Control Of Multiple Unmanned Vehicles Research Articles

Extended Kalman Filter Based Resilient Formation Tracking Control of Multiple Unmanned Vehicles via Game-Theoretical Reinforcement Learning

In this paper, we discuss the resilient formation tracking control problem of multiple unmanned vehicles (MUV). A dynamic leader-follower distributed control structure is utilized to optimize the performance of the formation tracking. For the follower of the MUV, the leader is a cooperative unmanned vehicle, and the target of formation tracking is a non-cooperative unmanned vehicle with a nonlinear trajectory. Therefore, an extended Kalman filter (EKF) observer is designed to estimate the state of the target. Then the leader of the MUV is adjusted dynamically according to the state of the target. In order to describe the interactions between the follower and dynamic leader, a Stackelberg game model is constructed to handle the hierarchical decision problems. At the lower layer, each follower responds by observing the leader's strategy, and the potential game is used to prove a Nash equilibrium among all followers. At the upper layer, the dynamic leader makes decisions depending on the response of all followers to reaching the Stackelberg equilibrium. Moreover, the Stackelberg-Nash equilibrium of the designed game theoretical model is proven. A novel reinforcement learning-based algorithm is designed to achieve the Stackelberg-Nash equilibrium of the game. Finally, the effectiveness of the method is verified by a variety of formation tracking simulation experiments.

Formation control of multiple unmanned vehicles based on graph theory: A Comprehensive Review

In recent years, formation control for multiple unmanned vehicles becomes an active research topic that has received a lot of attention from scientists due to its superior advantages compared with other conventional systems. Algebraic graph and graph rigidity theories are the two main mathematical backgrounds of the formation control theory. The graph theory is used to describe the interconnections among vehicles in formation while rigid graph theory - an important subset of graph theory - ensured that the inter-vehicle distance constraints of the desired formation are enforced via the graph rigidity. This paper provides a comprehensive review of graph theory supporting formation control for groups of unmanned aerial vehicles (UAV) or swarm UAVs. The background of the theory and the recent developments of graph-theory-based formation control are reviewed. We provide a cohesive overview of the formation control and coordination of multiple vehicles. Finally, some challenges and future potential directions in formation control are discussed.

Team Member Personality, Performance and Stress in a RoboFlag Synthetic Task Environment

Effective teamwork is an important component in the control of multiple unmanned vehicles (UVs). Personality traits of individual team members may be a factor influencing overall team performance and individual levels of perceived workload and stress. In this study, a multiple UV environment was simulated by use of the RoboFlag game. Participants played the RoboFlag game individually or in two-person teams and were presented with this sequence of task-phases: RoboFlag alone, RoboFlag with a secondary task, RoboFlag alone. It was predicted that the RoboFlag performance would relate to ‘Big Five’ personality traits. It was also expected that Neuroticism would relate to higher workload and stress, especially in more demanding task conditions. The results suggested that the influence of personality is generally limited, but may sometimes be important. Specifically, high Neuroticism individuals showed higher workload and stress in a team player condition, which may impose social as well as task demands.

Supervisory Control of Multiple Unmanned Vehicles: International Perspectives on Methodologies and Enabling Interface Technologies

Unmanned vehicles (UVs) have proven to be an extremely valuable military capability. Added system automation/autonomy will change the role of the UV operator from manual controller to supervisor of, and maybe even teammate to, these highly automated systems. Continuing this trend, there is a desire across several NATO countries to explore concepts in which multiple autonomous UVs are simultaneously controlled by a single supervisor. The obvious utility of UVs, the rapid advances in associated technology, and the desire to increase operator span of control has led to the formation of a NATO Research Task Group to address the human factors challenges, methodologies, and technologies associated with single operator control of multiple UVs. This panel, consisting of prominent members of the seven country NATO group, will describe the state-of-the art in human factors related developments of multi-UV control through examples of their respective nation's research and technical demonstrations in this area. Additionally, panelists will interact with the audience and address questions centering on the rationale, methodologies, and challenges associated with multi-UV control.

OPTIMAL TASK ALLOCATION AND DYNAMIC TRAJECTORY PLANNING FOR MULTI-VEHICLE SYSTEMS USING NONLINEAR HYBRID OPTIMAL CONTROL

Based on a nonlinear hybrid dynamical systems model a new planning method for optimal coordination and control of multiple unmanned vehicles is investigated. The time dependent hybrid state of the overall system consists of discrete (roles, actions) and continuous (e.g. position, orientation, velocity) state variables of the vehicles involved. The evolution in time of the system's hybrid state is described by a hybrid state automaton. The presented approach enables a tight and formal coupling of discrete and continuous state dynamics, i.e. of dynamic role and action assignment and sequencing as well as of the physical motion dynamics of a single vehicle modeled by nonlinear differential equations. The planning problem of determining optimal hybrid state trajectories that minimize a cost function as time or energy for optimal multi-vehicle cooperation subject to constraints including the vehicle's motion dynamics is transformed to a mixed-binary dynamic optimization problem being solved numerically. The numerical method consists of an inner iteration where multiphase optimal control problems are solved using a direct collocation method and an outer iteration based on a branch-and-bound search of the discrete solution space. The approach presented in this paper is applied to the scenarios of optimal simultaneous waypoint or target sequencing and dynamic trajectory planning for a team of unmanned aerial vehicles in a plane and to optimal role assignment and physics-based trajectories in robot soccer.