The exponential growth of commercially available unmanned aerial systems (UAVs) provides a reliable, low-cost platform for mobile sensor deployment. Extensive work exists leveraging large numbers of these platforms for intelligence, surveillance, and reconnaissance (ISR) applications ranging from search and rescue to plume detection and tracking. The proposed Robotic Catch And Release Manipulation Architecture (CARMA) is designed to address flight time limitations of small UAVs, leveraging a robotic manipulation and computer vision to actively compensate for perturbations of the UAV in flight (caused by environmental conditions such as wind) and movements of the recharging station (cause by movement over rough terrain, sea state in maritime applications, etc.). CARMA leverages an industrial robotic manipulator to create a robust system capable of capturing multi-rotor UAVs in an agitated hover. Using a custom-designed end-effector incorporating a monocular camera system, CARMA employs a closed-loop control strategy informed by a relative position and orientation estimate of the UAV using an active marker approach. Results demonstrate UAV tracking accuracy sufficient for capture of a small, CrazyFlie 2.0 UAV. The architecture is deployed on the Universal Robots UR10 manipulator which is able to successfully track the Crazyflie for capture.
A large group of small, limited endurance autonomous vehicles working cooperatively may be more effective in target search and track operations when compared with a long endurance vehicle. For a persistent search and track task, a need exists for coordination algorithms that account for limited agent endurance. This paper presents a multi-agent persistent search and track algorithm incorporating endurance constraints in a high-level algorithm that deploys and recovers vehicles from a stationary base station. Agents are assigned to search, track, return, and deploy modes using on-board sensor and battery measurements. Simulations and experiments show the relationship between the number of agents, battery capacity, search performance, and target tracking performance. The measures used to quantify these relationships include spatiotemporal coverage, target tracking effectiveness, and the usage of available aircraft. Hardware experiments demonstrate the effectiveness of the approach.
This paper presents an autonomous multivehicle control algorithm capable of persistently searching and tracking targets in a defined search area subject to operational endurance constraints of individual agents. The algorithm development is modular to allow scalability and a control architecture that can be modified to any type of autonomous vehicle, search area, or target. In practical application, a target can be anything from heat signatures to radioactive material; therefore, this work employs a generic emitter-detector pair as a placeholder relationship for real world applications. The control strategy accounts for the appearance, motion, and disappearance of multiple targets in the search space constituting the utility of creating a team of multiple search agents. When agent battery level drops below a predetermined threshold, the agent returns to a base station to recharge and be relaunched into the mission. Remaining agents must account for this loss and gain of other team members as they exit the search environment. The contributions of this work are 1) the design of search trajectories for autonomous vehicles with limited endurance, 2) incorporation of return-to-base and recharge time requirements, and 3) coordination of multiple vehicles by developing a decision-making model to and assign agents to operational modes. We have run an extensive number of experimental trials to collect and analyze performance data for further development and testing.
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