Rajnikant Sharma

Cooperative target-capturing with inaccurate target information

This paper presents a distributed target-centric formation control strategy for multiple unmanned aerial vehicles (UAVs) in the presence of target motion uncertainty. The formation is maintained around a target using a combination of a consensus protocol and a sliding mode control law. Consensus helps in distributing the target information which is available only to a subset of vehicles. Sliding mode control compensates for the uncertainty in the target information. Hence, collectively the combined strategy enforces each of the vehicles to maintain its respective position in the formation. We show that if at least one vehicle in a group has target information with some uncertainty and the corresponding communication graph is connected, then a target-centric formation can be maintained. The performance of the proposed strategy is illustrated through simulations.

Graph-Based Observability Analysis of Bearing-Only Cooperative Localization

In this paper, we investigate the nonlinear observability properties of bearing-only cooperative localization. We establish a link between observability and a graph that represents measurements and communication between the robots. It is shown that graph theoretic properties like the connectivity and the existence of a path between two nodes can be used to explain the observability of the system. We obtain the maximum rank of the observability matrix without global information and derive conditions under which the maximum rank can be achieved. Furthermore, we show that for complete observability, all of the nodes in the graph must have a path to at least two different landmarks of known location.

Collision avoidance between UAV clusters using swarm intelligence techniques

In this article, several basic swarming laws for Unmanned Aerial Vehicles (UAVs) are developed for both two-dimensional (2D) plane and three-dimensional (3D) space. Effects of these basic laws on the group behaviour of swarms of UAVs are studied. It is shown that when cohesion rule is applied an equilibrium condition is reached in which all the UAVs settle at the same altitude on a circle of constant radius. It is also proved analytically that this equilibrium condition is stable for all values of velocity and acceleration. A decentralised autonomous decision-making approach that achieves collision avoidance without any central authority is also proposed in this article. Algorithms are developed with the help of these swarming laws for two types of collision avoidance, Group-wise and Individual, in 2D plane and 3D space. Effect of various parameters are studied on both types of collision avoidance schemes through extensive simulations.

Cooperative navigation of MAVs In GPS denied areas

Cooperative missions for Miniature Air Vehicles (MAVs) require accurate position, velocity, and attitude estimates for all MAVs within the group for its successful completion. This paper details a cooperative methodology for MAV navigation in times of Global Positioning System (GPS) outages or in GPS denied areas. In this method, each MAV estimates position, attitude, and velocity of all MAVs in its sensor range, including itself. Each MAV collects the IMU measurements from each of the neighboring MAVs and fuses these measurements with relative range and bearing measurements taken of every MAV in its sensor range. This collected data is then used to estimate navigation states using an Extended Kalman Filter (EKF). Simulation results presented in this paper demonstrate that this Cooperative Navigation System (CNS) can effectively constrain pose estimation drift in the absence of GPS. We also performed the nonlinear observability analysis to support the improved performance of CNS.

Vehicular Platooning in an Adversarial Environment

In this paper, we show that a single, maliciously controlled vehicle can destabilize a vehicular platoon, to catastrophic effect, through local modifications to the prevailing control law. Specifically, by combining changes to the gains of the associated law with the appropriate vehicle movements, the attacker can cause the platoon to oscillate at a resonant frequency, causing accidents that could result in serious injury or death. We determine the range of gains, and their corresponding frequencies, that allow an attacker to violate the string stability and stability criteria at different positions in the platoon. Furthermore, we prove that the attack can be successful at any position in the platoon and at frequencies that can be realized by the other vehicles in the platoon. Our work implies that neither the string stability nor stability conditions, when used singly, ensure proper platoon operation, and that neither can be used to ensure the other. Finally, we show that an attacker is theoretically capable of gaining control over the individual position and velocity (states) of other vehicles in the platoon; two attacks are demonstrated for this vulnerability.

Observability-based local path planning and collision avoidance for micro air vehicles using bearing-only measurements

In this paper we detail an observability based path planning algorithm for Small and Miniature Air Vehicles (MAVs) navigating among multiple static obstacles. Bearing only measurements are utilized to estimate the time-to-collision (TTC) and bearing to obstacles using the extended Kalman filter (EKF). For the error covariance matrix computed by the EKF to be bounded, the system should be observable. We perform a nonlinear observability analysis to obtain the necessary conditions for complete observability. We use these conditions to design a path planning algorithm which simultaneously minimizes the uncertainties in state estimation while avoiding collisions with obstacles. Simulation results show that the planning algorithm successfully solves the single and multiple obstacle avoidance problems for MAVs while improving the estimation accuracy.

Reactive Path Planning for Micro Air Vehicles Using Bearing-only Measurements

Autonomous path planning of Micro Air Vehicles (MAVs) in an urban environment is a challenging task because urban environments are dynamic and have variety of obstacles, and the locations of these obstacles may not be available a priori. In this paper we develop a reactive guidance strategy for collision avoidance using bearing-only measurements. The guidance strategy can be used to avoid collision from circular obstacles and to follow straight and curved walls at safe distance. The guidance law moves a obstacle in the sensor field-of-view to a desired constant bearing angle, which causes the MAV to maintain a constant distance from the obstacle. We use sliding mode control theory to derive the guidance law, which is fast, computationally inexpensive, and guarantees collision avoidance.

Observability-based local path planning and obstacle avoidance using bearing-only measurements

In this paper we present an observability-based local path planning and obstacle avoidance technique that utilizes an extended Kalman Filter (EKF) to estimate the time-to-collision (TTC) and bearing to obstacles using bearing-only measurements. To ensure that the error covariance matrix computed by an EKF is bounded, the system should be observable. We perform a nonlinear observability analysis to obtain the necessary conditions for complete observability of the system. These conditions are used to explicitly design a path planning algorithm that enhances observability while simultaneously avoiding collisions with obstacles. We analyze the behavior of the path planning algorithm and specially define the environments where the path planning algorithm will guarantee collision-free paths that lead to a goal configuration. Numerical results show the effectiveness of the planning algorithm in solving single and multiple obstacle avoidance problems while improving the estimation accuracy.

Vision Based Distributed Cooperative Navigation for MAVs in GPS denied areas

Current Miniature Air Vehicle (MAV) systems rely on the availability of GPS to enable navigation (state estimation) during ∞ight. However, many envisioned MAV usage scenarios require MAVs that can ∞y in tightly constrained spaces (e.g., urban terrain, indoors, dense jungle, etc.) meaning that GPS may not be available. While inertial sensors can be used to estimate navigation state, their estimates will diverge over time without GPS. In this paper, we propose a method for overcoming this drift in a multiple-MAV scenario. We develop a distributed cooperative navigation system where each MAV is equipped with a bearing-only sensor (an electro-optical camera) and an inertial measurement unit. The camera is used to measure the bearing from other MAVs and/or landmarks which are in its fleld of view. By communicating local bearing information with other MAVs in their communication range, each MAV can estimate, without drift, its navigation state assuming the entire system of MAVs observes two or more landmarks at known locations. The distributed cooperative navigation system explicitly minimizes communication both in terms of message size and number of communication links. We describe su‐cient conditions for eliminating drift in the connectivity of the MAVs and how many landmarks are observed. We present simulation results to support the developed theory.

Attack Mitigation in Adversarial Platooning Using Detection-Based Sliding Mode Control

In this paper, we consider a mitigation strategy to prevent a vehicle controlled by an attacker from causing collisions in a vehicular platoon. An adversarial-aware control scheme, based on sliding mode control using only local sensor information and a decentralized attack detector, is shown to significantly reduce the number and severity of collisions, without the need for inter-vehicle or vehicle-to-infrastructure communication. Simulations demonstrate that collisions are eliminated (or significantly reduced) when the attacker and normal vehicles have same capabilities, and collisions are reduced even with more powerful attackers.