Real-time Collision Avoidance Research Articles

Autonomous collision avoidance system for unmanned vessels: algorithms and software.

Ship collision is one of the most substantial concerns in the global maritime transportation industry. Hence, navigation safety is considerably cited topic in maritime literature. Recently, Unmanned Navigation (UN) technology is gradually becoming more widely used across in the field of marine robotics. The paper investigates the problem of navigation safety in the movement control of Unmanned Vessels (UVs). The object of the study is the process of avoiding collisions of UVs. The subject of the research is the algorithms of the autonomous decision-making system and software for preventing vessel collisions during UN. The intent of this article is to improve the safety of UN by developing new Decision-Making algorithms for autonomous collision prevention of UVs in real time, taking into account the International Rules for the Prevention of Collisions at Sea, 1972 (COLREGs-72) and the recommendations of the Federal agency for sea and inland water transport of the Russian Federation (Rosmorrechflot).In this article, the fundamental concept and the key functions set of the Autonomous Collision Avoidance System (ACAS) are carried out for UVs which are marine transport vehicles capable of sensing its environment and operating without human involvement. Along this line of research, this work focuses on the development of a software algorithm for determining the most dangerous obstacle located within a radius of 12 miles (recommendations of Rosmorrechflot) around an UV based on the principle of vessels collision avoidance geometry, collision risk assessment and the characteristics of obstacles. Moreover, the proposed algorithms can prevent the collision and address the issues of real-time collision avoidance for UVs. The simulation results also demonstrate the promising application of the proposed algorithms in studying the UN safety. Nonetheless, this study provides a way forward to conduct a new information decision-making system design for UVs collision avoidance. This is currently under development, and will be proposed later.

A dynamic obstacle avoidance method for collaborative robots based on trajectory optimization

Background Collision detection is crucial in the design of robot planning algorithms. Efficient distance sensors can provide high-resolution environmental collision information to the robot's planning algorithm. However, this also leads to the robot obstacle avoidance performance being limited by the performance of the sensors. Therefore, it becomes a challenge to achieve efficient obstacle avoidance with low-resolution environmental information. Methods First, we use a self-developed capacitive array non-contact distance sensing flexible surface for sensing the proximity of colliding objects. Second, we designed an optimization-based dynamic obstacle avoidance planning algorithm, using only the minimum separation distance and penetration direction as obstacle avoidance information, and referring to the idea of stochastic gradient descent, using real-time collision avoidance information to do single-step optimization adjustment. Results We conducted the dynamic obstacle avoidance test experiment by connecting the electronic skin to the semi-physical prototype and the full physical prototype. The experiments show that efficient dynamic obstacle avoidance can be realized under the maximum effective range of only 5~7cm, and it has strong flexibility to avoid different shapes of dynamic obstacles in a non-contact manner, and finally arrive at the target position. Conclusions In this paper, an online obstacle avoidance planning algorithm designed based on an optimization method that is not limited to the shape of obstacles is proposed, and the effectiveness of the algorithm is verified by physical experiments in combination with a self-developed flexible distance sensing surface. It is of great significance for the safe operation of human-robot interaction in collaborative robots.

Real-time planning and collision avoidance control method based on deep reinforcement learning

Real time planning and collision avoidance is an important part of the motion planning for USVs, its core is to design effective planning and collision avoidance methods. This paper proposes a real-time planning and collision avoidance method based on deep reinforcement learning for USVs in complex environments. Firstly, a novel reward function is designed, which transforms the collision avoidance problem into a deep reinforcement learning strategy solution problem that includes distance, direction, speed, and GOLREGs constraints, it ensures the smooth and safe operation of the USV. Secondly, inefficient exploration and exploitation methods make it difficult for intelligent agents to discover key information in the environment, high value experience is obtained through the designed layered sampling exploration mechanism in this article, the intelligent agent learns effective control strategies through this mechanism, which accelerates the convergence of the strategies and reduces the waste of computing resources. Finally, a real-time collision avoidance simulation platform for the USV is established, and experimental results of different exploration mechanisms are compared, the efficiency of the layered sampling exploration mechanism has been verified. The simulation scenarios from easy to difficult are designed, and the results indicate that the USV can complete real-time planning and collision avoidance tasks in complex environments.

Research on Collision Avoidance Systems for Intelligent Vehicles Considering Driver Collision Avoidance Behaviour

In this paper, a new collision avoidance switching system is proposed to address the lack of adaptability of intelligent vehicles under different collision avoidance operating conditions. To ensure the rationality of the collision avoidance switching strategy for intelligent vehicles, the NGSIM road dataset is introduced to analyse the driver’s collision avoidance behaviour, and a two-layer fuzzy controller considering the overlap rate is established to design the collision avoidance switching strategy. In order to achieve real-time collision avoidance system activation, a lane change collision avoidance model based on MPC control is also developed. Finally, a simulation environment was created using Matlab/CarSim for simulation verification. The simulation results show that the collision avoidance switching system is more responsive and has a shorter start-up distance and is more adaptable to different driving conditions.

Path Planning of an Unmanned Surface Vessel Based on the Improved A-Star and Dynamic Window Method

In order to ensure the safe navigation of USVs (unmanned surface vessels) and real-time collision avoidance, this study conducts global and local path planning for USVs in a variable dynamic environment, while local path planning is proposed under the consideration of USV motion characteristics and COLREGs (International Convention on Regulations for Collision Avoidance at Sea) requirements. First, the basis of collision avoidance decisions based on the dynamic window method is introduced. Second, the knowledge of local collision avoidance theory is used to study the local path planning of USV, and finally, simulation experiments are carried out in different situations and environments containing unknown obstacles. The local path planning experiments with unknown obstacles can prove that the local path planning algorithm proposed in this study has good results and can ensure that the USV makes collision avoidance decisions based on COLREGs when it meets with a ship.

Fast Obstacle Avoidance Based on Real-Time Sensing

Humans excel at navigating and moving through dynamic and complex spaces, such as crowded streets. For robots to do the same, it is crucial that they are endowed with highly reactive obstacle avoidance which is adept at partial and poor sensing. We address the issue of enabling obstacle avoidance based on sparse and asynchronous perception. The proposed control scheme combines a high-level input command provided by either a planner or a human operator with fast reactive obstacle avoidance (FOA). The sampling-based sensor data can be combined with an analytical reconstruction of the obstacles for real-time collision avoidance. Thus, we can ensure that the agent does not become stuck when a feasible path exists between obstacles. Our algorithm was evaluated experimentally on static laser data from cluttered, indoor office environments. Additionally, it was used in shared-control mode in a dynamic and complex outdoor environment in the center of Lausanne. The proposed control scheme successfully avoided collisions in both scenarios. During the experiments, the controller took 1 ms to evaluate over 30000 data points.

A Receding Horizon Navigation and Control System for Autonomous Merchant Ships: Reducing Fuel Costs and Carbon Emissions under the Premise of Safety

In order to solve the multi-objective planning and trajectory tracking control problem related to maritime autonomous surface ships (MASSs), a new design scheme for autonomous navigation is proposed in this paper, with a receding horizon navigation and control (RHNC) system that contains navigation and control modules. In the navigation module, we designed a superposition field gradient descent local search algorithm based on the cost field, emission field, and guidance field to navigate the MASS reference path, and in the control module, we designed a nonlinear controller that can handle multiple constraints based on the NMPC framework. Under the new scheme, the navigation module completes local path planning to reduce costs and emissions, the control module accomplishes accurate trajectory tracking and real-time collision avoidance, and the information is transmitted in both directions between the two modules to collaboratively complete the MASS navigation and control tasks. We conducted a simulation study of the navigation algorithm and controller and the autonomous navigation system using a Kriso Container ship (KCS). The simulation results demonstrate the effectiveness of the proposed cooperative design scheme in reducing navigation costs and emissions and avoiding autonomous collision avoidances.

An Enabling Trajectory Planning Scheme for Lane Change Collision Avoidance on Highways

This paper presents a hierarchical three-layer trajectory planning framework to realize real-time collision avoidance under complex driving conditions. This is mainly ascribed to the generation of a collision-free trajectory cluster based on the speed and the path re-planning. The upper-layer controller is to generate a reference quintic polynomial trajectory based on the Sequential Quadratic Programming by assuming mild speed and acceleration variations of the surrounding vehicles. The waypoints and time stamps can be obtained via the reference trajectory. When the assumption is invalid under complex driving conditions, the middle-layer controller would generate a Quadratic Programming-based trajectory cluster to assign different time stamps to each waypoint through time-based sampling methods. The lower-layer controller would be triggered to create a new feasible trajectory based on the path sampling if the collision avoidance requirements are not satisfied. The host vehicle will return to its original lane if no feasible time window is available to perform a lane change maneuver under the vehicle kinematics and lane change time/displacement constraints. The effectiveness of the proposed scheme is verified under various scenarios through comprehensive simulations.

COLREGs Experience-Based Real-Time Route Planning of Intelligent Ship

The route planning of intelligent ship has been studied a lot, especially in the dynamic planning of collision avoidance, but it is still faced with some difficulties, such as complex model structure, poor compatibility of collision avoidance rules, difficult to practice and so on. Therefore, this paper adopts the integration of field model theory and COLREGs for real-time ship route planning, taking into account the model efficiency and application value. It uses FM algorithm as the basic route planning algorithm, introducing the virtual electric field model to construct the ship navigation situation, and gives the guidance strategy of collision avoidance behavior under different encounter situations considering the COLREGs and daily navigation experience, which integrates it into the field model to form a real-time collision avoidance adjustment route planning algorithm. The proposed algorithm has been compared and verified by simulation in multiple scenes, it is worth mentioning that the algorithm can potentially be developed to advance collision-free route planning in unconstructed environment.

Stepwise Cooperative Trajectory Planning for Multiple BUVs Based on Temporal–Spatial Bezier Curves

Biomimetic underwater vehicles (BUVs) are important instrumentation in applications such as water quality monitoring and underwater detection. In this paper, we study the cooperative trajectory planning problem for multiple BUVs and propose a stepwise trajectory planning algorithm based on temporal-spatial Bezier curves to enable cooperative formations. We introduce the time variable into the three-dimensional (3D) Bezier curve, designing a four-dimensional temporal-spatial Bezier curve. Considering the various physical constraints of the BUV, we obtain a smooth and feasible trajectory using Particle Swarm Optimization (PSO) when taking the shortest trajectory distance as the optimization goal. On this basis, we propose a method to perform cooperative trajectory planning and efficient obstacle avoidance replanning for multiple BUVs by integrating the real-time collision avoidance, real-time detection, and underwater complex terrain constraints. We verify the effectiveness of the proposed cooperative planning method through simulation and comparison.

Collision avoidance control for unmanned surface vehicle with COLREGs compliance

In this paper, a collision avoidance control algorithm for unmanned surface vehicle (USV) with COLREGs is studied. To solve the constraints of actuator and dynamics in the maneuvering characteristics and the estimation of velocity window parameters in dynamic window approach (DWA), a real-time collision avoidance control algorithm based on dynamic constraints is proposed according to the principle of DWA, which mainly includes the design of candidate control behavior space, feasible trajectory prediction, collision risk assessment, acquisition of optimal control quantities and so on. The candidate control behavior space designed according to DWA meets the actuator constraints; The feasible trajectory prediction method can meet the dynamic constraints and avoid the problem of velocity window parameters estimation in DWA; The optimal control quantity method constructs the collision avoidance control evaluation function according to the collision risk evaluation principle, and obtain the feasible dynamic trajectory and collision avoidance control quantities at the same time. In addition, the collision avoidance rule function is introduced into the collision avoidance control evaluation function to enable the USV to comply with the COLREGs. Finally, simulation results are provided to illustrate the efficacy of collision avoidance control algorithm.

Multi-Joint Active Collision Avoidance for Robot Based on Depth Visual Perception

Dear editor, Human-robot collaboration is a research topic that has numerous potential applications, such as in smart cities. An important safety consideration in human-robot collaboration is collision avoidance [1]. Many studies have prioritized collision avoidance of the robot end-effector. However, multi-joint (whole-body) collision avoidance is also very important in complex working scenarios. Several studies have achieved multi-joint collision avoidance by estimating the distance between obstacles and control points placed on the robot body, which adds computational effort to the collision-detection process. Therefore, we combine a robot skeleton with depth visual perception to achieve a fast multi-joint collision avoidance response. This method can be divided into collision detection and collision avoidance strategies, as illustrated in Fig. 1. In collision detection, we use a statistical filtering algorithm to denoise visual data, and propose a real-time obstacle distance-estimation algorithm based on a robot skeleton. In the collision avoidance strategy, a multi-joint repulsive force model is established based on the artificial potential field, and a motion control strategy is developed to obtain a collision-free trajectory. Real-time collision avoidance experiments with a 7-DOF collaborative robot are conducted to verify our approach. The experimental results show that our method enables the robot to have the capability of active collision avoidance for both the end-effector and the whole body. The robot can avoid obstacles and complete desired tasks with a smooth trajectory.

Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment

The introduction of collaborative robots in industrial environments reinforces the need to provide these robots with better cognition to accomplish their tasks while fostering worker safety without entering into safety shutdowns that reduce workflow and production times. This paper presents a novel strategy that combines the execution of contact-rich tasks, namely disassembly, with real-time collision avoidance through machine learning for safe human-robot interaction. Specifically, a goal-conditioned reinforcement learning approach is proposed, in which the removal direction of a peg, of varying friction, tolerance, and orientation, is subject to the location of a human collaborator with respect to a 7-degree-of-freedom manipulator at each time step. For this purpose, the suitability of three state-of-the-art actor-critic algorithms is evaluated, and results from simulation and real-world experiments are presented. In reality, the policy’s deployment is achieved through a new scalable multi-control framework that allows a direct transfer of the control policy to the robot and reduces response times. The results show the effectiveness, generalization, and transferability of the proposed approach with two collaborative robots against static and dynamic obstacles, leveraging the set of available solutions in non-monotonic tasks to avoid a potential collision with the human worker.

Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles

AbstractCollision avoidance is critical in multirobot systems. Most of the current methods for collision avoidance either require high computation costs (e.g., velocity obstacles and mathematical optimization) or cannot always provide safety guarantees (e.g., learning-based methods). Moreover, they cannot deal with uncertain sensing data and linguistic requirements (e.g., the speed of a robot should not be large when it is near to other robots). Hence, to guarantee real-time collision avoidance and deal with linguistic requirements, a distributed and hybrid motion planning method, named Fuzzy-VO, is proposed for multirobot systems. It contains two basic components: fuzzy rules, which can deal with linguistic requirements and compute motion efficiently, and velocity obstacles (VOs), which can generate collision-free motion effectively. The Fuzzy-VO applies an intruder selection method to mitigate the exponential increase of the number of fuzzy rules. In detail, at any time instant, a robot checks the robots that it may collide with and retrieves the most dangerous robot in each sector based on the predicted collision time; then, the robot generates its velocity in real-time via fuzzy inference and VO-based fine-tuning. At each time instant, a robot only needs to retrieve its neighbors’ current positions and velocities, so the method is fully distributed. Extensive simulations with a different number of robots are carried out to compare the performance of Fuzzy-VO with the conventional fuzzy rule method and the VO-based method from different aspects. The results show that: Compared with the conventional fuzzy rule method, the average success rate of the proposed method can be increased by 306.5%; compared with the VO-based method, the average one-step decision time is reduced by 740.9%.

Collision Avoidance Method for Autonomous Ships Based on Modified Velocity Obstacle and Collision Risk Index

A novel real-time collision avoidance method for autonomous ships based on modified velocity obstacle (VO) algorithm and grey cloud model is proposed. A typical VO algorithm is used to judge whether there is a collision risk for ships in the potential collision area (PCA). Then, in order to quantify the collision risk of ships in different encounter situations within the PCA and trigger a prompt warning of danger of collision, this study sets up a novel collision risk assessment method based on asymmetric grey cloud model (AGC). It can effectively consider the randomness, ambiguity, and incompleteness of the information in the ship collision risk evaluation process. Moreover, reachable collision-free velocity sets under different encounter situations and optimal steering angle model are constructed. A real-time collision avoidance method based on modified VO algorithm and manoeuvring motion characteristics of vessels is put forward. In this model, various constraints are considered including the International Regulations for Preventing Collisions at Sea (COLREGs), ship manoeuvrability, and ordinary practice of seaman. Finally, several case studies are carried out to verify the performance and reliability of the collision avoidance model. The results show that the proposed method can not only effectively identify and quantify the collision risk in real-time but also offer proper collision-free solutions for autonomous ships.

Collision Avoidance and Direction Planning for Autonomous Vehicles

The rapid development of information and signal processing technology has contributed significantly to the development of autonomous driving (AD) techniques, which improve driving safety while minimizing human efforts. According to analyst predictions, automatic cars will soon surpass manual cars in number as automatic driving becomes more sophisticated. Ensuring safety and reducing the road accidents is the primary concerns in automated vehicles. Recent research has shown that computer vision, deep learning, and other fields have advanced beyond the imagination. The paper covers the deep learning algorithms and techniques necessary to make a reliable and real-time collision avoidance system using concepts such as Convolutional Neural Networks (CNN), YOLOv4 (You Look Only Once), and other deep learning architectures while also reviewing the current state of the art strategies for Advanced Driver Assistance System (ADAS). By using CNN algorithms, it is possible to detect lane markings and signboards in real time as well as estimate distances between them.

DCA-Based Collision Avoidance Path Planning for Marine Vehicles in Presence of the Multi-Ship Encounter Situation

The problem of ship collision avoidance path planning is one of the key problems in the ship motion control field. Aiming at the high computational time problem of path planning in multi-ship encounter situations and the impact of the target ship’s action changes on path planning, this paper proposes a dynamic path-planning method based on dynamic cluster analysis (DCA), which is used to dynamically cluster target ships with similar attributes into a group ship, reducing the number of calculated targets and improving the efficiency of path planning. Taking into full consideration the action requirements of the International Regulations for Preventing Collisions at Sea (COLREGs), the course alteration action matrix (CAAM) for collision avoidance is established to limit the space of candidate solutions. On the basis of the rapid optimization capability of the deterministic optimization algorithm (DOA), a dynamic monitoring mechanism is introduced to establish a multi-ship encounter intelligent collision avoidance decision-making model that meets the needs of real-time collision avoidance. The simulation results showed that the method can obtain a dynamic collision avoidance path that is safe and feasible.

Safe, Fast, and Efficient Distributed Receding Horizon Constrained Control of Aerial Robot Swarms

This letter proposes a reactive, safe, and high-performance distributed constrained control law for ensuring safety in multi-UAV systems. The UAVs possess higher-order nonlinear dynamics and are subject to actuation and collision avoidance constraints. To address the case of real-time multi-UAV collision avoidance, we propose a novel Trajectory-Tube Distributed Explicit Reference Governor (TT-D-ERG) approach based on the idea of embedding future state trajectories into safety tubes. The control law is ideally suited for systems with limited on-board hardware for computation and communication. The safety, efficacy, performance, and scalability of the method are demonstrated and benchmarked against state-of-the-art real-time collision avoidance methods by simulations and field experiments with multiple quadrotor UAVs (videos at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/V4P1O8vhKlo</uri> ).

MADER: Trajectory Planner in Multiagent and Dynamic Environments

This article presents MADER, a 3-D decentralized and asynchronous trajectory planner for UAVs that generates collision-free trajectories in environments with static obstacles, dynamic obstacles, and other planning agents. Real-time collision avoidance with other dynamic obstacles or agents is done by performing outer polyhedral representations of every interval of the trajectories and then including the plane that separates each pair of polyhedra as a decision variable in the optimization problem. MADER uses our recently developed MINVO basis to obtain outer polyhedral representations with volumes 2.36 and 254.9 times, respectively, smaller than the Bernstein or B-Spline bases used extensively in the planning literature. Our decentralized and asynchronous algorithm guarantees safety with respect to other agents by including their committed trajectories as constraints in the optimization and then executing a collision check-recheck scheme. Finally, extensive simulations in challenging cluttered environments show up to a 33.9% reduction in the flight time, and a 88.8% reduction in the number of stops compared to the Bernstein and B-Spline bases, shorter flight distances than centralized approaches, and shorter total times on average than synchronous decentralized approaches.

UAV Obstacle Avoidance Algorithm to Navigate in Dynamic Building Environments

In this work, a real-time collision avoidance algorithm was presented for autonomous navigation in the presence of fixed and moving obstacles in building environments. The current implementation is designed for autonomous navigation between waypoints of a predefined flight trajectory that would be performed by an UAV during tasks such as inspections or construction progress monitoring. It uses a simplified geometry generated from a point cloud of the scenario. In addition, it also employs information from 3D sensors to detect and position obstacles such as people or other UAVs, which are not registered in the original cloud. If an obstacle is detected, the algorithm estimates its motion and computes an evasion path considering the geometry of the environment. The method has been successfully tested in different scenarios, offering robust results in all avoidance maneuvers. Execution times were measured, demonstrating that the algorithm is computationally feasible to be implemented onboard an UAV.