Esten Ingar Grøtli

Path Planning for UAVs Under Communication Constraints Using SPLAT! and MILP

We will in this paper address the problem of offline path planning for Unmanned Aerial Vehicles (UAVs). Our goal is to find paths that meet mission objectives, are safe with respect to collision and grounding, fuel efficient and satisfy criteria for communication. Due to the many nonconvex constraints of the problem, Mixed Integer Linear Programming (MILP) will be used in finding the path. Approximate communication constraints and terrain avoidance constraints are used in the MILP formulation. To achieve more accurate prediction of the ability to communicate, the path is then analyzed in the radio propagation toolbox SPLAT!, and if the UAVs are not able to communicate according to design criteria for bandwidth, constraints are modified in the optimization problem in an iterative manner. The approach is exemplified with the following setup: The path of two UAVs are planned so they can serve as relay nodes between a target without line of sight to the base station.

Optimization of Wireless Sensor Network and UAV Data Acquisition

This paper deals with selection of sensor network communication topology and the use of Unmanned Aerial Vehicles (UAVs) for data gathering. The topology consists of a set of cluster heads that communicate with the UAV. In conventional wireless sensor networks Low Energy Adaptive Clustering Hierarchy (LEACH) is commonly used to select cluster heads in order to conserve energy. Energy conservation is far more challenging for large scale deployments. Particle Swarm Optimization (PSO) is proposed as an optimization method to find the optimal topology in order to reduce the energy consumption, Bit Error Rate (BER), and UAV travel time. PSO is compared to LEACH using a simulation case and the results show that PSO outperforms LEACH in terms of energy consumption and BER, while the UAV travel time is similar. The numerical results further illustrate that the performance gap between them increases with the number of cluster head nodes. Because of reduced energy consumption, network life time can be significantly extended while increasing the amount of data received from the entire network. By considering the wind effect into the PSO scheme, it is shown that this has an impact on the traveling time for the UAV but BER and energy consumption are not significantly increased.

Performance evaluation of cooperative relay and Particle Swarm Optimization path planning for UAV and wireless sensor network

Energy efficiency is crucial when wireless nodes are deployed in a remote or isolated area. This paper provides an optimal solution for data gathering from a wide area Wireless Sensor Network (WSN) with the use of Unmanned Aerial Vehicles (UAVs). Particle Swarm Optimization (PSO) is proposed as an optimization method to find the waypoints for a UAV in order to reduce the energy consumption and bit error rate (BER) of the sensor nodes, and the UAV travel time. In our previous work, the sensor nodes were required to transmit data to a Cluster Head (CH) node, which then forwarded the data to the UAV. The waypoints were restricted to be straight above the CH. In this work we employ cooperative relay, to make the data gathering more efficient. In addition, the waypoints of the UAV can be selected freely. To illustrate the effectiveness, we compare our new strategy to the one of our previous paper. Numerical results illustrate that the performance gap between them increases with the number of waypoints, in favor of the new strategy. Furthermore, the optimal number of waypoints for the UAV is also described, based on the size of the sensor network area and the density of the sensor node distribution. These contributions have maximized the network lifetime and communication quality, while minimized the UAVs flying time.

UAVs Trajectory Planning by Distributed MPC under Radio Communication Path Loss Constraints

In this paper, a distributed linear MPC approach to solve the trajectory planning problem for rotary-wing UAVs is proposed, where the objective of the UAV system is to form a communication network to multiple targets with given radio communication capacities. The approach explicitly incorporates constraints on radio communication path losses, computed by using SPLAT! that is able to take into account terrain models and antenna locations. In order to enhance the online optimization, at each time sample the terrain below each UAV and the communication path losses are approximated with linear functions of the spatial coordinates. This leads to linear MPC sub-problems, which are solved by using convex quadratic programming. An algorithm for automatic initialization and optimal reconfiguration of the communication topology in case of failures or severe radio path loss due to e.g. channel fading, is proposed. The communication network that is provided by the UAVs is considered to be a payload communication capacity that is normally independent of the command and control datalink used to control the UAVs. The performance of the distributed linear MPC trajectory planning and the reconfiguration algorithm is studied on two simulation cases with four UAVs and two targets.

Hybrid attitude tracking of rigid bodies without angular velocity measurement

In this paper we address the problem of output-feedback attitude control of a rigid body in quaternion-coordinate space through a hybrid (switching) PD+ based tracking controller; we establish stability for all initial values in a compact subset which may be arbitrarily enlarged by increasing the control gains. Assumptions used in the literature such as supposing that the initial states lay in a determined compact set or that the attitude error norm is smaller than π rad for all time, are removed by including a switching law. Simulation results are presented to corroborate our theoretical findings, showing that the system stabilises as expected, even when the initial estimated velocity error is large.

Model-based design: a report from the trenches of the DARPA Urban Challenge

The impact of model-based design on the software engineering community is impressive, and recent research in model transformations, and elegant behavioral specifications of systems has the potential to revolutionize the way in which systems are designed. Such techniques aim to raise the level of abstraction at which systems are specified, to remove the burden of producing application-specific programs with general-purpose programming. For complex real-time systems, however, the impact of model-driven approaches is not nearly so widespread. In this paper, we present a perspective of model-based design researchers who joined with software experts in robotics to enter the DARPA Urban Challenge, and to what extent model-based design techniques were used. Further, we speculate on why, according to our experience and the testimonies of many teams, the full promises of model-based design were not widely realized for the competition. Finally, we present some thoughts for the future of model-based design in complex systems such as these, and what advancements in modeling are needed to motivate small-scale projects to use model-based design in these domains.

Optimal search mission with Unmanned Aerial Vehicles using Mixed Integer Linear Programming

This paper proposes the use of Mixed Integer Linear Programming (MILP) for efficient planning of search missions. The Unmanned Aerial Vehicles (UAVs) taking part in a search mission are assumed to be equipped with cameras or other sensors, with a specified field-of-view below the UAV. The design and implementation of this search algorithm have been made for a general case, such that multiple UAVs with arbitrarily geographically located base stations can take part in the joint search mission and be allocated to different parts of the search area. An algorithm for automatically generation of waypoints inside the defined area has been developed, such that the whole area is covered by the sensors field-of-view when all waypoints have been visited.

Cluster-based communication topology selection and UAV path planning in wireless sensor networks

In conventional wireless sensor networks (WSNs) to conserve energy Low Energy Adaptive Clustering Heirarchy (LEACH) is commonly used. Energy conversation is far more challenging for large scale deployments. This paper deals with selection of sensor network communication topology and the use of Unmanned Aerial Vehicle (UAV) for data gathering. Particle Swarm Optimzation (PSO) is proposed as an optimization method to find the optimal clusters in order to reduce the energy consumption, bit error rate (BER), and UAV travel time. PSO is compared to LEACH using a simulation case and the results show that PSO outperforms LEACH in terms of energy consumption and BER while the UAV travel time is similar. The numerical results further illustrate that the performance gap between them increases with the number of cluster head nodes. The reduced energy consumption means that the network life time can be significantly extended and the lower BER will increase the amount of data received from the entrie network.

Optimal relay path selection and cooperative communication protocol for a swarm of UAVs

In many applications based on the use of unmanned aerial vehicles (UAVs), it is possible to establish a cluster of UAVs in which each UAV knows the other vehicles position. Assuming that the common channel condition between any two nodes of UAVs is line-of-sight (LOS), the time and energy consumption for data transmission on each path that connecting two nodes may be estimated by a node itself. In this paper, we use a modified Bellman-Ford algorithm to find the best selection of relay nodes in order to minimize the time and energy consumption for data transmission between any UAV node in the cluster and the UAV acting as the cluster head. This algorithm is applied with a proposed cooperative MAC protocol that is compatible with the IEEE 802.11 standard. The evaluations under data saturation conditions illustrate noticeable benefits in successful packet delivery ratio, average delay, and in particular the cost of time and energy.

Task assignment for cooperating UAVs under radio propagation path loss constraints

Unmanned Aerial Vehicles (UAVs) may be used for surveillance of power lines and railways and topological surveying to support the planning of new road routes. Motivated by these applications we will in this paper formulate a Mixed Integer Linear Programming (MILP) problem to be used for coarse offline path planning of such missions. Many of the missions rely on real time transfer of sensor data back to a human operator at the base station, to allow for intervention if the collected data show something of particular interest. In our approach we allow for the use of multiple UAVs where one or more of the vehicles also can be used as relay nodes for the transmitted data. The path obtained by solving the optimization problem is analyzed using a realistic radio propagation path loss simulator. If the radio propagation path loss exceeds the maximum design criterion the optimization problem is solved again with stricter communication constraint, and the procedure is continued in an iterative manner until the criterion is met.