Amir R. Rahmani

Multi-UAV Convoy Protection: An Optimal Approach to Path Planning and Coordination

In this paper, we study the problem of controlling a group of unmanned aerial vehicles (UAVs) to provide convoy protection to a group of ground vehicles. The UAVs are modeled as Dubins vehicles flying at a constant altitude with bounded turning radius. We first present time-optimal paths to provide convoy protection to stationary ground vehicles. Then, we propose a control strategy to provide convoy protection to ground vehicles moving in straight lines. The minimum number of UAVs required to provide perpetual convoy protection, in both cases, are derived.

Optimal multi-UAV convoy protection

In this paper, we study time-optimal trajectories for unmanned aerial vehicles (UAVs) to provide convoy protection to a group of stationary ground vehicles. The UAVs are modelled as Dubins vehicles flying at a constant altitude. Due to kinematic constraints of the UAVs, it is not possible for a single UAV to provide convoy protection indefinitely. In this paper, we derive time-optimal paths for a single UAV to provide continuous ground convoy protection for the longest possible time. Furthermore, this paper provides optimal trajectories for multiple UAVs to achieve uninterrupted convoy protection. The minimum number of UAVs required to achieve this task is determined.

Biologically inspired confinement of multi-robot systems

Confinement of a group of mobile robots is of significant interest to the multi-agent robotics community. We develop confinement strategies through simple biological models; in particular, we draw inspiration from the foraging techniques used by bottlenose dolphins to catch fish. For a multi-agent system, we achieve the following goals: 1) provide an algorithm for one group of agents to perpetually confine the other group; 2) characterise the regions from which the herded agents are guaranteed to be captured. The simplicity of the model allows easy implementation in engineered devices (e.g., exploiting the collision avoidance modules already embedded in unmanned air and ground vehicles) and the richness of the model allows replication of a complex biological phenomenon, such as capturing of prey.

Geometric foraging strategies in multi-agent systems based on biological models

In nature, communal hunting is often performed by predators by charging through an aggregation of prey. However, it has been noticed that variations exist in the geometric shape of the charging front; in addition, distinct differences arise between the shapes depending on the particulars of the feeding strategy. For example, each member of a dolphin foraging group must contribute to the hunt and will only be able to eat what it catches. On the other hand, some lions earn a “free lunch” by feigning help and later feasting on the prey caught by the more skilled hunters in the foraging group. We model the charging front of the predators as a curve moving through a prey density modeled as a reaction-diffusion process and we optimize the shape of the charging front in both the free lunch and no-free-lunch cases. These different situations are simulated under a number of varied types of predator-prey interaction models, and connections are made to multi-agent robot systems.

A Hybrid, Multi-Agent Model of Foraging Bottlenose Dolphins

Abstract Social behavior of animals can offer solution models for missions involving a large number of heterogeneous vehicles, such as light combat ships, unmanned aerial vehicles, and unmanned underwater vehicles. We draw inspiration from the foraging techniques of bottlenose dolphins to address the problem of heterogeneous multi-agent herding. We produce a hybrid automaton model of the entire foraging method - search, detect, and capture - where agents are modeled as first-order systems in which interactions are defined through spatial proximity. Finally, simulations are provided to illustrate that our model is expressive enough to capture this complex biological phenomenon.

Merging and Spacing of Heterogeneous Aircraft in Support of NextGen

The Federal Aviation Administration’s NextGen program aims to increase the capacity of the national airspace, while ensuring the safety of aircraft. This paper provides a distributed merging and spacing algorithm that maximizes the throughput at the terminal phase of flight, using information communicated between neighboring aircraft through the ADS-B framework. Aircraft belonging to a mixed fleet negotiate with each other and use dual decomposition to reach an agreement on optimal merging times, with respect to a pairwise cost, while ensuring proper interaircraft spacing for the respective aircraft types. A set of sufficient conditions on the geometry and operating conditions of merging forks is provided to identify when proper interaircraft spacing can always be achieved using the proposed algorithm for any combination of merging aircraft. Also, optimal decentralized controllers are derived for merging air traffic when operating under such conditions. The performance of the presented algorithm is verified through computer simulations.

Duty cycle scheduling in dynamic sensor networks for controlling event detection probabilities

A sensor network comprising of RF or radar-based sensors has a deteriorating performance in that the effective sensor footprint shrinks as the power level decreases. Power is typically only drawn from the sensor nodes when they are turned on, and as a consequence, the power consumption can be controlled by controlling the duty cycle of the sensors. In this paper, we provide a probabilistic scheduling of the duty cycles in a sensor network deployed in an area of interest based on a Poisson distribution which ensures that a performance measure, e.g., the probability of event detection, is achieved throughout the lifetime of the network. Upper bounds on the performance of the network are given in terms of the decay rates, the spatial distribution intensity, and the desired performance of the network.

Air traffic maximization for the terminal phase of flight under FAA's NextGen framework

The NextGen program is the FAAs response to the ever increasing air traffic, that provides tools to increase the capacity of national airspace, while ensuring the safety of aircraft. In support of this vision, this paper provides a decentralized algorithm based on dual decomposition for safe merging and spacing of aircraft at the terminal phase of the flight. Aircraft negotiate optimal merging times that ensure safety, while penalizing deviations from the nominal path. We provide feasibility conditions for the safe merging of all incoming legs of flight and put the viability of the proposed algorithm to the test through simulations.

Biologically motivated shape optimization of foraging fronts

Social animals often form a predator front to charge through an aggregation of prey. It is observed that the nature of the feeding strategy dictates the geometric shape of these charging fronts. Inspired by this observation, we model foraging multi-robot fronts as a curve moving through a prey density. We optimize the shape of the curve using variational arguments and simulate the results to illustrate the operation of the proposed curve optimization algorithm.

A Hardware Testbed for Multi-UAV Collaborative Ground Convoy Protection in Dynamic Environments

In this paper, we will present a hardware testbed for multi-UAV systems that bridges the gap between algorithm design and field deployment. The testbed allows for UAV coordination algorithms, that have been shown to work in simulation, to be further tested in an environment where limited on-board computational resources, wireless communication constraints, environmental noise, and differences in the UAVs modeled versus actual dynamics come into effect. In particular, we will introduce an efficient assignment algorithm. This algorithm is used in a multi-UAV ground convoy protection scenario, where UAVs escort the ground convoy and are deployed to check potential threats along the way.