Nonlinear trajectory generation for unmanned air vehicles with multiple radars

Inanc, Tamer; Misovec, Kathleen; Murray, Richard M.

doi:10.1109/cdc.2004.1429333

Cited by 16 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Trajectories should not require a movement-e.g., navigation around a sharp turn-that exceeds a vehicle's capability [2]. In still other cases the trajectory should provide a low probability of detection [3]. All of these examples have the same thing in common: they assume at least one desirable trajectory exists that satisfies all constraints, but it is not known a priori.…”

Section: Introductionmentioning

confidence: 97%

Online generation of trajectories for autonomous vehicles using a multi-agent system

Greenwood

Elsayed

Sarker

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

Autonomous vehicles are frequently deployed in environments where only certain trajectories are feasible. Classical trajectory generation methods attempt to find a feasible trajectory that satisfies a set of constraints. In some cases the optimal trajectory may be known, but it is hidden from the autonomous vehicle. Under such circumstance the vehicle must discover a feasible trajectory. This paper describes a multi-agent system that uses a combination of reinforcement learning and differential evolution to generate a trajectory that is ε-close to a target trajectory that is hidden.

show abstract

Section: Introductionmentioning

confidence: 97%

Online generation of trajectories for autonomous vehicles using a multi-agent system

Greenwood

Elsayed

Sarker

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

show abstract

“…Zhu et al [32] proposed the Delaunay triangulation method to generate way points for cooperative UAVs with Dubin's car model. Single UAV real-time motion planning and trajectory generation are discussed in [33]- [35]. Cooperative path planning and task assignment were recently investigated in [14] and [36].…”

Section: Introductionmentioning

confidence: 99%

Optimal scheduling for refueling multiple autonomous aerial vehicles

2006

View full text Add to dashboard Cite

Abstract-The scheduling, for autonomous refueling, of multiple unmanned aerial vehicles (UAVs) is posed as a combinatorial optimization problem. An efficient dynamic programming (DP) algorithm is introduced for finding the optimal initial refueling sequence. The optimal sequence needs to be recalculated when conditions change, such as when UAVs join or leave the queue unexpectedly. We develop a systematic shuffle scheme to reconfigure the UAV sequence using the least amount of shuffle steps. A similarity metric over UAV sequences is introduced to quantify the reconfiguration effort which is treated as an additional cost and is integrated into the DP algorithm. Feasibility and limitations of this novel approach are also discussed.

show abstract

“…(See also Inanc et al 2004.) Tsitsiklis (1995) and Polymenakos et al (1998) describe reasonably efficient algorithms for finding "continuous shortest paths," but these algorithms do not easily extend to sideconstrained problems or to problems with direction-dependent travel costs.…”

Section: Introductionmentioning

confidence: 99%

Routing Military Aircraft With A Constrained Shortest-Path Algorithm

Carlyle

Røyset

Wood

2009

mor

View full text Add to dashboard Cite

We formulate and solve aircraft-routing problems that arise when planning missions for military aircraft that are subject to ground-based threats such as surface-to-air missiles. We use a constrained-shortest path (CSP) model that discretizes the relevant airspace into a grid of vertices representing potential waypoints, and connects vertices with directed edges to represent potential flight segments. The model is flexible: It can route any type of manned or unmanned aircraft; it can incorporate any number of threats; and it can incorporate, in the objective function or as side constraints, numerous mission-specific metrics such as risk, fuel consumption, and flight time. We apply a new algorithm for solving the CSP problem and present computational results for the routing of a high-altitude F/A-18 strike group, and the routing of a medium-altitude unmanned aerial vehicle. The objectives minimize risk from ground-based threats while constraints limit fuel consumption and/or flight time. Run times to achieve a near-optimal solution range from fractions of a second to 80 seconds on a personal computer. We also demonstrate that our methods easily extend to handle turn-radius constraints and round-trip routing.

show abstract

Nonlinear trajectory generation for unmanned air vehicles with multiple radars

Cited by 16 publications

References 18 publications

Online generation of trajectories for autonomous vehicles using a multi-agent system

Online generation of trajectories for autonomous vehicles using a multi-agent system

Optimal scheduling for refueling multiple autonomous aerial vehicles

Routing Military Aircraft With A Constrained Shortest-Path Algorithm

Contact Info

Product

Resources

About