No abstract

Area coverage with an onboard sensor is an important task for an unmanned aerial vehicle (UAV) with many applications. Autonomous fixed-wing UAVs are more appropriate for larger scale area surveying since they can cover ground more quickly. However, their non-holonomic dynamics and susceptibility to disturbances make sensor coverage a challenging task. Most previous approaches to area coverage planning are offline and assume that the UAV can follow the planned trajectory exactly. In this paper, this restriction is removed as the aircraft maintains a coverage map based on its actual pose trajectory and makes control decisions based on that map. The aircraft is able to plan paths in situ based on sensor data and an accurate model of the on-board camera used for coverage. An information theoretic approach is used that selects desired headings that maximize the expected information gain over the coverage map. In addition, the branch entropy concept previously developed for autonomous underwater vehicles is extended to UAVs and ensures that the vehicle is able to achieve its global coverage mission. The coverage map over the workspace uses the projective camera model and compares the expected area of the target on the ground and the actual area covered on the ground by each pixel in the image. The camera is mounted on a two-axis gimbal and can either be stabilized or optimized for maximal coverage. Hardware-in-the-loop simulation results and real hardware implementation on a fixed-wing UAV show the effectiveness of the approach. By including the already developed automatic takeoff and landing capabilities, we now have a fully automated and robust platform for performing aerial imagery surveys.

This work addresses the problem of localizing a ground target observed by multiple heterogeneous unmanned vehicles. Specifically, the case of a team of unmanned aerial and ground vehicles is analyzed. Effective collaboration between unmanned aerial and ground vehicles can utilize the strengths of both platforms while mediating their individual weaknesses. In this research, a probabilistic framework is proposed to improve target localization accuracy by utilizing the epipolar geometry and inter-robot localization measurements. A virtual simulation environment is implemented to fully test the proposed methods. Hardware experiments are carried out to validate the proposed methods. Note to Practitioners-This paper was motivated by the problem of tracking ground objects of interest using multiple cooperative autonomous vehicles. Most of the existing approaches to cooperative target tracking are designed for homogeneous vehicles and neglect geometrical constraints of the multiview target tracking problem. The methodology proposed in this work incorporates heterogenous robots to cooperatively track a ground object of interest.To improve the accuracy of target localization, the epipolar geometry between multirobot views is utilized to formulate mathematical constraints. In this paper, it is analytically proved that the imposing the proposed constraints improves target localization accuracy. Experimental and simulation results demonstrate the benefits in target localization accuracy using the proposed method.

In this study, a novel robot control framework is presented for multiple autonomous underwater vehicles. In this framework, we incorporate sonar sensor data and integrated navigation system position data in a simulation environment, called UNBeatable-Sim, where complex control behaviors can be executed and analyzed. UNBeatable-Sim is developed by the COllaboration Based Robotics and Automation (COBRA) research group at the University of New Brunswick, Canada. Range and pose sensor data are accumulated in an ocean environment constructed using seabed data collected at Bedford Basin, Nova Scotia, Canada by DRDC Atlantic. A seabed map is generated from the real-world data using UNBeatable-Sim. The underwater vehicle and the seabed are simulated and visualized using OpenGL. An external controller implemented using Matlab and Simulink is used to control the robot model. Simulations of multiple underwater vehicles to navigate in the ocean environment to sense and map the seabed are performed using UNBeatable-Sim to assess the system architecture and controller performance.

Autonomous navigation in global positioning system (GPS)-denied environments is one of the challenging problems in robotics. For small flying robots, autonomous navigation is even more challenging. These robots have limitations such as fast dynamics and limited sensor payload. To develop an autonomous robot, many challenges including two-dimensional (2D) and three-dimensional (3D) perception, path planning, exploration, and obstacle avoidance should be addressed in real-time and with limited resources. In this paper, a complete solution for autonomous navigation of a quadrotor rotorcraft is presented. The proposed solution includes 2D and 3D mapping with several autonomous behaviors such as target localization and displaying maps on multiple remote tablets. Multiple tests were performed in simulated and indoor/outdoor environments to show the effectiveness of the proposed solution.