Abstract
A critical requirement for unmanned aerial vehicles (UAV) is the collision avoidance (CA) capability to meet safety and flexibility issues in an environment of increasing air traffic densities. This paper proposes two efficient algorithms: conflict detection (CD) algorithm and conflict resolution (CR) algorithm. These two algorithms are the key components of the cooperative multi-UAV CA system. The CD sub-module analyzes the spatial-temporal information of four dimensional (4D) trajectory to detect potential collisions. The CR sub-module calculates the minimum deviation of the planned trajectory by an objective function integrated with track adjustment, distance, and time costs, taking into account the vehicle performance, state and separation constraints. Additionally, we extend the CR sub-module with causal analysis to generate all possible solution states in order to select the optimal strategy for a multi-threat scenario, considering the potential interactions among neighboring UAVs with a global scope of a cluster. Quantitative simulation experiments are conducted to validate the feasibility and scalability of the proposed CA system, as well as to test its efficiency with variable parameters.
Highlights
The unmanned aerial vehicle (UAV) has received a wide range of urban, civilian and warfare applications [1]
There are several existing widely used or adequately evaluated collision avoidance (CA) systems for conventional aviation, and these can be applied on UAVs, such as the efficient Medium Term CA approach based on four-dimensional (4D) trajectories to resolve conflicts in a terminal maneuvering area (TMA) [2], the Traffic Alert and Collision Avoidance System (TCAS) equipped to issue advisories on how to maneuver vertically to prevent collisions [3], and the Airborne Separation Assurance System (ASAS) that enables the flight crew to maintain separation of an aircraft from one or more other aircraft and provides flight information concerning the surrounding traffic [4]
The conflict detection logic can be defined as follows: if the current relative ij distance of the two UAVs is smaller than the Rcf ( Rt < Rc f ), and they have a trend to be closer, ij i.e., when the value of Rt that is the relative distance between UAVi and nearby UAVj is getting smaller, the resolution maneuver fires regardless of whether the coming closest point of approach (CPA) [21] is in collision volume Rcl or not
Summary
The unmanned aerial vehicle (UAV) has received a wide range of urban, civilian and warfare applications [1] It offers several extraordinary features, especially the acceptance of long-endurance and high-risk missions that could not be reasonably performed by manned aircraft. Some of the studies only focus on the two-UAV scenarios (not multiple vehicles) Most of these methodologies could not support exploring the emergent dynamics between the generated trajectories and the surrounding traffic. This paper presents an innovative strategy, distributed dynamic optimization approach (DDOA), to provide alternative trajectories for each UAV, by adding some essential constraints (i.e., performance, cost and distance) to make the problem treatable while the feasible trajectory generation for multi-UAV is a non-deterministic polynomial (NP) problem.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
