carried out to verify the effectiveness of the proposed algorithm.

UAV dynamic obstacle avoidance based on improved reciprocal velocity obstacle

This paper aims to solve the problem that when the UAV flies from the starting point to the target point in the velocity space, the obstacle avoidance speed provided by the mutual velocity obstacle method will appear too large turning angle in order to avoid dynamic obstacles. In order to better meet the flight performance of UAV, an improved mutual velocity obstacle method is proposed in this paper. This method can not only solve the problem of path jitter caused by the speed obstacle method, but also solve the problem of excessive turning angle when the velocity direction selected by the mutual velocity obstacle method is flying towards the target point. In addition, the UAV will not return to the original path after the obstacle avoidance, but fly along the target point according to the updated best speed direction, so as to reduce the number of turns. The simulation results show that the improved mutual velocity obstacle method can effectively reduce the turning angle of UAV in dynamic environment and meet the flight performance of UAV.

A dynamic collision avoidance solution scheme of unmanned surface vessels based on proactive velocity obstacle and set-based guidance

This paper proposes a proactive velocity obstacle (PVO) method through pre-judging whether there are collision risks between an unmanned surface vessel (USV) and its obstacle vessels according to the predicted motion states of the USV by its motion mathematical model to optimize the collision avoidance decision-making. Then integrating the proposed PVO method and the line of sight (LOS) algorithm into the set-based guidance (SBG) framework, we create a dynamic collision avoidance (DCA) solution scheme of USVs and carry out simulations in the cases of single vessel and multiple vessels encounter, respectively. The USV-DCA solution scheme can make the USV successfully avoid obstacle vessels while complying with the international regulations for preventing collisions at sea (COLREGs) and follow the desired path without collisions. The simulation results show that the USV-DCA solution scheme based on PVO and SBG can make USVs avert a series of small velocity changes and make a safer collision avoidance decision than the original SBG framework scheme in the multiple vessels encounter case, which verifies the effectiveness and superiority of our proposed USV-DCA solution scheme.

Uncertain moving obstacles avoiding method in 3D arbitrary path planning for a spherical underwater robot

In order to avoid the risk of obstacles collision during the spherical underwater robot (SUR) move to target points in 3D arbitrary path planning, an underwater obstacle avoiding method was studied. Considering the uncertainty of the movement of obstacles in the actual environment, we present an uncertain moving obstacle avoiding method based on the improved velocity obstacle method. In addition, to reduce the distance and time of obstacle avoidance, the concept of the time of obstacle avoiding was designed. First, the size and velocity information of obstacles are obtained through the camera, which can provide an accurate decision basis for obstacles avoidance in the next step. Then, according to the time when the robot collides with the obstacle, the time of start and end of the obstacle avoidance is determined. The movement direction and velocity of the robot are obtained based on the improved velocity obstacle method and the movement characteristics of SUR. Finally, a detailed 3D arbitrary path planning analysis based on an improved ant colony algorithm was conducted. A series of experiments were carried out in the pool that validates the proposed methods are also presented.

Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method

This work provides a generalization of the three-dimensional velocity obstacle (VO) collision avoidance strategy for nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments. A hierarchical architecture is exploited to deal with conflicting multiple subtasks, which are defined as several rotations and are parameterized by quaternions. An improved VO method considering the kinodynamic constraints of a class of fixed-wing unmanned aerial vehicles (UAV) is proposed to implement the motion planning. The position error and velocity error can be mapped onto one desired axis so that, only relying on an engine, UAVs can achieve the goal of point tracking without collision. Additionally, the performance of the closed-loop system is demonstrated through a series of simulations performed in a three-dimensional manner.

Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.

Partly COLREGs-compliant collision avoidance for ASVs using encounter-specific velocity obstacles

A velocity obstacle (VO) based collision avoidance method for autonomous surface vessels operating in restricted and unstructured environments with traffic is considered. We propose a novel VO for enforcing maneuvering compliance with rules 13-15 and 17 in the International Regulations for Preventing Collisions at Sea (COLREGs), where the vessel-to-vessel encounter is first classified w.r.t. the COLREGs, then an encounter-specific domain is assigned to the opposing vessel, and finally a VO for that domain is formulated. The maneuvers of the opposing vessel throughout the encounter is considered when evaluating which side is appropriate to pass the vessel on. This increases robustness to non-compliant behaviour by the opposing vessel. The domain size is determined based on a measure for the available space to maneuver, which ensures an appropriate separation of the vessels in both confined spaces and open waters. Furthermore, collision avoidance with static obstacles from electronic charts is included, where a convex set that is free of static obstacles is constructed to simplify complex geometries, and the boundary of the set is enforced by VOs. The performance of the proposed collision avoidance method is demonstrated through numerical simulation, where it show compliance with COLREGs rules 13-15 and 17. Furthermore, the novel VO shows improved COLREGs compliance compared to another popular COLREGs-specific VO.

Ensuring Robot-Human Safety for the BD Spot Using Active Visual Tracking and NMPC With Velocity Obstacles

When humans and robots operate in and occupy the same local space, proximity detection and proactive collision avoidance is of high importance. As legged robots, such as the Boston Dynamics (BD) Spot, start to appear in real-world application environments, ensuring safe robot-human interactions, while operating in full autonomy mode, becomes a critical gate-keeping technology for trust in robotic workers. Towards that problem, this article proposes a track-and-avoid architecture for legged robots that combines a visual object detection and estimation pipeline with a Nonlinear Model Predictive Controller (NMPC) based on the Optimization Engine, capable of generating trajectories that satisfy the avoidance and tracking problems in real-time operations. The system is experimentally evaluated using the BD Spot, in a custom sensor and computational suite, in fully autonomous operational conditions, for the robot-human safety scenario of quickly moving noncooperative obstacles.

Intent Inference-Based Ship Collision Avoidance in Encounters With Rule-Violating Vessels

All vessels operating in a marine environment are required to comply with the international regulations for preventing collisions at sea (COLREGs), which provide the guidelines and evasive procedures required to resolve potential conflicts between vessels. However, not all vessels strictly abide by COLREGs, often leading to dangerous situations. This paper presents a novel approach for robust collision avoidance in encounter situations involving COLREG-violating vessels. A probabilistic velocity obstacle algorithm based on intent inference is designed and implemented with consideration of the tradeoff between the adherence to traffic rules and the proactive evasive actions for safety. One-to-one and multi-ship encounter situations in the presence of rule-violating vessels are examined through Monte-Carlo simulations, and the results are discussed to demonstrate the feasibility and performance of the proposed approach.