Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFairFly: A Fair Motion Planner for Fleets of Autonomous UAVs in Urban Airspace
262
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We present a solution to the problem of fairly planning a fleet of Unmanned Aerial Vehicles (UAVs) that have different missions and operators, such that no one operator unfairly gets to finish its missions early at the expense of others - unless this was explicitly negotiated. When hundreds of UAVs share an urban airspace, the relevant authorities should allocate corridors to them such that they complete their missions, but no one vehicle is accidentally given an exceptionally fast path at the expense of another, which is thus forced to wait and waste energy. Our solution, FairFly, addresses the fair planning question for general autonomous systems, including UAV fleets, subject to complex missions typical of urban applications. FairFly formalizes each mission in temporal logic. An offline search finds the fairest paths that satisfy the missions and can be flown by the UAVs, leading to lighter online control load. It allows explicit negotiation between UAVs to enable imbalanced path durations if desired. We present three fairness notions, including one that reduces energy consumption. We validate our results in simulation, and demonstrate a lighter computational load and less UAV energy consumption as a result of flying fair trajectories.
rate research
Read More
Urban air mobility (UAM) has attracted the attention of aircraft manufacturers, air navigation service providers and governments in recent years. Preventing the conflict among urban aircraft is crucial to UAM traffic safety, which is a key in enabling large scale UAM operation. Pre-flight conflict-free path planning can provide a strategic layer in the maintenance of safety performance, thus becomes an important element in UAM. This paper aims at tackling conflict-free path planning problem for UAM operation with a consideration of four-dimensional airspace management. In the first place, we introduced and extended a four-dimensional airspace management concept, AirMatrix. On the basis of AirMatrix, we formulated the shortest flight time path planning problem considering resolution of conflicts with both static and dynamic obstacles. A Conflict-Free A-Star algorithm was developed for planning four-dimensional paths based on first-come-first-served scheme. The algorithm contains a novel design of heuristic function as well as a conflict detection and resolution strategy. Numerical experiment was carried out in Jurong East area in Singapore, and the results show that the algorithm can generate paths resolving a significant number of potential conflicts in airspace utilization, with acceptable computational time and flight delay. The contributions of this study provide references for stakeholders to support the development of UAM.
We propose a novel receding horizon planner for an autonomous surface vehicle (ASV) performing path planning in urban waterways. Feasible paths are found by repeatedly generating and searching a graph reflecting the obstacles observed in the sensor field-of-view. We also propose a novel method for multi-objective motion planning over the graph by leveraging the paradigm of lexicographic optimization and applying it to graph search within our receding horizon planner. The competing resources of interest are penalized hierarchically during the search. Higher-ranked resources cause a robot to incur non-negative costs over the paths traveled, which are occasionally zero-valued. The framework is intended to capture problems in which a robot must manage resources such as risk of collision. This leaves freedom for tie-breaking with respect to lower-priority resources; at the bottom of the hierarchy is a strictly positive quantity consumed by the robot, such as distance traveled, energy expended or time elapsed. We conduct experiments in both simulated and real-world environments to validate the proposed planner and demonstrate its capability for enabling ASV navigation in complex environments.
A total 19% of generation capacity in California is offered by PV units and over some months, more than 10% of this energy is curtailed. In this research, a novel approach to reduce renewable generation curtailments and increasing system flexibility by means of electric vehicles charging coordination is represented. The presented problem is a sequential decision making process, and is solved by fitted Q-iteration algorithm which unlike other reinforcement learning methods, needs fewer episodes of learning. Three case studies are presented to validate the effectiveness of the proposed approach. These cases include aggregator load following, ramp service and utilization of non-deterministic PV generation. The results suggest that through this framework, EVs successfully learn how to adjust their charging schedule in stochastic scenarios where their trip times, as well as solar power generation are unknown beforehand.
This paper presents a novel solution technique for scheduling multi-energy system (MES) in a commercial urban building to perform price-based demand response and reduce energy costs. The MES scheduling problem is formulated as a mixed integer nonlinear program (MINLP), a non-convex NPhard problem with uncertainties due to renewable generation and demand. A model predictive control approach is used to handle the uncertainties and price variations. This in-turn requires solving a time-coupled multi-time step MINLP during each time-epoch which is computationally intensive. This investigation proposes an approach called the Scenario-Based Branch-and-Bound (SB3), a light-weight solver to reduce the computational complexity. It combines the simplicity of convex programs with the ability of meta-heuristic techniques to handle complex nonlinear problems. The performance of the SB3 solver is validated in the Cleantech building, Singapore and the results demonstrate that the proposed algorithm reduces energy cost by about 17.26% and 22.46% as against solving a multi-time step heuristic optimization model.
This paper studies the collision avoidance problem for autonomous multiple fixedwing UAVs in the complex integrated airspace. By studying and combining the online path planning method, the distributed model predictive control algorithm, and the geometric reactive control approach, a three-layered collision avoidance system integrating conflict detection and resolution procedures is developed for multiple fixed-wing UAVs modeled by unicycle kinematics subject to input constraints. The effectiveness of the proposed methodology is evaluated and validated via test results of comparative simulations under both deterministic and probabilistic sensing conditions.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
