Kuo-Chi Lin

Using genetic algorithms to evolve the control rules of a swarm of UAVs

Due to the large number of interactions that the agents in a swarm of UAVs have with each other as well as with their environment, it is necessary to obtain a viable procedure that yields a reasonable group behavior from these local interactions. This paper proposes a hierarchical behavior-based model in which several parameters are adjusted with a genetic algorithm (GA). The presented model implements three explicit layers of behaviors (basic, group and mission) in a simulation in which the agents seek to survey a rectangular target area while avoiding a circular obstacle

A Cooperative Path Planning Algorithm for a Multiple Mobile Robot System in a Dynamic Environment

A practical path planning method for a multiple mobile robot system (MMRS) requires handling both the collision-free constraint and the kinematic constraint of real robots, the latter of which has to date been neglected by most path planning methods. In this paper, we present a practical cooperative path planning algorithm for MMRS in a dynamic environment. First, each robot uses an analytical method to plan an obstacle-avoidance path. Then, a distributed prioritized scheme is introduced to realize cooperative path planning. In the scheme, each robot calculates a priority value according to its situation at each instant in time, which will determine the robots priority. Higher-priority robots can ignore lower-priority robots, whereas lower-priority robots should avoid collisions with higher-priority robots. To minimize the path length for MMRS, a least path length constraint is added. The priority value is also calculated by a path cost function that takes the path length into consideration. Unlike other priority methods, the algorithm proposed is not time consuming; therefore, it is suitable for dynamic environments. Simulation results are presented to verify the effectiveness of the proposed algorithm.

Confidence-Level-Based New Adaptive Particle Filter for Nonlinear Object Tracking:

Nonlinear object tracking from noisy measurements is a basic skill and a challenging task of mobile robotics, especially under dynamic environments. The particle filter is a useful tool for nonlinear object tracking with non-Gaussian noise. Nonlinear object tracking needs the real-time processing capability of the particle filter. While the number in a traditional particle filter is fixed, that can lead to a lot of unnecessary computation. To address this issue, a confidence-level-based new adaptive particle filter (NAPF) algorithm is proposed in this paper. In this algorithm the idea of confidence interval is utilized. The least number of particles for the next time instant is estimated according to the confidence level and the variance of the estimated state. Accordingly, an improved systematic re-sampling algorithm is utilized for the new improved particle filter. NAPF can effectively reduce the computation while ensuring the accuracy of nonlinear object tracking. The simulation results and the ball tracking results of the robot verify the effectiveness of the algorithm.

A joint subcarrier selection and power allocation scheme using variational inequality in OFDM-based cognitive relay networks

Introducing orthogonal frequency division multiplexing OFDM into cognitive radio CR can potentially increase the spectrum efficiency, but it also leads to further challenges for the resource allocation of CR networks. In OFDM-based cognitive relay networks, two of the most significant research issues are subcarrier selection and power allocation. In this paper, a non-cooperative game model is proposed to maximize the system throughput by jointly optimizing subcarrier selection and power allocation. First, taking the direct and relay links into consideration, an equivalent channel gain is presented to simplify the cooperative relay model into a non-relay model. Then, a variational inequality method is utilized to prove the existence and uniqueness of the Nash equilibrium solution of the proposed non-cooperative game. Moreover, to compute the solution of the game, a suboptimal algorithm based on the Lagrange function and distributed iterative water-filling algorithm is proposed. The proposed algorithm can jointly optimize the process of subcarrier selection and power allocation. Finally, simulation results are shown to demonstrate the effectiveness of the proposed joint subcarrier selection and power allocation scheme. Copyright

Robust estimation of a maneuvering target from multiple unmanned air vehicles' measurements

When multiple UAVs collaborate to track a maneuvering target, their position measurement sensors are sometimes corrupted by noise biases (e.g. sensor drifting). In this case, the zero-mean noise assumption of the Kalman filter is therefore violated and the desired optimal estimate will not be guaranteed. In this paper, an H-infinity filter is utilized to estimate the position of the maneuvering target to compensate for non-zero-mean noise. Furthermore, the constrained H-infinity filter is shown to be superior to the Kalman filter.

Multi-agent coordination method based on fuzzy Q-learning

Traditional reinforcement learning algorithm can only solve the learning problem of the intelligent agent with discrete state space and discrete action space. This paper studies the coordination of multiple intelligent agents in a complicated dynamic environment with uncertainty. A coordination model based on the fuzzy Q-learning technique is suggested. This model uses fuzzy logic to generalize the agentpsilas continuous state space. Every agent, when making decisions on its actions, needs to consider the influences of other agents to the environment. The agent first evaluates the actions they select, then, uses the fuzzy Q-learning to learn their action strategy. In the process, the action keeps improving and the conflicts among agents can be resolved. This model was used in the RoboCup soccer simulation game and the simulation results showed that the performance of attacking is obviously improved.

Behavior-Based Control Hierarchy of Unmanned Aerial Vehicle Swarming

Although there are many advantages associated with UAVs, the production cost as well as operation cost of one of today’s military UAV’s is an overwhelming problem. In this paper, a more basic and fundamental approach to controlling a mission of UAVs is proposed. Based on a combination of low-level behaviors, a swarming hierarchy is derived from three levels of behaviors: basic, individual and group. The proposed hierarchy enables multiple UAVs to successfully conduct a mission using a condition-based switching mechanism and relying on local interactions between each other and with minimal interactions with human controllers who only intervene when coordinating the mission-level actions [1]. From an analytical model of a point mass UAV in a 2D environment where mass, air drag force, initial speed and gravitational acceleration are constant and the only controls for the UAV are the thrust force and its banking angle, several behavior combinations are simulated in order to obtain a family of feasible solutions. The results achieved suggest that the above mentioned condition-based switching mechanism can be used and eventually applied to actual UAVs.

Hybrid Relay Forwarding and Interference Mitigation Mechanisms in Cooperative Cognitive Ad-Hoc Networks

In this paper, a cooperative cognitive ad-hoc network is considered where a primary user and secondary users coexist with an interference user. A hybrid cooperation mechanism is proposed which allows secondary users to cooperate with primary user by forwarding the interference user’s data as well as conventionally forwarding the primary user’s data. The proposed scheme provides a more flexible approach that the secondary user can select the relay method according to channel state information. The primary user would release a reasonable portion of spectrum in return for data relay (so the primary user could improves throughput), while the secondary users could earn more spectrum access rights. When there is an interference user, secondary users could choose a method to help primary users by relay forwarding or interference mitigation depending on its own channel quality. With an aim of maximizing primary user’s rate and minimizing the secondary users’ energy consumption, a Stackelberg game framework is introduced in which a primary user is modeled as the leader and multiple secondary users are modeled as followers. The secondary users control their power to cooperate with the primary user to optimize game utilization. The existence and uniqueness of the proposed game’s equilibrium is proved and a distributed iterative algorithm is designed to reach the game equilibrium. Numerical results show that the proposed hybrid relay forwarding and interference mitigation mechanism outperforms the single cooperation scheme when an interference user exists.

Communication Scheme in Trusted Sensor Network

Trust is an important issue when a wireless sensor network is used in the fields such as military surveillance and reconnaissance, emergency rescue, and intelligent residential district. Due to the restrictions of energy, resource, and computing power in the sensor nodes, the traditional secure solutions cannot be applied to the wireless sensor networks directly. In this paper, applying the concept of trusted computing, a trust-based dynamic key-distribution scheme is suggested. In this scheme, when a cluster head distributes the key for the communication among nodes in its cluster, based on the trust relations between the base station and cluster head and between the cluster head and sensor nodes, a trust chain is formed, which provides a secured path for the transmission of data. It can be shown that this solution ensures the security of communications between nodes, and reduces the energy consumption of the security scheme.

Communication scheme in trusted sensor network

Trust is an important issue when a wireless sensor network is used in the fields such as military surveillance and reconnaissance, emergency rescue, and intelligent residential district. Due to the restrictions of energy, resource, and computing power in the sensor nodes, the traditional secure solutions cannot be applied to the wireless sensor networks directly. In this paper, applying the concept of trusted computing, a trust-based dynamic key-distribution scheme is suggested. In this scheme, when a cluster head distributes the key for the communication among nodes in its cluster, based on the trust relations between the base station and cluster head and between the cluster head and sensor nodes, a trust chain is formed, which provides a secured path for the transmission of data. It can be shown that this solution ensures the security of communications between nodes, and reduces the energy consumption of the security scheme.