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On the surface of complex sea areas with obstacles, the encirclement mission against one hostile unmanned surface vehicle (USV) with high capability is usually extremely challenging. Especially when the evasion USV is endowed with a higher speed, the pursuit USVs are more likely to fail. In this work, the containment strategies by multiple low-speed pursuit USVs to capture one high-speed evasion USV considering collision avoidance are studied. On the basis of the conventional artificial potential field (APF) method, we first design a virtual target point to replace the actual evasion USV, which will help the pursuit USVs better implement the surrounding behavior. Thereafter, we reset the triggering conditions of the APF method to avoid the unnecessary obstacle avoidance actions. The proposed strategies can help the pursuit USVs better accomplish the containment strategies while bypassing the obstacles. Simulation experiments have verified the effectiveness of the designed cooperative containment strategies.

In urban environments, the path planning (PP) of unmanned aerial vehicles (UAVs) presents significant challenges, particularly since they are tasked with executing various operations in crowded areas. This scenario can be framed as a Multiple Traveling Salesman Problem (MTSP), where multiple drones must efficiently visit a set of target locations while ensuring safety and collision avoidance. The high density of obstacles, such as buildings, trees, and other aerial vehicles, increases the risk of collisions, making effective PP essential for operational safety. This paper proposes a two-stage PP approach to address these challenges. In the first stage, we introduce an improved Particle Swarm Optimization (PSO) algorithm for task allocation (TA), assigning each UAV a unique task queue to minimize the overall flight distance while ensuring efficient coverage of the target area. In the second stage, we employ a multi-agent reinforcement learning algorithm for PP and incorporate a safe action correction module that operates independently to adjust actions, thereby enhancing collision avoidance capabilities. Experimental results demonstrate that our approach reduces the probability of collisions with obstacles by 9% compared to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm while also increasing the success rate of drone mission execution by 10%. This validates the effectiveness of our strategies for safe and efficient multi-drone operations in urban environments.

Discussions surrounding Maritime Autonomous Surface Ships (MASS) are occurring in academia, industry, and the International Maritime Organization (IMO). Automatic collision avoidance, as one of the key technologies of MASS, is the core of the autonomous navigation function of MASS, and its role is to solve the autonomous collision avoidance problem during MASS navigation. Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is a fundamental basis for automatic collision avoidance, and the navigation of MASS should adhere to it. However, the applicability of MASS to COLREGS has not yet been resolved. Responding to these issues, the paper proceeds to analyze the principal challenges of MASS to COLREGS in terms of the application of MASS to good seamanship, the neglect provision, the lookout provision, the insight of one another provision, and the problems of the deviation provision. Furthermore, suggestions are put forth for the revision of COLREGS, including the mode of revision, the reconstruction of MASS collision liability, the long-term coexistence of MASS and traditional ships, and the risk control of COLREGS revision in the context of artificial intelligence. These suggestions aim to establish a foundation for a more effective adaptation to the advent of the MASS era.

The robot path planning problem involves planning optimal paths for a robot to follow while ensuring it will not hit any obstacles or itself. In a state or perfectly known world, this has been addressed using the configuration space representation and the A* search algorithm. However, when movement, changes, or unexpected obstacles occur in the environment, a new method, Differential A*, can adapt the solution to the current situation. It updates only the fraction of space that is critically affected. This technique can provide significant speed improvements, with the same desired results, compared to complete space regeneration.

Chaotic nonlinear networks are investigated, which are controlled by simple boids rules. They exhibit complex and emergent behaviors such as flocking behavior, separation behavior, joining behavior and obstacle avoiding behavior.

In the present era, Underwater Wireless Sensor Network (UWSN) is an emerging technology that involves a huge amount of sensor nodes to collect and monitor information from the underwater environment. However, the data transmission process is constrained due to the collision and energy consumption which can adversely affect the performance. Hence, there is an essential need to develop a suitable mechanism that addresses these challenges using a data aggregation-based routing mechanism in UWSN. In this paper, a Multi-Slot Scheduling with a Two-Layer Hexagonal based Integrated Aggregation model (MSS-TLHIA) is proposed that offers a prolonged lifetime with less energy consumption and collision avoidance. In this model, data aggregation is performed using the aggregator node selection process. Initially, the entire network is partitioned into several hexagonal grids using the golden ratio. This partitioning offers an improved coverage area for every node which are participating in the network. Once the network is partitioned into coverage areas called clusters, a Cluster Head (CH) is selected using the ranking-based fuzzy mechanism. Then, an aggregator node is selected in common for both the layers of the hexagonal grids. In order to prevent the energy drain of the aggregator node completely and to prolong their lifetime, the aggregator node is re-selected for every time slot. Furthermore, the occurrence of collision is avoided by the multi-slot scheduling process. Experimental results demonstrate that the performance of the proposed model is compared with other existing protocols and achieves better results in terms of network lifetime, energy consumption, collision rate, packet dropped rate, packet delivery, and data forwarding measures.

We propose a self collision avoidance system for humanoid robots designed for interacting with the real world. It protects not only the humanoid robots' hardware but also expands its working range while keeping smooth motions. It runs in real-time in order to handle unpredictable reactive tasks such as reaching to moving targets tracked by vision during dynamic motions like e.g. biped walking.

The collision avoidance is composed of two important elements. The first element is reactive self collision avoidance which controls critical segments in only one direction — as opposed to other methods which use 3D position control. The virtual force for the collision avoidance is applied to this direction and therefore the system has more redundant degrees of freedom which can be used for other criteria. The other second element is a dynamic task prioritization scheme which blends the priority between target reaching and collision avoidance motions in a simple way. The priority between the two controllers is changed depending on current risk.

We test the algorithm on our humanoid robot ASIMO and works while the robot is standing and walking. Reaching motions from the front to the side of the body without the arm colliding with the body are possible. Even if the target is inside the body, the arm stops at the closest point to the target outside the body. The collision avoidance is working as one module of a hierarchical reactive system and realizes reactive motions. The proposed scheme can be used for other applications: We also apply it to realizing a body schema and occlusion avoidance.

Warning systems have been proposed to reduce driver cognitive and judgment load as driver systems for traffic safety. The system's efficacy could be decreased if the driver feels annoyance and/or mistrust with inappropriate warning timing, etc. This paper explores the possibility of personalized warning timing to cope with this problem. First, we propose a new warning method for rear-side obstacles using a driver's perceptual risk model that we derived from the analysis of the driver's deceleration behavior. Validity of the warning method will be shown by driving simulator experiments. In addition, there exists individual difference in expectation of meaning of warning. Thus, differences of the efficacy and driver's response behaviors against warning will be analyzed based on the difference of their expectation.

A dynamic inversion-based three-dimensional nonlinear aiming point guidance law is presented in this paper for reactive collision avoidance of unmanned aerial vehicles. When an obstacle is detected in the close vicinity and collision is predicted, an artificial safety sphere is put around the center of the obstacle. Next, the velocity vector of the vehicle is realigned towards an 'aiming point' on the surface of the sphere in such a way that passing through it can guarantee safe avoidance of the obstacle. The guidance command generation is based on angular correction between the actual and the desired direction of the velocity vector. Note that the velocity vector gets aligned along the selected aiming point quickly (i.e., within a fraction of the available time-to-go), which makes it possible to avoid pop-up obstacles. The guidance algorithm has been verified with simulations carried out both for single obstacles as well as for multiple obstacles on the path and also with different safety sphere sizes around the obstacles. The proposed algorithm has been validated using both kinematic as well as point mass model of a prototype unmanned aerial vehicle. For better confidence, results have also been validated by incorporating a first-order autopilot models for the velocity vector magnitude and directions.

In this study, applicability of verification and correct-by-design hybrid systems modeling and reachability-based controllers for vehicular automation are investigated. Two perspectives in hybrid systems modeling will be introduced, and then reachability analysis techniques will be developed to compute exact reachable sets from a specified unsafe set. Using level set methods, a Hamilton–Jacobi–Isaacs equation is derived whose solutions describe the boundaries of the finite time backward reachable set, which will be manipulated to design a safe controller that guarantees the safety of a given system. An automated longitudinal controller with a fully integrated collision avoidance functionality will be designed as a hybrid system and validated through simulations with a number of different scenarios in order to illustrate the potential of verification methods in automated vehicles.

An effective reactive collision avoidance algorithm is presented in this paper for unmanned aerial vehicles (UAVs) using two simple inexpensive pinhole cameras. The vision sensed data, which consists of the azimuth and elevation angles at the two camera positions, is first processed through a Kalman filter formulation to estimate the position and velocity of the obstacle. Once the obstacle position is estimated, the collision cone philosophy is used to predict the collision over a short period of time. In case a collision is predicted, steering guidance commands are issued to the vehicle to steer its velocity vector away using the nonlinear differential geometric guidance. A new cubic spline based post-avoidance merging algorithm is also presented so that the vehicle rejoins the intended global path quickly in a smooth manner after avoiding the obstacle. The overall algorithm has been validated using the point mass model of a prototype UAV with first-order autopilot delay. Both extended Kalman filtering (EKF) and unscented Kalman filtering (UKF) have been experimented. Both are found to be quite effective. However, performance of UKF was found to be better than EKF with minor compromise in computational efficiency and hence it can be a better choice. Note that because of two cameras, stereovision signature gets associated with optical flow signature thereby making the overall signature quite strong for obstacle position estimation. This leads to a good amount of success as compared to the usage of a single pinhole camera, results of which has been published earlier.

This paper presents a leader-follower type of fault-tolerant formation control (FTFC) methodology with application to multiple unmanned aerial vehicles (UAVs) in the presence of actuator failures and potential collisions. The proposed FTFC scheme consists of both outer-loop and inner-loop controllers. First, a leader-follower control scheme with integration of a collision avoidance mechanism is designed as the outer-loop controller for guaranteeing UAVs to keep the desired formation while avoiding the approaching obstacles. Then, an active fault-tolerant control (FTC) strategy for counteracting the actuator failures and also for preventing the healthy actuators from saturation is synthesized as the inner-loop controller. Finally, a group of numerical simulations are carried out to verify the effectiveness of the proposed approach.

Detecting collision-course targets in aerial scenes from purely passive optical images is challenging for a vision-based sense-and-avoid (SAA) system. Proposed herein is a processing pipeline for detecting and evaluating collision course targets from airborne imagery using machine vision techniques. The evaluation of eight feature detectors and three spatio-temporal visual cues is presented. Performance metrics for comparing feature detectors include the percentage of detected targets (PDT), percentage of false positives (POT) and the range at earliest detection ($R_{\mathrm{\text{det}}}$). Contrast and motion-based visual cues are evaluated against standard models and expected spatio-temporal behavior. The analysis is conducted on a multi-year database of captured imagery from actual airborne collision course flights flown at the National Research Council of Canada. Datasets from two different intruder aircraft, a Bell 206 rotor-craft and a Harvard Mark IV trainer fixed-wing aircraft, were compared for accuracy and robustness. Results indicate that the features from accelerated segment test (FAST) feature detector shows the most promise as it maximizes the range at earliest detection and minimizes false positives. Temporal trends from visual cues analyzed on the same datasets are indicative of collision-course behavior. Robustness of the cues was established across collision geometry, intruder aircraft types, illumination conditions, seasonal environmental variations and scene clutter.

Autonomous unmanned vehicles are preferable in patrolling, surveillance and, search and rescue missions. Multi-agent architectures are commonly used for autonomous control of unmanned vehicles. Existing multi-robot architectures for unmanned aerial and ground robots are generally mission and platform oriented. Collision avoidance, path-planning and tracking are some of the fundamental requirements for autonomous operation of unmanned robots. Though aerial and ground vehicles operate differently, the algorithms for obstacle avoidance, path-planning and path-tracking can be generalized. Service Oriented Interoperable Framework for Robot Autonomy (SOIFRA) proposed in this work is an interoperable multi-agent framework focused on generalizing platform independent algorithms for unmanned aerial and ground vehicles. SOIFRA is behavior-based, modular and interoperable across unmanned aerial and ground vehicles. SOIFRA provides collision avoidance, and, path-planning and tracking behaviors for unmanned aerial and ground vehicles. Vector Directed Path-Generation and Tracking (VDPGT), a vector-based algorithm for real-time path-generation and tracking, is proposed in this work. VDPGT dynamically adopts the shortest path to the destination while minimizing the tracking error. Collision avoidance is performed utilizing Hough transform, Canny contour, Lucas–Kanade sparse optical flow algorithm and expansion of object-based time-to-contact estimation. Simulation and experimental results from Turtlebot and AR Drone show that VDPGT can dynamically generate and track paths, and SOIFRA is interoperable across multiple robotic platforms.

This paper presents a critical analysis of some of the most promising approaches to geometric collision avoidance in multi-agent systems, namely, the velocity obstacle (VO), reciprocal velocity obstacle (RVO), hybrid-reciprocal velocity obstacle (HRVO) and optimal reciprocal collision avoidance (ORCA) approaches. Each approach is evaluated with respect to increasing agent populations and variable sensing assumptions. In implementing the localized avoidance problem, the author notes a problem of symmetry not considered in the literature. An intensive 1000-cycle Monte Carlo analysis is used to assess the performance of the selected algorithms in the presented conditions. The ORCA method is shown to yield the most scalable computation times and collision likelihood in the presented cases. The HRVO method is shown to be superior than the other methods in dealing with obstacle trajectory uncertainty for the purposes of collision avoidance. The respective features and limitations of each algorithm are discussed and presented through examples.

This paper proposes a low complexity distributed multi-agent coordination algorithm for agents to reach their target positions in dense traffic under limited communication. Each single-integrator agent is limited to communicating with only one other agent at a time in consideration of limited bandwidth. We adapt the Velocity Obstacle collision avoidance method from literature to the limited communication problem by incorporating Voronoi Cells and repulsion in our hybrid algorithm. We also introduce a priority system for distributed coordination to avoid deadlocks and livelocks by having agent pairs make mutual decisions based on each agent’s conditional priority. An event trigger-based communication protocol is designed to determine when and to whom to communicate. Our method’s effectiveness is demonstrated in simulations including 100 randomized scenarios of 50 agents. The simulations show that our proposed algorithm enables agents to reach their assigned target positions without deadlock and collision while requiring an average communication rate that is significantly lower than the control frequency.

Multi-UAV system is an important part of unmanned system, which plays an indispensable role in military field and civil agriculture. First, task assignment model including complex constraints is established and Discrete Pigeon-Inspired Optimization-Simulated Annealing algorithm (DPIO-SA) is proposed to solve it, which updates the speed and position of pigeons through exchange and cross operations. Then, Genetic Algorithm (GA) is adopted to optimize the UAVs’ locations in formation. The experimental results show that the average fitness value of DPIO-SA is 13.5% higher than DPIO; After running the algorithm for 30 times, the number of times that DPIO-SA algorithm reaches the global optimum is 15, while DPIO is 2. Both mean DPIO-SA is easier to jump out of local extremum. To describe the fixed-wing UAV, the Unicycle model is adopted. PID control is used to control the fixed-wing’s heading and speed. Aiming at the collision avoidance, Optimal Reciprocal Collision Avoidance (ORCA) algorithm is proposed, which allows fixed-wings to avoid collisions without having to communicate with each other. In the algorithm, the velocity region is divided by the definition of velocity obstacle, and the optimal velocity is obtained by linear planning algorithm. This enables the fixed-wings’ formation to find the right velocity in real time and effectively to avoid collision. Experiments show that 24 fixed-wings completed the formation assembly after running 16.4 s. Finally, according to the Contract Network Algorithm (CNA), the task scheduling problem is solved by the interaction among fixed-wings.

Safety and efficiency are primary goals of air traffic management. With the integration of unmanned aerial vehicles (UAVs) into the airspace, UAV traffic management (UTM) has attracted significant interest in the research community to maintain the capacity of three-dimensional (3D) airspace, provide information, and avoid collisions. We propose a new decision-making architecture for UAVs to avoid collision by formulating the problem into a multi-agent game in a 3D airspace. In the proposed game-theoretic approach, the Ego UAV plays a repeated two-player normal-form game, and the payoff functions are designed to capture both the safety and efficiency of feasible actions. An optimal decision in the form of Nash equilibrium (NE) is obtained. Simulation studies are conducted to demonstrate the performance of the proposed game-theoretic collision avoidance approach in several representative multi-UAV scenarios.

In this paper, we propose a cooperating motion generation method for man-machine cooperation systems in which the machines are controlled based on the intentional force applied by a human/humans. By applying this method, the systems could avoid self-collisions, collisions with obstacles and other dangerous situations during the tasks. As the application examples of proposed method, we focused on robots cooperating with a human/humans and surgery robot tools from the aspect of medical and welfare field. We did the experiments using human-friendly robot, referred to as "MR Helper", for illustrating the validity of the proposed method. We also did the computer simulation to indicate the prospects of applications of our self-collision avoidance method to surgery robot tools.

This paper presents an improvement for the software implementation (MOFS) of a user adaptive fuzzy control system for autonomous navigation of mobile robots in unknown environments. This improvement consists of a priority areas definition where the environment is measured by a PLS laser sensor, in order to get a reduction in the number of fuzzy rules and also in the computational cost, and hence obtaining improvements in the trajectory. This system has been tested in a pioneer mobile robot and on a robotic wheelchair, odometry sensors are used to localize the robots and the goal positions. The system is able to drive the robots to their goal position avoiding static and dynamic obstacles, without using any pre-built map. This approach improves the way to measure the danger of the obstacles, the way to follow the walls of corridors and the detection of doors. These improvements reduce the zigzag effect of the previous system by making the trajectories significantly straighter and hence reducing the time to reach the goal position.