A Virtual Reality Soldier Simulator with Body Area Networks for Team Training

Abstract

Soldier-based simulators have been attracting increased attention recently, with the aim of making complex military tactics more effective, such that soldiers are able to respond rapidly and logically to battlespace situations and the commander's decisions in the battlefield. Moreover, body area networks (BANs) can be applied to collect the training data in order to provide greater access to soldiers' physical actions or postures as they occur in real routine training. Therefore, due to the limited physical space of training facilities, an efficient soldier-based training strategy is proposed that integrates a virtual reality (VR) simulation system with a BAN, which can capture body movements such as walking, running, shooting, and crouching in a virtual environment. The performance evaluation shows that the proposed VR simulation system is able to provide complete and substantial information throughout the training process, including detection, estimation, and monitoring capabilities.

Keywords: body area network; training simulator; virtual reality.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1

System training effectiveness process.

Figure 2

System architecture: single-soldier layout (a); multi-soldier network (b).

Figure 3

Top view of sensor nodes (Left); the wearable sensor node (Right).

Figure 4

Deployment of sensor nodes and the sink node.

Figure 5

Mean and variance of the gyroscope in the x, y and z directions.

Figure 6

Mean and variance of the accelerometer in the x, y and z directions.

Figure 7

Mean and variance of the magnetometer in the x, y and z directions.

Figure 8

The attitude angles of a sensor node placed on the upper arm when standing in a T-pose.

Figure 9

The diagram shows the process of sensor fusion.

Figure 10

Operation mode during each time slot. (a) Step 1: automatic packet synchronization; (b) Step 2: identification check on valid packets by the sink node.

Figure 11

The diagram shows that sensor nodes communicate with sink node in two domains.

Figure 12

A fully equipped soldier.

Figure 13

System initialization flow diagram.

Figure 14

The kinematic chain of a human body.

Figure 15

Virtual environment on a HMD. (a) An indoor view. (b) An outdoor view.

Figure 16

Sensing measurement errors of the sensor nodes were calibrated when the T-pose calibration was performed. (a) All sensor nodes were calibrated well during the T-pose procedure. (b) One sensor node attached to the right thigh was not calibrated well, and a sensing error was derived in the pitch direction.

Figure 17

The scalar part and the vector part of the quaternion error for a small rotation of measurement error of pitch angle.

Figure 18

Snapshot of the system.

Figure 19

Means of experienced participants and unexperienced participants under various experimental conditions (horizontal axis: single-man, two-man, three-man; vertical axis unit: seconds).

Figure 20

Mean of three-man teams with voice communication/without voice communication under the experimental conditions (horizontal axis: experienced/unexperienced participants; vertical axis unit: seconds).

Figure 21

Mean times with different numbers of participants under the experimental conditions (horizontal axis: number of participants; vertical axis unit: seconds).