Collision Avoidance Algorithm Research Articles

Convex distributed robust model predictive control for collision and obstacle avoidance

AbstractThe article proposes a convex distributed robust model predictive control algorithm for collision avoidance in multi‐agent systems with additive disturbances, which utilizes the concept of tube model predictive control to address the disturbances. To tackle the coupled collision avoidance constraints, each agent integrates assumed nominal position and input trajectories from its neighbors, rather than relying on actual ones. Compatibility constraints are then designed using the normal vector of the constructed separating hyperplanes for collision avoidance and the residual collision avoidance margin of the optimal solutions at the last time step to restrict deviations between assumed and actual trajectories and ensure consistency among the agents. The nonconvex collision and obstacle avoidance constraints are convexified using the concept of safe sets, transforming them into time‐varying closed polyhedral constraints while accounting for the impact of disturbances. Particularly, the residual collision avoidance margin is incorporated into the construction of safe sets for the satisfaction of coupled collision avoidance constraints. Consequently, the original collision avoidance optimal control problems can be efficiently and simultaneously solved for all agents using standard quadratic programming techniques. Under the design of local terminal constraint sets for each agent, the proposed algorithm ensures a rigorous analysis of robust constraint satisfaction for collision avoidance constraints, as well as an assessment of recursive feasibility and closed‐loop stability. The effectiveness of the algorithm is demonstrated through an illustrative example, and simulation results validate its ability to successfully achieve collision and obstacle avoidance even in the presence of disturbances.

Integrated Autonomous Underwater Vehicle Path Planning and Collision Avoidance Algorithms

Integrated Autonomous Underwater Vehicle Path Planning and Collision Avoidance Algorithms

Framework of safety evaluation and scenarios for automatic collision avoidance algorithm

This paper proposes a novel method to design a scenario set for evaluating automatic collision avoidance algorithms. While many automatic collision avoidance methods have been proposed recently, they have been verified using different methods and scenarios. This study aims to construct a scenario set that covers all possible encounter situations between ships. Based on risks, rules such as the COLREGs and characteristics of encounter angle of ships, the scenario sets of one-on-one and one-on-two encounter situations are built. For the evaluation of collision avoidance maneuvers, the authors provide the analytical solution in the time domain of evaluation indices using the position and velocity vectors of two ships. The authors also present the design method of the additional scenario to evaluate the automatic ship avoidance maneuvering algorithm, for the safety verification of autonomous and unmanned ships.

Improved Collision Avoidance Algorithm of Autonomous Rice Transplanter Based on Virtual Goal Point

To ensure the operation safety and efficiency of an autonomous rice transplanter, a path planning method of obstacle avoidance based on the improved artificial potential field is proposed. Firstly, the obstacles are divided into circular or elliptic obstacles according to the difference between the length and width of an obstacle as well as the angle between the vehicle’s forward direction and the length direction of the obstacle. Secondly, improved repulsive fields for circular and elliptic models are developed. To escape the local minimum and goal inaccessibility of the traditional artificial potential field as well as meet the requirements of agronomy and vehicle kinematics constraints, the adaptive setting and adjusting strategy for virtual goal points is proposed according to relative azimuth between obstacle and vehicle. The path smoothing method based on the B-spline interpolation method is presented. Finally, the intelligent obstacle avoidance algorithm is designed, and the path evaluation rule is given to obtain the low-cost, non-collision, smooth and shortest obstacle avoidance path. To verify the effectiveness of the proposed obstacle avoidance algorithm, simulation and field experiments are conducted. Simulation and experimental results demonstrate that the proposed improved collision avoidance algorithm is highly effective and realizable.

Collision Avoidance Capabilities in High-Density Airspace Using the Universal Access Transceiver ADS-B Messages

The safe integration of a large number of unmanned aircraft systems (UASs) into the National Airspace System (NAS) is essential for advanced air mobility. This requires reliable air-to-air transmission systems and robust collision avoidance algorithms. Automatic Dependent Surveillance-Broadcast (ADS-B) is a potential solution for a dependable air-to-air messaging system, but its reliability when stressed with hundreds to thousands of vehicles operating simultaneously is in question. This paper presents an ADS-B model and analyzes the capabilities of the Universal Access Transceiver (UAT), which operates at a frequency of 978 MHz. We use a probabilistic collision avoidance algorithm to examine the impact of varying parameters, including the number of vehicles and the transmission power of the UAT, on the overall safety of the vehicles. Additionally, we investigate the root causes of co-channel interference, proposing enhancements for safe operations in environments with a high density of UAS. Simulation results show message success and collision rates. With our proposed enhancements, UAT ADS-B can provide a decentralized air traffic system that operates safely in high-density situations.

Synthetic Maritime Traffic Generation System for Performance Verification of Maritime Autonomous Surface Ships

With the global advancement of maritime autonomous surface ships (MASS), the critical task of verifying their key technologies, particularly in challenging conditions, becomes paramount. This study introduces a synthetic maritime traffic generation system (S-MTGS) designed for the efficient and safe verification of these technologies. The S-MTGS encompasses a maritime traffic generator integrating a generator based on absolute position (GAP) and a generator based on relative position (GRP). This innovative system leverages historical maritime data to create various scenarios or generate virtual ships based on their interactions with and proximity to other ships. The virtual ships adeptly navigate and perform collision avoidance maneuvers with nearby vessels enabled by the integrated collision avoidance algorithm. The S-MTGS’s ability to generate a wide range of maritime traffic information mirroring actual maritime conditions is pivotal for thoroughly verifying the performance of MASS technology under both standard and extreme situations. The development of the S-MTGS represents a significant advancement in maritime safety and technology. It can evaluate collision avoidance and navigation systems in MASS, featuring a virtual environment for realistic scenario testing and an intelligent navigation system focused on route tracking and collision avoidance.

Safe Motion Planning and Control Framework for Automated Vehicles with Zonotopic TRMPC

Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles (AVs). The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer. To address this issue, this study proposes a safe motion planning and control (SMPAC) framework for AVs. For the control layer, a dynamic model including multi-dimensional uncertainties is established. A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set. A flexible tube with varying cross-sections is constructed to reduce the controller conservatism. For the planning layer, a concept of safety sets, representing the geometric boundaries of the ego vehicle and obstacles under uncertainties, is proposed. The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories. An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles. A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC. The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.

Collision avoidance trajectory planning for a dual-robot system: using a modified APF method

AbstractDual-robot system has been widely applied to the field of handling and palletizing for its high efficiency and large workspace. It is one of the key problems of the trajectory planning to determine the collision avoidance method of the dual-robot system. In the present study, a collision avoidance trajectory planning method for the dual-robot system was proposed on the basis of a modified artificial potential field (APF) algorithm. The interference and collision criterion of the dual-robot system was given firstly, which was established based on the method of kinematic analysis in robotics. And then, in consideration of the problem of excessive virtual potential field force induced by using the traditional APF algorithm in the process of dual-robot trajectory planning, a modified APF algorithm was proposed. Finally, the modified APF algorithm was used for motion control of a dual-robot palletizing process, and the collision avoidance performance of the proposed collision avoidance algorithm was studied through a dual-robot palletizing simulation and experiment. The results have shown that with the proposed collision avoidance trajectory planning algorithm, the two robots in dual-robot system can maintain a safe distance at all times during palletizing process. Compared with the traditional APF and rapidly-exploring random tree (RRT) algorithm, the trajectory solution time of the modified APF algorithm is greatly reduced. And the modified APF algorithm’s convergence time is 14.2% shorter than that of the traditional APF algorithm.

Relaxing the Limitations of the Optimal Reciprocal Collision Avoidance Algorithm for Mobile Robots in Crowds

Relaxing the Limitations of the Optimal Reciprocal Collision Avoidance Algorithm for Mobile Robots in Crowds

Multi-level decision framework collision avoidance algorithm in emergency scenarios

Multi-level decision framework collision avoidance algorithm in emergency scenarios

Adversarial waypoint injection attacks on Maritime Autonomous Surface Ships (MASS) collision avoidance systems

ABSTRACT Autonomous navigation is currently subject to particular interest for naval and commercial vessels. In addition to the regulatory effort, several pieces of research tackled the development of new guidance laws, stable and robust control algorithms, methodologies to increase situational awareness, and collision avoidance algorithms. However, most of these systems blindly trust information from navigation sensors, which can malfunction or, even worse, have their data altered by cyber-attacks. This last scenario will be increasingly common shortly and represents a dangerous threat that future highly computerised ships must face. The proposed work shows an approach capable of hijacking a state-of-the-art collision avoidance algorithm route by feeding it with rogue data. The effects of such a cyber-attack are shown via an extensive simulation campaign, testing the methodology on three different COLREG scenarios: head-on, crossing, and overtaking. The study additionally highlights the susceptibility of forthcoming navigation systems to security breaches, which still needs to be addressed in the existing literature. According to the experimental findings, attackers can attract the autonomous ship guidance toward their objective at a mean distance of 133 m . Finally, suggestions on how to harden these systems against this family of attacks are provided in the conclusion.

Autonomous collision avoidance method for MASSs based on precise potential field modelling and COLREGs constraints in complex sailing environments

Autonomous collision avoidance (CA) for maritime autonomous surface ships (MASSs) in complex navigational environments requires accurate environment modelling and efficient CA algorithms. This study proposes a fast construction method for complex environmental potential field models applicable to vector charts. It accurately represents all complex static obstacles, including concave polygons, and dynamically adjusts the range of influence of a potential field according to its danger level. For dynamic ships, a dynamic obstacle modelling method, based on the quaternion ship domain, is designed to highlight the uncertain motion characteristics of ships. Based on the coupled modelling of dynamic and static obstacles, the virtual potential field of target ships, constrained by the International Regulations for Preventing Collisions at Sea (COLREGs), was constructed. By combining the first-order Nomoto model and optimal field prediction strategy, an optimal CA path, satisfying ship dynamics and COLREGs constraints, was realised, and the local optimal problem of the traditional artificial potential field method was solved. Simulation results show that the proposed method accomplishes fast and accurate modelling of complex environments and realises autonomous path planning and real-time multivessel CA tasks for MASSs in complex water considering the COLREGs constraints. Thus, the proposed method can be implemented in actual ships.

Ship encounter scenario generation for collision avoidance algorithm testing based on AIS data

Collision avoidance algorithms play a crucial role in assisting onboard officers and ensuring navigation safety. To ensure the effectiveness of these algorithms, comprehensive testing with a wide range of scenarios is necessary before widespread deployment. However, there are currently few systematic methods available for generating such testing scenarios. In this paper, a general framework is proposed to generate testing scenarios based on Automatic Identification System (AIS) data, consisting of three modules: encounter scenario extraction, scenario importance evaluation, and testing scenario sampling. Firstly, ship encounter scenarios are extracted from the AIS data according to spatial-temporal proximity relationships between ships. Subsequently, a two-factor model is developed to evaluate the importance of extracted scenarios, considering both exposure frequency and collision risk. Finally, the unequal probability sampling method, which takes into account the importance of each scenario, is utilized to construct the testing scenario set. The proposed framework is validated through a systematic experiment using real AIS data. The results demonstrate that the framework effectively extracts diverse encounter scenarios from AIS data and rationally evaluates their importance. On this basis, an appropriate scenario set can be generated for collision avoidance algorithm testing. The generated scenario set ensures comprehensive coverage while prioritizing high-importance scenarios.

A path planning algorithm for three-dimensional collision avoidance based on potential field and B-spline boundary curve

Path planning is one of the key technologies in unmanned aerial systems. The path-planning algorithm for UAVs in this study incorporates a three-dimensional obstacle model, addressing the limitations of existing research that primarily focuses on the two-dimensional plane. This approach elevates traditional two-dimensional obstacle constraints to a three-dimensional space, fulfilling the requirements for precise obstacle avoidance. Utilizing B-spline curves, an obstacle boundary model is proposed, which, in combination with an improved artificial potential field accounting for localized self-locking oscillations, achieves smooth path planning for obstacle avoidance from the starting point to the destination. The simulation results show the effectiveness of this method in collision-free path planning within three-dimensional environments containing static single or multiple obstacles. At the points of maximum curvature in the two-dimensional coordinate system, the path planned by the proposed algorithm exhibits a smoothness improvement of 68 % and 98 %, respectively, as compared to the traditional artificial potential field algorithm with the equivalent three-dimensional obstacle model and the tangent point method. The proposed algorithm enables drones to achieve precise obstacle avoidance along surfaces, generating smoother collision-free flight paths in a shorter period of time.

QuickNav: An Effective Collision Avoidance and Path-Planning Algorithm for UAS

Obstacle avoidance is a desirable capability for Unmanned Aerial Systems (UASs)/drones which prevents crashes and reduces pilot fatigue, particularly when operating in the Beyond Visual Line of Sight (BVLOS). In this paper, we present QuickNav, a solution for obstacle detection and avoidance designed to function as a pre-planned onboard navigation system for UAS flying in a known obstacle-cluttered environment. Our method uses a geometrical approach and a predefined safe perimeter (square area) based on Euclidean Geometry for the estimation of intercepting points, as a simple and efficient way to detect obstacles. The square region is treated as the restricted zone that the UAS must avoid entering, therefore providing a perimeter for manoeuvring and arriving at the next waypoints. The proposed algorithm is developed in a MATLAB environment and can be easily translated into other programming languages. The proposed algorithm is tested in scenarios with increasing levels of complexity, demonstrating that the QuickNav algorithm is able to successfully and efficiently generate a series of avoiding waypoints. Furthermore, QuickNav produces shorter distances as compared to those of the brute force method and is able to solve difficult obstacle avoidance problems in fractions of the time and distance required by the other methods. QuickNav can be used to improve the safety and efficiency of UAV missions and can be applied to the deployment of UAVs for surveillance, search and rescue, and delivery operations.

Understanding of sailing rule based on COLREGs: Comparison of navigator survey and automated collision-avoidance algorithm

The Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) comprises rules to prevent collisions at sea and is based on the qualitative rules and ordinary practice of seamen. However, because the criteria of the sailing rule interpretation are different, the problem of misunderstanding occurs owing to the difference in the interpretation between navigators and collision-avoidance algorithm designers. Therefore, this study aims to clarify the collision situations by understanding in the sailing rule interpretation of COLREGs. To identify the understanding of collision situations, we surveyed the navigators’ understanding of collision situations and reviewed the collision-avoidance algorithms used in recent studies. The survey results were analyzed based on head-on and crossing (HC) and crossing and overtaking (OC) situations. Results showed that navigators were unsure about whether the HC situation sailing rule should be applied to incoming vessels from the 008° and whether the overtaking or crossing situation should be applied to coming vessels at 160°. In OC situations, as the angle increased from 130°, it became difficult for navigators to interpret the navigation rules. A notable disparity emerged between the navigators’ understanding and the automated collision-avoidance algorithms. Moreover, it was confirmed that practical navigators exhibited divergent interpretations of these regulations. This study contributes to provide an understanding of COLREGs sailing rules based on the understanding of navigators and researchers.

Quadratic Programming for Continuous Control of Safety-Critical Multiagent Systems Under Uncertainty

This paper studies the control problem for safety-critical multi-agent systems based on quadratic programming (QP). Each controlled agent is modeled as a cascade connection of an integrator and an uncertain nonlinear actuation system. In particular, the integrator represents the position-velocity relation, and the actuation system describes the dynamic response of the actual velocity to the velocity reference signal. The notion of input-to-output stability (IOS) is employed to characterize the essential velocity-tracking capability of the actuation system. The standard QP algorithms for collision avoidance may be infeasible due to uncertain actuator dynamics. Even if feasible, the solutions may be non-Lipschitz because of possible violation of the full rank condition of the active constraints. Also, the interaction between the controlled integrator and the uncertain actuator dynamics may lead to significant robustness issues. Based on the current development of nonlinear control theory and numerical optimization methods, this paper first contributes a new feasible-set reshaping technique and a refined QP algorithm for feasibility, robustness, and local Lipschitz continuity. Then, we present a nonlinear small-gain analysis to handle the inherent interaction for guaranteed safety of the closed-loop multi-agent system. The proposed method is illustrated by numerical simulation and a physical experiment.

A novel route-plan-guided artificial potential field method for ship collision avoidance: Modeling, integration and test

To address the technical difficulties of artificial potential filed (APF)-based automatic collision avoidance (CA) algorithms, a stable and reliable technological framework was established for autonomous CA from the perspectives of navigation practice and real-ship application. First, an improved route-plan-guided APF (RPGAPF) approach was introduced, considering the ship motion and constraints by the International Regulations for Preventing Collisions at Sea (COLREGS). Second, a simulation-based real-ship accompanying test system (SRSATS) was well-designed for the intelligent navigation projects and installed on a 45.0 m length ship. Third, various real-ship open-loop CA tests were conducted under different encountering situations, and concurrently, simulation testing in both open-loop and closed-loop were carried out. The results indicated that simulation tests may be used as precursors before real-ship trials, and that the proposed SRSATS can be employed as a practical verification method for real-ship testing to generate variety of complex encountering situations. The CA procedures and results for the corresponding real-ship and simulation tests produced similar trends, and the improved RPGAPF approach can be applied in real-ship automatic CA systems owing to its dependability, determinism, and repeatability. This study proposes a complete solution for the APF-base CA approach, including system modelling, integration and testing. The findings provide valuable insights into the ship CA research and contribute to the improvement of ship intelligence.

Formation Transformation Method for UUV Group to Approach a Static Target

The unmanned underwater vehicle (UUV) group composed of UUVs carrying different kinds of detection equipment is powerful for underwater target searching and detection. In this paper, a formation transformation method, used while the mission of the UUV group transformed from searching to detecting, is proposed. Firstly, a new formation transformation strategy, in which the UUVs converged to their detection points followed by the aggregation points achieved, is proposed to improve the safety of UUVs during transformation. Following the proposed strategy, particle swarm optimization is employed to find the optimal aggregation points. Finally, possible collisions between UUVs and collisions between UUVs and targets are avoided by adding a collision avoidance algorithm based on a virtual torque field. The model prediction is used to solve the delay problem of underwater communication, and the communication results are used to correct the model prediction results. The test shows that the method is effective. The effectiveness of the proposed method is demonstrated using two simulation examples.

Object Detection and Distance Measurement Algorithm for Collision Avoidance of Precast Concrete Installation during Crane Lifting Process

In the construction industry, the process of carrying heavy loads from one location to another by means of a crane is inevitable. This reliance on cranes to carry heavy loads is more obvious when it comes to high-rise building construction. Depending on the conditions and requirements on-site, various types of construction lifting equipment (i.e., cranes) are being used. As off-site construction (OSC) is gaining more traction recently, cranes are becoming more important throughout the construction project as precast concrete (PC) members are major components of OSC calling for lifting work. As a result of the increased use of cranes on construction sites, concerns about construction safety as well as the effectiveness of existing load collision prevention systems are attracting more attention from various parties involved. Besides the inherent risks associated with heavy load lifting, the unpredictable movement of on-site workers around the crane operation area, along with the presence of blind spots that obstruct the crane operator’s field-of-view (FOV), further increase the accident probability during crane operation. As such, the need for a more reliable and improved collision avoidance system that prevents lifted loads from hitting other structures and workers is paramount. This study introduces the application of deep learning-based object detection and distance measurement sensors integrated in a complementary way to achieve the stated need. Specifically, the object detection technique was used with the application of an Internet Protocol (IP) camera to detect the workers within the crane operation radius, whereas ultrasonic sensors were used to measure the distance of surrounding obstacles. Both applications were designed to work concurrently so as to prevent potential collisions during crane lifting operations. The field testing and evaluation of the integrated system showed promising results.