Collision Avoidance Capability Research Articles

Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle

This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained.

Swarm of Drones in a Simulation Environment—Efficiency and Adaptation

In the swiftly advancing field of swarm robotics and unmanned aerial vehicles, precise and effective testing methods are essential. This article explores the crucial role of software-in-the-loop (SITL) simulations in developing, testing, and validating drone swarm control algorithms. Such simulations play a crucial role in reproducing real-world operational scenarios. Additionally, they can (regardless of the type of application) accelerate the development process, reduce operational risks, and ensure the consistent performance of drone swarms. Our study demonstrates that different geometrical arrangements of drone swarms require flexible control strategies. The leader-based control model facilitates coherent movement and enhanced coordination. Addressing various issues such as communication delays and inaccuracies in positioning is essential here. These shortcomings underscore the value of improved approaches to collision avoidance. The research described in this article focused on the dynamics of drone swarms in a simulated context and emphasized their operational efficiency and adaptability in various scenarios. Advanced simulation tools were utilized to analyze the interaction, communication, and adaptability of autonomous units. The presented results indicate that the arrangement of drones significantly affects their coordination and collision avoidance capabilities. They also underscore the importance of control systems that can adapt to various situations. The impact of communication delays and errors in positioning systems on the required distance between drones in a grid structure is also presented. This article assesses the impact of different levels of GPS accuracy and communication delays on the coordination of group movement and collision avoidance capabilities.

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.

TacSuit: A Wearable Large-Area, Bioinspired Multimodal Tactile Skin for Collaborative Robots

Robots are now working more and more closely with humans outside of traditional fences in industrial scenes. Their real-time tactile interaction perception is crucial to the safety of human–robot collaboration (HRC). In this work, we present a customized, wearable and modular robot skin (TacSuit), which is scalable for large-area surface coverage of robot with easily accessible multi-modal sensors, including pressure, proximity, acceleration and temperature sensors. The TacSuit is co-designed for mechanical structure and data fusion algorithm, consisting of three levels of design: sensor, cell (of multi-modal sensors) and block (of multiple cells). These sensors are stored with custom-designed and 3D printed capsules to achieve the conformity, scalability and easy installation to the arbitrary robot surface. A multi-level event-driven data fusion algorithm enables efficient information processing for large number of tactile sensors. Furthermore, a virtual interaction force fusion method takes both the proximity and force perception information into consideration in order to achieve safety of whole interaction process before and after direct physical contacts. A humanoid robotic platform successfully realizes the TacSuit wear of 159 tactile cells. Validation experiments of obstacle detection demonstrate the effective collision avoidance capability of the TacSuit for safe HRC.

Learning-Based Multi-UAV Flocking Control With Limited Visual Field and Instinctive Repulsion.

This article explores deep reinforcement learning (DRL) for the flocking control of unmanned aerial vehicle (UAV) swarms. The flocking control policy is trained using a centralized-learning-decentralized-execution (CTDE) paradigm, where a centralized critic network augmented with additional information about the entire UAV swarm is utilized to improve learning efficiency. Instead of learning inter-UAV collision avoidance capabilities, a repulsion function is encoded as an inner-UAV "instinct." In addition, the UAVs can obtain the states of other UAVs through onboard sensors in communication-denied environments, and the impact of varying visual fields on flocking control is analyzed. Through extensive simulations, it is shown that the proposed policy with the repulsion function and limited visual field has a success rate of 93.8% in training environments, 85.6% in environments with a high number of UAVs, 91.2% in environments with a high number of obstacles, and 82.2% in environments with dynamic obstacles. Furthermore, the results indicate that the proposed learning-based methods are more suitable than traditional methods in cluttered environments.

Design and Realization of a Low-Cost Smart Walking Aid for Visually Impaired and Blind People

We have developed an affordable and intelligent walking aid designed to provide visually impaired and blind individuals with alert-based support while navigating both outdoor streets and indoor environments. This innovative smart blind stick offers more than just collision avoidance capabilities; it boasts a range of advantages including cost-effectiveness, rapid responsiveness, minimal power consumption, lightweight design, and water detection features. The smart walking aid utilizes an ultrasonic sensor to detect obstacles in the user's path and promptly notifies them through a combination of buzzer alerts and mechanical vibrations, helping them avoid potential obstacles. Moreover, it can not only identify solid obstructions but can also alert the user to the presence of water using a dedicated water sensing mechanism. We rigorously evaluated the prototype's performance through extensive testing to optimize its accuracy in a variety of scenarios, revealing an impressive maximum obstacle detection range of approximately 2 meters on average.

Interaction between COLREG-compliant collision avoidance systems in a multiple MASS scenario

The transportation systems are heading towards increasing autonomy in all domains, and the maritime field makes no exception. The International Maritime Organization has been working on releasing a regulatory framework for Maritime Autonomous Surface Ships (MASS) to keep pace with the technological developments in the field. Autonomous shipping forces the researcher and the designers to face a wide range of scientific challenges, such as navigation decision support systems, collision avoidance algorithms, path planning, navigation and control, sensor data processing and fusion, remote control, and communication, with the final intent of achieving a fully integrated and autonomous worldwide maritime transportation system, where a human-less collaborative conflict resolution could potentially retire the COLREGs. However, the maritime sector will first face a transition period where traditional ships will share the seas and interact with heterogeneous MASS with various autonomous capabilities featuring different and probably incompatible communication protocols. In such a scenario, the COLREGs will still play a primary role in helping the collision avoidance systems resolve conflicts and limiting the degrees of freedom. This paper aims to study the interaction among multiple vessels with autonomous collision avoidance capabilities operating in a close navigation scenario. The ships operate according to a COLREG-compliant collision avoidance algorithm. The paper relies on numerical simulation to systematically investigate different scenarios in which autonomous vessels operate and interact in the presence of fixed obstacles. Results are presented and critically discussed.

Collision-Avoiding Model Predictive Rendezvous Strategy to Tumbling Launcher Stages

This paper considers the situation where a small satellite shall autonomously rendezvous with a tumbling object in a circular low Earth orbit (LEO) and derives a path-based model predictive controller that uses the docking point state and position of the chaser to guide it to a safe docking autonomously. The strategy embeds collision avoidance elements and reduces the computational effort for calculating the pulses to be provided by the thrusters through opportune algebraic manipulations, a Runge–Kutta 4 propagation method using linearized state transition matrices, and implicit embedding of dynamically equivalent thrust models, leading to constant state propagation matrices. Furthermore, the inputs design optimization problem and the embedded collision avoidance scheme are modeled and explicitly crafted as convex problems, contributing positively to low computational requirements. The docking and collision avoidance capabilities of the proposed scheme are extensively tested in an environment that accounts for all the perturbations relevant to LEO frameworks, for realistic thrust schemes, and for uncertainties in the measurement. Numerical results assess which tumbling objects can be docked or not by means of the proposed schemes as a function of the tumbling rates versus the thrust capabilities and hardware uncertainty of the docker.

Leonardo Drone Contest Autonomous Drone Competition: Overview, Results, and Lessons Learned from Politecnico di Milano Team

In this paper, the Politecnico di Milano solutions proposed for the Leonardo Drone Contest (LDC) are presented. The Leonardo Drone Contest is an annual autonomous drone competition among universities, which has already seen the conclusion of its second edition. In each edition, the participating teams were asked to design and build an autonomous multicopter, capable of accomplishing complex tasks in an indoor urban-like environment. To reach this goal, the designed systems should be capable of navigating in a Global Navigation Satellite System (GNSS)-denied environment with autonomous decision making, online planning and collision avoidance capabilities. In this light, the authors describe the first two editions of the competition, i.e., their rules, objectives and overview of the proposed solutions. While the first edition is presented as relevant for the experience and takeaways acquired from it, the second edition solution is analyzed in detail, providing both the simulation and experimental results obtained.

Closed-Loop Guidance for Asteroid Landing Using Stability-Related Control and Three-Dimensional Convex Curvature Constraints

For asteroid landing missions in uncertain environments, the thrust commands determined by a feedback control and a fuel-optimal landing trajectory may exceed the allowable thrust magnitude, which affects the stability of the controller. In contrast to the traditional method that designs the nominal trajectory and feedback control law separately, a novel closed-loop guidance method that generates an optimal trajectory while designing a stable feedback control law within the thrust limit is proposed. The method incorporates the feedback control design into the nominal trajectory optimization by shaping controlled trajectory bundles into a contractive invariant set centered on the nominal trajectory. To shape the trajectory bundles, the contractive invariant criterion is derived and augmented into the optimization with the feedback gain as an optimization variable. Then, the corresponding feedback control, expressed by the feedback gain and contractive invariant set, is introduced to form a stability-related control constraint, which keeps closed-loop thrust within the limit and ensures control stability. In addition, to improve the collision avoidance capability in uncertain terrains, a three-dimensional convex curvature constraint is proposed by constraining the geometric relationship between position and velocity vectors, and used as a soft constraint in the optimization. Finally, the proposed method is simulated using a landing mission on asteroid 433 Eros, which validates the superiority in the tracking performance and the collision avoidance capability.

Passive Electro-Optical Tracking of Resident Space Objects for Distributed Satellite Systems Autonomous Navigation

Autonomous navigation (AN) and manoeuvring are increasingly important in distributed satellite systems (DSS) in order to avoid potential collisions with space debris and other resident space objects (RSO). In order to accomplish collision avoidance manoeuvres, tracking and characterization of RSO is crucial. At present, RSO are tracked and catalogued using ground-based observations, but space-based space surveillance (SBSS) represents a valid alternative (or complementary asset) due to its ability to offer enhanced performances in terms of sensor resolution, tracking accuracy, and weather independence. This paper proposes a particle swarm optimization (PSO) algorithm for DSS AN and manoeuvring, specifically addressing RSO tracking and collision avoidance requirements as an integral part of the overall system design. More specifically, a DSS architecture employing hyperspectral sensors for Earth observation is considered, and passive electro-optical sensors are used, in conjunction with suitable mathematical algorithms, to accomplish autonomous RSO tracking and classification. Simulation case studies are performed to investigate the tracking and system collision avoidance capabilities in both space-based and ground-based tracking scenarios. Results corroborate the effectiveness of the proposed AN technique and highlight its potential to supplement either conventional (ground-based) or SBSS tracking methods.

Anisotropic GPMP2: A Fast Continuous-Time Gaussian Processes Based Motion Planner for Unmanned Surface Vehicles in Environments With Ocean Currents

In the past decade, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) for undertaking ocean missions in dynamic, complex maritime environments. The success of these missions largely relies on motion planning algorithms that can generate optimal navigational trajectories to guide a USV. Apart from minimising the distance of a path, when deployed a USVs’ motion planning algorithms also need to consider other constraints such as energy consumption, the affected of ocean currents as well as the fast collision avoidance capability. In this paper, we propose a new algorithm named anisotropic GPMP2 to revolutionise motion planning for USVs based upon the fundamentals of GP (Gaussian process) motion planning (GPMP, or its updated version GPMP2). Firstly, we integrated the anisotropy into GPMP2 to make the generated trajectories follow ocean currents where necessary to reduce energy consumption on resisting ocean currents. Secondly, to further improve the computational speed and trajectory quality, a dynamic fast GP interpolation is integrated in the algorithm. Finally, the new algorithm has been validated on a WAM-V 20 USV in a ROS environment to show the practicability of anisotropic GPMP2. Note to Practitioners—The work reported in this article will be significant for USVs to conduct missions in complex, dynamic maritime environments where various obstacles and time-varying ocean currents exit. We develop this novel motion planning algorithm based on Gaussian process and optimise the trajectory using probabilistic inferences. The new algorithm can generate collision free trajectories that also minimise the influences caused by adverse ocean currents in a highly efficient way. In addition, the planning has been undertaken in a continuous-time domain making the generated trajectory have a guaranteed smoothness and readily feasible for autopilots to track. We use a coastal area with time-varying vortexes to present a challenging practical maritime environment. The presented algorithm integrates the available information about a fluid field regarding energy consumption and hazard level, along with the density of obstacles to plan a navigational route efficiently. To increase the practical performance of the proposed method, diverse models for generating ocean currents need to be developed in the future to tackle unpredictable situations.

Multi-agent Q-Learning control of spacecraft formation flying reconfiguration trajectories

This paper presents a novel approach based on multi-agent reinforcement learning for spacecraft formation flying reconfiguration tracking problems. In this scheme, spacecrafts learn the control strategy via transfer learning. For this matter, a new generalized discounted value function is introduced for the tracking problems. Due to the digital nature of spacecraft computer systems, local optimal controllers are developed for the spacecrafts in discrete-time. The stability of the controller is proven. Two Q-learning algorithms are proposed, in each of which the optimal control solution is learned on-line without knowledge about the system dynamics. In the first algorithm, each agent learns the optimal control independently. In the second one, each agent shares the learned information with other agents. Next, the collision avoidance capability is provided. The effectiveness of the presented schemes is verified through simulations and compared with each other.

USV collision hazard assessment and track planning algorithm

To improve the autonomous collision avoidance capability of an unmanned surface vehicle (USV) under complex working conditions, this study proposes a local track planning algorithm for USVs based on collision hazard assessment. This algorithm uses hierarchical analysis and discrepancy method to determine the collision risk index (CRI) values of different encounter scenarios, and then introduces fuzzy inference theory to construct a quantitative collision hazard assessment model based on the principle of combining subjective and objective assessment and the pessimistic principle of class classification, which provides a decision basis for a collision avoidance plan. In response to the unexpected encounter, an improved vector field histogram algorithm is proposed to use the CRI value of the obstacle as the key parameter of the histogram, solve the restricted area by removing the grid model and threshold improvement, establish the cost function to calculate the optimal sailing interval under the rules and dynamics constraints of the Convention on International Regulations for Preventing Collisions at Sea (COLREGS), and realise the local target point by tracking local track planning during autonomous collision avoidance. Simulation and on-lake experiments demonstrate the effectiveness of the collision hazard assessment model and local track planning algorithm proposed in this paper.

Collision Avoidance Decision Method for Unmanned Surface Vehicle Based on an Improved Velocity Obstacle Algorithm

To ensure navigation safety, unmanned surface vehicles (USVs) need to have autonomous collision avoidance capability. A large number of studies on ship collision avoidance are available, and most of these papers assume that the target ships keep straight or follows the International Regulations for Preventing Collisions at Sea (COLREGS). However, in the actual navigation process, the target ship may temporarily turn. Based on the above reasons, this paper proposes a multi-ship collision avoidance decision method for USVs based on the improved velocity obstacle algorithm. In the basic dynamic ship domain model, a collision risk model is constructed to improve the accuracy of the risk assessment between the USV and target ships. The velocity obstacle algorithm is combined with the dynamic ship domain, and the collision avoidance timing and method are judged according to the collision risk. The simulation results show that the decision method can handle the situation that the target ship temporarily turns and has an emergency collision avoidance capability. Compared with the traditional VO algorithm, the collision avoidance time of the method is shorter, and the number of course changes is less.

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.

A Rudimentary Mission Planning System for Marine Autonomous Surface Ships

Maritime autonomous surface ships (MASS) have become a topic of intensive research in academia and industry, with focus areas such as path following and tracking, low-level control, collision avoidance, situational awareness, and others. However, high-level mission planning has received less attention in the literature, although it constitutes an important aspect of autonomy. This paper takes a step towards closing this gap by developing a basic mission planning system, which can generate a feasible and efficient sequence of high-level actions (plans) that are subsequently executed by the control system. The mission planner is based on the Graphplan algorithm, and considers actions related to, for example, entering docking mode, visiting a specific location, or starting (un)loading of containers, which are then to be executed by appropriate control systems. In addition, we couple the mission planner with the ship's guidance, navigation and control system, which has path planning, path following and fuzzy logic-based collision avoidance capabilities. To assess the performance of the proposed method, we present a set of simulations of a MASS navigating in a realistic marine environment including static obstacles and other ships.

Distributed Predictive Drone Swarms in Cluttered Environments

Recent works in aerial robotics show that the self-organized and cohesive flight of swarms can emerge from the exchange of purely local information between neighboring agents. However, most of the current swarm models are not capable of flight in densely cluttered environments. Predictive models have the potential to incorporate safe collision avoidance capabilities and give the agents the ability to anticipate and synchronize their trajectories in real-time. Here, we propose a distributed predictive swarm model that generates self-organized, safe, and cohesive trajectories by solving an optimization problem in real-time. In simulation, we show that our method is scalable to large numbers of agents and suitable for deployment in different environments, specifically a forest and a funnel-like environment. Furthermore, our results show that the agents are capable of collision-free flight with noisy sensor measurements for a noise level of up to 70% of the magnitude of the agent safety distance. Real-world experiments with a swarm of up to 16 quadrotors in an indoor artificial environment validate our method. Supplementary Materials can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://doi.org/10.5281/zenodo.5245214</uri> .

Comparison of Routing Protocols and Communication Interfaces for the Implementation of Collision Avoidance Capabilities in Fleets of Industrial Mobile Robots

In this paper a novel approach to a collision avoidance system between forklifts based on a wireless communication is proposed in order to test its performance and feasibility. First of all an overview on collision avoidance systems between vehicles inside warehouses is introduced with their main advantages and disadvantages. Then the reasons that led to the study of the new solution proposed in this article are motivated. Subsequently, a brief survey on mobile ad hoc networks and their main routing protocols that allow multi-hop communication is presented. For the implementation of the simulations OMNeT++ and SUMO software were used, which allowed to develop the model and the warehouse scenarios. Furthermore, we report the collision avoidance algorithm used in this study for the generation of a plausible network data load in order to collect and analyze simulation statistics. In particular, two applications have been developed, DiscoveryApp and AnticollisionApp, which are run by each node. Several simulations were then carried out, varying the size of the scenario, the communication interface standards and the routing protocols. In particular, communication interfaces based on the IEEE 802.15.4 and 802.11g standards and AODV, DSDV, GPSR routing protocols were tested. The last part of the paper concerns the analysis of the results based on the metrics of average end to end delay, overhead, average goodput and packet delivery ratio in order to determine the best combination of technologies for an application of this type.

An improved automated braking system for rear-end collisions: A study based on a driving simulator experiment

Introduction: To assist drivers in avoiding rear-end collisions, many early warning systems have been developed up to date. Autonomous braking technology is also used as the last defense to ensure driver’s safety. Method: By taking the accuracy and timeliness of automatic system control into account, this paper proposes a rear-end Real-Time Autonomous Emergency Braking (RTAEB) system. The system inserts brake intervention based on drivers’ real-time conflict identification and collision avoidance performance. A driving simulator-based experiment under different traffic conditions and deceleration scenarios were conducted to test the different thresholds to trigger intervention and the intervention outcomes. The system effectiveness is verified by four evaluation indexes, including collision avoidance rate, accuracy rate, sensitivity rate, and precision rate. Results: The results showed that the system could help avoid all collision events successfully and enlarge the final headway distance, and a TTC threshold of 1.5 s and a maximum deceleration threshold of −7.5 m/s2 could achieve the best collision avoidance effect. The paper demonstrates the situations that are more inclined to trigger the RTAEB (i.e., a sudden brake of the leading vehicle and a small car-following distance). Moreover, the study shows that driver characteristics (i.e., gender and profession) have no significant association with system trigger. Practical Applications: The study suggests that development of collision avoidance systems design should pay attention to both the real-time traffic situation and drivers’ collision avoidance capability under the present situation.