AFIT UAV Swarm Mission Planning and Simulation System

The purpose of this research is to design and implement a comprehensive mission planning system for swarms of autonomous aerial vehicles. The system integrates several problem domains including path planning, vehicle routing, and swarm behavior. The developed system consists of a parallel, multi-objective evolutionary algorithm-based path planner, a genetic algorithm-based vehicle router, and a parallel UAV swarm simulator. Each of the system\u27s three primary components are developed on AFIT\u27s Beowulf parallel computer clusters. Novel aspects of this research include: integrating terrain following technology into a swarm model as a means of detection avoidance, combining practical problems of path planning and routing into a comprehensive mission planning strategy, and the development of a swarm behavior model with path following capabilities

UAV 3-D path planning based on MOEA/D with adaptive areal weight adjustment

Unmanned aerial vehicles (UAVs) are desirable platforms for time-efficient and cost-effective task execution. 3-D path planning is a key challenge for task decision-making. This paper proposes an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) with an adaptive areal weight adjustment (AAWA) strategy to make a tradeoff between the total flight path length and the terrain threat. AAWA is designed to improve the diversity of the solutions. More specifically, AAWA first removes a crowded individual and its weight vector from the current population and then adds a sparse individual from the external elite population to the current population. To enable the newly-added individual to evolve towards the sparser area of the population in the objective space, its weight vector is constructed by the objective function value of its neighbors. The effectiveness of MOEA/D-AAWA is validated in twenty synthetic scenarios with different number of obstacles and four realistic scenarios in comparison with other three classical methods.Comment: 23 pages,11 figure

Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art

Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points, which are solutions, policies, or strategies that cause an irrecoverable loss (e.g., breakage of a machine or equipment, or life threat). Although a comprehensive survey of safe reinforcement learning algorithms was published in 2015, a number of new algorithms have been proposed thereafter, and related works in active learning and in optimization were not considered. This paper reviews those algorithms from a number of domains including reinforcement learning, Gaussian process regression and classification, evolutionary algorithms, and active learning. We provide the fundamental concepts on which the reviewed algorithms are based and a characterization of the individual algorithms. We conclude by explaining how the algorithms are connected and suggestions for future research.Comment: The final authenticated publication was made In: Heintz F., Milano M., O'Sullivan B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science, vol 12641. Springer, Cham. The final authenticated publication is available online at \<https://doi.org/10.1007/978-3-030-73959-1_12

Testing Method for Multi-UAV Conflict Resolution Using Agent-Based Simulation and Multi-Objective Search

A new approach to testing multi-UAV conflict resolution algorithms is presented. The problem is formulated as a multi-objective search problem with two objectives: finding air traffic encounters that 1) are able to reveal faults in conflict resolution algorithms and 2) are likely to happen in the real world. The method uses agent-based simulation and multi-objective search to automatically find encounters satisfying these objectives. It describes pairwise encounters in three-dimensional space using a parameterized geometry representation, which allows encounters involving multiple UAVs to be generated by combining several pairwise encounters. The consequences of the encounters, given the conflict resolution algorithm, are explored using a fast-time agent-based simulator. To find encounters meeting the two objectives, a genetic algorithm approach is used. The method is applied to test ORCA-3D, a widely cited open-source multi-UAV conflict resolution algorithm, and the method’s performance is compared with a plausible random testing approach. The results show that the method can find the required encounters more efficiently than the random search. The identified safety incidents are then the starting points for understanding limitations of the conflict resolution algorithm

improving path planning of unmanned aerial vehicles in an immersive environment using meta-paths and terrain information

Effective command and control of unmanned aerial vehicles (UAVs) is an issue under investigation as the military pushes toward more automation and incorporation of technology into their operational strategy. UAVs require the intelligence to maneuver safely along a path to an intended target while avoiding obstacles such as other aircraft or enemy threats. To date, path-planning algorithms (designed to aid the operator in the control of semi-autonomous UAVs) have been limited to providing only a single solution (alternate path) without utilizing input or feedback from the UAV operator. The work presented in this thesis builds off of and improves an existing path planner. The original path planner presents a unique platform for decision making in a three-dimensional environment where multiple solution paths are generated using Particle Swarm Optimization (PSO) and returned to the operator for evaluation. The paths are optimized to minimize risk due to enemy threats, to minimize fuel consumption incurred by deviating from the original path, and to maximize reconnaissance over predefined targets. The work presented in this thesis focuses on improving the mathematical models of these objectives. Terrain data is also incorporated into the path planner to ensure that the generated alternate paths are feasible and at a safe height above ground. An effective interface is needed to evaluate the alternate paths returned by PSO. A meta-path is a new concept presented in this thesis to address this issue. Meta-paths allow an operator to explore paths in an efficient and organized manner by displaying multiple alternate paths as a single path cloud. The interface was augmented with more detailed information on these paths to allow the operator to make a more informed decision. Two other interaction techniques were investigated to allow the operator more interactive control over the results displayed by the path planner. Viewing the paths in an immersive environment enhances the operator\u27s understanding of the situation and the options while facilitating better decision making. The problem formulation and solution implementation are described along with the results from several simulated scenarios. Preliminary assessments using simulated scenarios show the usefulness of these features in improving command and control of UAVs. Finally, a user study was conducted to gauge how different visualization capabilities affect operator performance when using an interactive path planning tool. The study demonstrates that viewing alternate paths in 3D instead of 2D takes more time because the operator switches between multiple views of the paths but also suggests that 3D is better for allowing the operator to understand more complex situations

Learning soft task priorities for safe control of humanoid robots with constrained stochastic optimization

Multi-task prioritized controllers are able to generate complex robot behaviors that concurrently satisfy several tasks and constraints. To perform, they often require a human expert to define the evolution of the task priorities in time. In a previous paper [1] we proposed a framework to automatically learn the task priorities thanks to a stochastic optimization algorithm (CMA-ES) maximizing the robot performance on a certain behavior. Here, we learn the task priorities that maximize the robot performance, ensuring that the optimized priorities lead to safe behaviors that never violate any of the robot and problem constraints. We compare three constrained variants of CMA-ES on several benchmarks, among which two are new robotics benchmarks of our design using the KUKA LWR. We retain (1+1)-CMA-ES with covariance constrained adaptation [2] as the best candidate to solve our problems, and we show its effectiveness on two whole-body experiments with the iCub humanoid robot

Multiobjective overtaking maneuver planning of autonomous ground vehicles

This paper proposes a computational trajectory optimization framework for solving the problem of multi-objective automatic parking motion planning. Constrained automatic parking maneuver problem is usually difficult to solve because of some practical limitations and requirements. This problem becomes more challenging when multiple objectives are required to be optimized simultaneously. The designed approach employs a swarm intelligent algorithm to produce the trade-off front along the objective space. In order to enhance the local search ability of the algorithm, a gradient operation is utilized to update the solution. In addition, since the evolutionary process tends to be sensitive with respect to the flight control parameters, a novel adaptive parameter controller is designed and incorporated in the algorithm framework such that the proposed method can dynamically balance the exploitation and exploration. The performance of using the designed multi-objective strategy is validated and analyzed by performing a number of simulation and experimental studies. The results indicate that the present approach can provide reliable solutions and it can outperform other existing approaches investigated in this paper

Multi-objective optimal parking maneuver planning of autonomous wheeled vehicles

This paper proposes a computational trajectory optimization framework for solving the problem of multi-objective automatic parking motion planning. Constrained automatic parking maneuver problem is usually difficult to solve because of some practical limitations and requirements. This problem becomes more challenging when multiple objectives are required to be optimized simultaneously. The designed approach employs a swarm intelligent algorithm to produce the trade-off front along the objective space. In order to enhance the local search ability of the algorithm, a gradient operation is utilized to update the solution. In addition, since the evolutionary process tends to be sensitive with respect to the flight control parameters, a novel adaptive parameter controller is designed and incorporated in the algorithm framework such that the proposed method can dynamically balance the exploitation and exploration. The performance of using the designed multi-objective strategy is validated and analyzed by performing a number of simulation and experimental studies. The results indicate that the present approach can provide reliable solutions and it can outperform other existing approaches investigated in this paper