Collision Avoidance of Two Autonomous Quadcopters

Robotics and Autonomous Systems, 2019

The ability to avoid collisions with each other is one of the fundamental requirements for autonomous unmanned aerial vehicles (UAVs) to be safely integrated into the civilian airspace, and for the viability of multi-UAV operations. This paper introduces a new approach for online cooperative collision avoidance between quadcopters, involving reciprocal maneuvers, i.e., coherent maneuvers without requiring any real-time consensus. Two maneuver strategies are presented, where UAVs respectively change their speed or heading to avoid a collision. A learning-based framework that trains these reciprocal actions for collision evasion (called TRACE) is developed. The primary elements of this framework include: 1) designing simulated experiments that cover a variety of UAV-UAV approach scenarios; 2) performing optimization to identify speed/heading change actions that satisfy safety constraints while minimizing the energy cost of the maneuver; and 3) using the offline optimization outcomes to train classifier (via ensemble bagged tree) and function approximation (via neural networks and Kriging) models for respectively selecting and encoding the avoidance actions. Trajectory generation and dynamics/controls are incorporated in the simulation environment used for training and testing. Over 90% accuracy in action prediction and over 95% success in avoiding collisions is observed when the trained models are applied to simulated unseen test scenarios.

This paper presents a distributed methodology to produce collision-free control laws for an Unmanned Aerial Vehicle (UAV) fleet. We use a game theoretic framework, where UAVs accommodate for individual and fleet goals, while respecting safety requirements. The method combines Control Barrier Functions (CBFs) and a primal-dual algorithm for Nash equilibrium (NE) seeking in generalized games. Feedback is introduced by Model Predictive Control (MPC) and we analyze its stability properties. The combination of these tools allows for a distributed, collision-free pointwise equilibrium solution, despite the agents' coupling, due to common target tracking and the collision avoidance constraints. Our algorithmic results are supported theoretically and our method's efficacy is demonstrated via extensive numerical simulations. Research support by MathWorks is gratefully acknowledged; the authors are particularly grateful to Dr Rory Adams and Dr Roberto Valenti for their useful insights.

IEEE Access

A collision avoidance method for multi-agent systems based on the centralization and decentralization effects for cooperative control is presented in this article. In this context, a matrix called Anti-Laplacian is proposed to control the agents when the UAVs are on a conflicting route. The matrix adjustment occurs through proportionality relations with the Laplacian Matrix from an interaction graph between the agents. The adjustment method aims a balance between centering and decentering to avoid collisions. Controlled quadcopters follow the trajectory indicated by virtual agents that act as guides for the real ones. The tests are performed via simulation for the most critical cases, with protocols involving flock centralization. As a virtual agent, a first-order model is used in the simulation, the method efficiency is observed by varying the number of agents involved. The proposed method presents allows different forms to control the collision avoidance parameter, such as deterministic and machine learning methods.

2022

Multi-UAV collision avoidance is a challenging task for UAV swarm applications due to the need of tight cooperation among swarm members for collision-free path planning. Centralized Training with Decentralized Execution (CTDE) in Multi-Agent Reinforcement Learning is a promising method for multi-UAV collision avoidance, in which the key challenge is to effectively learn decentralized policies that can maximize a global reward cooperatively. We propose a new multi-agent critic-actor learning scheme called MACA for UAV swarm collision avoidance. MACA uses a centralized critic to maximize the discounted global reward that considers both safety and energy efficiency, and an actor per UAV to find decentralized policies to avoid collisions. To solve the credit assignment problem in CTDE, we design a counterfactual baseline that marginalizes both an agent's state and action, enabling to evaluate the importance of an agent in the joint observation-action space. To train and evaluate MACA, we design our own simulation environment MACAEnv to closely mimic the realistic behaviors of a UAV swarm. Simulation results show that MACA achieves more than 16% higher average reward than two state-of-the-art MARL algorithms and reduces failure rate by 90% and response time by over 99% compared to a conventional UAV swarm collision avoidance algorithm in all test scenarios.

Proceedings of the 10th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2023), 2023

One of the significant aspects for enabling the intelligent behavior to the Unmanned Aerial Vehicles (UAVs) is by providing an algorithm for navigation through the dynamic and unseen environment. Therefore, to be autonomous, they need sensors to perceive their surroundings and utilize gathered information to decide which action to take. Having that in mind, in this paper, the authors designed the system for obstacle avoidance and also investigate the elements of the Markov decision process and their influence on each other. The flying mobile robot used within the considered problem is quadrotor type and has an integrated Lidar sensor which is utilized to detect obstacles. The sequential decision-making model based on Q-learning is trained within the MATLAB Simulink environment. The simulation results demonstrate that the UAV can navigate through the environment in most algorithm runs without colliding with surrounding obstacles.

2009

The ability to integrate unmanned and manned aircraft into airspace is a critical capability that will enable growth in wide varieties of applications. Collision avoidance is a key enabler for the integration of manned and unmanned missions in civil and military operation theaters. Large efforts have been done to address collision avoidance problem to both manned and unmanned aircraft. However, there has been little comparative discussion of the proposed approaches. This paper presents a survey of the collision avoidance approaches those deployed for aircraft, especially for unmanned aerial vehicles. The collision avoidance concept is introduced together with proposing generic functions carried by collision avoidance systems. The design factors of the sense and avoid system, which are used to categorize methods, are explained deeply. Based on the design factors, several typical approaches are categorized.

ACM Transactions on Cyber-Physical Systems, 2021

Urban Air Mobility, the scenario where hundreds of manned and Unmanned Aircraft Systems (UASs) carry out a wide variety of missions (e.g., moving humans and goods within the city), is gaining acceptance as a transportation solution of the future. One of the key requirements for this to happen is safely managing the air traffic in these urban airspaces. Due to the expected density of the airspace, this requires fast autonomous solutions that can be deployed online. We propose Learning-‘N-Flying (LNF), a multi-UAS Collision Avoidance (CA) framework. It is decentralized, works on the fly, and allows autonomous Unmanned Aircraft System (UAS)s managed by different operators to safely carry out complex missions, represented using Signal Temporal Logic, in a shared airspace. We initially formulate the problem of predictive collision avoidance for two UASs as a mixed-integer linear program, and show that it is intractable to solve online. Instead, we first develop Learning-to-Fly (L2F) by c...

International Journal of Computer and Electrical Engineering, 2017

Importance of Unmanned Combat Aerial Vehicles (UCAVs) in air combat has been increasing continuously. Dangerous nature of air combat, limits of human body, high cost of pilot training and combat readiness create a requirement for using (UCAV) in the battlefield. High speed, limited time for making decision and multivariable nature of the problem are the main challenges of the autonomous vehicle development problem. Another challenge is coordination of air combat when performed by multiple fighters. In this article, we propose a decision-making method for multi UCAV air combat. In our method, each UCAV chooses the best engagement for the advantage of team instead of its own advantage and provide a real time feedback for improving the engagement decision. An incomplete information zero sum game implemented to antagonistic team pair. And a reduction method is proposed for mixed Nash Equilibrium strategies when large number of agents is involved. Promising results have been obtained in multi-air engagement scenario, and our solution is based on game theory approaches.

Current Robotics Reports

Purpose of Review A lot of research into decentralised, state-based conflict detection and resolution, or detect and avoid algorithms has been executed. This paper explains the essential properties of state-based conflict detection and reviews the work in the context of applications for not only manned but also unmanned aerial vehicles, where this might be applied relatively soon. Recent Findings Lately, based on several reviews of a variety of published algorithms, a selection has been implemented and simulated in extremely high traffic densities for comparison. Summary The modified voltage potential has been surprisingly efficient, even compared with more complex algorithms or adaptations, as is apparent from looking at macroscopic metrics like domino effect, efficiency and safety. This indicates that to this date, it is so far the most suitable algorithm for the detect and avoid role for unmanned aerial vehicles in urban airspaces, or other areas where a high density is expected.

2007 International Conference on Integration of Knowledge Intensive Multi-Agent Systems, 2007

This contribution presents a distributed, multi-layer collision avoidance architecture supporting efficient utilization of air space shared by several autonomous aerial vehicles. Presented multi-layer architecture is based on deliberative deployment of several collision avoidance methods by the aircraft at the same time. Both cooperative and non-cooperative collision avoidance methods are presented in the paper. The robustness of the architecture is justified by means of experimental validation of multi-agent simulation.