Biblio

Advances in artificial intelligence, machine learning, and robotics have profoundly impacted the field of autonomous navigation and driving. However, sensor spoofing attacks can compromise critical components and the control mechanisms of mobile robots. Therefore, understanding vulnerabilities in autonomous driving and developing countermeasures remains imperative for the safety of unmanned vehicles. Hence, we demonstrate cross-validation techniques for detecting spoofing attacks on the sensor data in autonomous driving in this work. First, we discuss how visual and inertial odometry (VIO) algorithms can provide a root-of-trust during navigation. Then, we develop examples for sensor data spoofing attacks using the open-source driving dataset. Next, we design an attack detection technique using VIO algorithms that cross-validates the navigation parameters using the IMU and the visual data. Following, we consider hardware-dependent attack survival mechanisms that support an autonomous system during an attack. Finally, we also provide an example of spoofing survival technique using on-board hardware oscillators. Our work demonstrates the applicability of classical mobile robotics algorithms and hardware security primitives in defending autonomous vehicles from targeted cyber attacks.

Autonomous navigation of a robot is more challenging in an uncontrolled environment owing to the necessity of coordination among several activities. This includes, creating a map of the surrounding, localizing the robot inside the map, generating a motion plan consistent with the map, executing the plan with control and all other tasks involved concurrently. Moreover, autonomous navigation problems are significant for future robotics applications such as package delivery, security, cleaning, agriculture, surveillance, search and rescue, construction, and transportation which take place in uncontrolled environments. Therefore, an attempt has been made in this research to develop a robot which could function as a security agent for a house to address the aforesaid particulars. This robot has the capability to navigate autonomously in the prescribed map of the operating zone by the user. The desired map can be generated using a Light Detection and Ranging (LiDAR) sensor. For robot navigation, it requires to pick out the robot location accurately itself, otherwise robot will not move autonomously to a particular target. Therefore, Adaptive Monte Carlo Localization (AMCL) method was used to validate the accuracy of robot localization process. Moreover, additional sensors were placed around the building to sense the prevailing security threats from intruders with the aid of the robot.

To bring a uniform development platform which seamlessly combines hardware components and software architecture of various developers across the globe and reduce the complexity in producing robots which help people in their daily ergonomics. ROS has come out to be a game changer. It is disappointing to see the lack of penetration of technology in different verticals which involve protection, defense and security. By leveraging the power of ROS in the field of robotic automation and computer vision, this research will pave path for identification of suspicious activity with autonomously moving bots which run on ROS. The research paper proposes and validates a flow where ROS and computer vision algorithms like YOLO can fall in sync with each other to provide smarter and accurate methods for indoor and limited outdoor patrolling. Identification of age,`gender, weapons and other elements which can disturb public harmony will be an integral part of the research and development process. The simulation and testing reflects the efficiency and speed of the designed software architecture.

The detection of obstacles is a fundamental issue in autonomous navigation, as it is the main key for collision prevention. This paper presents a method for the segmentation of general obstacles by stereo vision with no need of dense disparity maps or assumptions about the scenario. A sparse set of points is selected according to a local spatial condition and then clustered in function of its neighborhood, disparity values and a cost associated with the possibility of each point being part of an obstacle. The method was evaluated in hand-labeled images from KITTI object detection benchmark and the precision and recall metrics were calculated. The quantitative and qualitative results showed satisfactory in scenarios with different types of objects.