Chasing the Intruder: A Reinforcement Learning Approach for Tracking Intruder Drones

Drones are becoming versatile in a myriad of applications. This has led to the use of drones for spying and intruding into the restricted or private air spaces. Such foul use of drone technology is dangerous for the safety and security of many critical infrastructures. In addition, due to the varied low-cost design and agility of the drones, it is a challenging task to identify and track them using the conventional radar systems. In this paper, we propose a reinforcement learning based approach for identifying and tracking any intruder drone using a chaser drone. Our proposed solution uses computer vision techniques interleaved with the policy learning framework of reinforcement learning to learn a control policy for chasing the intruder drone. The whole system has been implemented using ROS and Gazebo along with the Ardupilot based flight controller. The results show that the reinforcement learning based policy converges to identify and track the intruder drone. Further, the learnt policy is robust with respect to the change in speed or orientation of the intruder drone

Taxis strike back: A field trial of the driver guidance system

National Research Foundation (NRF) Singapore under Corp Lab @ University scheme; Fujitsu Lt

Upping the game of taxi driving in the age of Uber

National Research Foundation (NRF) Singapore under its Corp Lab @ University scheme; Fujitsu Limite

On Movement of Emergency Services amidst Urban Traffic

Managing traffic in urban areas is a complex affair. The same becomes more challenging when one needs to take into account the prioritized movement of emergency vehicles along with the normal flow of traffic. Although, mechanisms have been proposed to model intelligent traffic management systems, a concentrated effort to facilitate the movement of emergency services amongst urban traffic is yet to be formalized. This paper proposes a distributed multi-agent based mechanism to create partial green corridors for the movement of emergency service vehicles such as ambulances, fire brigade and police vans, amidst urban traffic. The proposed approach makes se of a digital network of traffic signal nodes equipped with traffic sensors and an agent framework to autonomously extend, maintain and manage partial green corridors for such emergency vehicles. The approach was emulated using Tartarus, an agent framework over a LAN. The results gathered under varying traffic conditions and also several emergency vehicles, validate the performance of this approach and its effects on the movement of normal traffic. Comparisons with the non-prioritized and full green corridor approaches indicate that the proposed partial corridor approach outperforms the rest

Optimizing Trajectory and Dynamic Data Offloading Using a UAV Access Platform

The use of unmanned aerial vehicles (UAV) as an integrated sensing and communication platform is emerging for surveillance and tracking applications, especially in large infrastructure-deficient environments. In this study, we develop a multi-UAV system to collect data dynamically in a resource-constrained context. The proposed approach consists of an access platform called Access UAV (A_UAV) that stochastically coordinates the data collection from the Inspection-UAVs (I_UAVs) equipped with a visual sensor to relay the same to the cloud. Our approach jointly considers the trajectory optimization of A_UAV and the stability of the data queues at each UAV. In particular, the Distance and Access Latency Aware Trajectory (DLAT) optimization for A_UAVs is developed, which generates a fair access schedule for I_UAVs. Moreover, a Lyapunov-based online optimization ensures the system stability of the average queue backlogs for dynamic data collection while minimizing total system energy. Coordination between I_UAV and A_UAV is achieved through a message-based mechanism. The simulation results validate the performance of our proposed approach against several baselines under different parameter settings