IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

In this paper, we present control and parameter estimation strategies with theoretical guarantees to turn a hex-rotor unmanned aerial vehicle (UAV) into a microgravity enabling platform. We make the UAV to maintain a constant acceleration equal to free-fall acceleration for it and any payload on-board to experience microgravity. Towards this, we derive a feedback linearisation-based acceleration control law exploiting the differential flatness property of our system. The proposed control law requires the estimates of the system parameters. Therefore, ancillary to this control law, we propose a parameter estimation scheme and prove that the proposed control law along with the parameter estimation scheme ensures convergence of acceleration to the desired value under certain conditions. We also characterize these conditions that guarantee convergence. The flight tests that we have performed employing the proposed control and parameter estimation schemes gave microgravity levels of the order of 10 - 3g for 1.6 s. To our knowledge, our hex-rotor UAV is the first multi-rotor UAV to achieve microgravity, and the first UAV—fixed-wing or rotary—to attain and maintain such levels of microgravity. © 2019, Shanghai Jiao Tong University.

Department of Aerospace Engineering

World University root node

Aerospace Systems

Acceleration control of a multi-rotor UAV towards achieving microgravity

We consider a three-agent pursuit-evasion problem involving an attacker, and a target-defender team. The goal of the attacker is to capture the target, while that of the target is to escape the capture and the goal of the defender is to intercept the attacker before the attacker reaches the target. The defender and the target cooperate such that their objective is achieved efficiently. The target-defender team do not have accurate information of the attacker and hence use Extended Kalman Filter to estimate its states. The target-defender team uses Nonlinear Model Predictive Control (NMPC) to compute their optimal control inputs, while the attacker switches between pure pursuit and proportional navigation guidance laws about which the team is unaware of. The performance of the solution obtained from the proposed NMPC formulation was evaluated through numerical simulations and hardware experiments performed using ground rovers. © 2019 American Automatic Control Council.

Proceedings of the American Control Conference

Nmpc based approach for cooperative target defence

Riding on a quadrotor in a standing position demands extreme piloting skills. However, niche tasks like certain inspection and rescue missions can be accomplished by doing so. In this paper, we propose a control scheme that uses the center of gravity (CoG) variation caused by the tilt of the pilot standing on a quadrotor to maneuver it in the horizontal plane. Keeping potential applications in mind, we design controllers for two modes of operation: A) Maneuver via leaning mode during which the quadrotor converts the forward CoG shift into a proportional forward speed, and the sideward CoG shift to a yaw rate, thus enabling a turn, and b) Remote control mode in which the quadrotor platform rejects all the disturbances including the pilot's movements and maintains the desired speed and turn commands provided by the pilot using a hand-held remote control. We use a simplified model of a human standing on a quadrotor to test the proposed control laws using simulations. The simulation results show that it is possible to control the quadrotor motion using CoG shifts caused by a pilot with moderate skills. © 2019 IEEE.

2019 6th Indian Control Conference, ICC 2019 - Proceedings

Quadrotor hoverboard

58th Israel Annual Conference on Aerospace Sciences, IACAS 2018

A bearings-only information based recovery guidance law

The motion planning problem of a single autonomous vehicle having a minimum turn radius constraint, visiting an ordered sequence of targets in an environment with polygonal obstacles, is addressed. Two types of vehicles are considered: aerial/ground vehicle - described by the Dubins/Reeds-Shepp vehicle models, respectively. The problem is posed in the form of a search tree by using the obstacles’ vertices, vehicle’s initial configuration, and the set of target points as nodes. The tree’s arcs are represented by the Dubins/Reed-Shepp paths without terminal angle constraint (relaxed paths) connecting two adjacent nodes. These relaxed paths - connecting an initial configuration and a destination, are calculated using a feedback algorithm. Due to the computational complexity of the problem a genetic algorithm is proposed. Additionally, two deterministic search algorithms are presented. A quick heuristic greedy algorithm which uses the visibility graph distances for estimating the remaining vehicle path and an exhaustive algorithm which provides optimal solution trajectories. The performance of the algorithms is demonstrated and compared through sample runs and a Monte Carlo study. Results confirm that the heuristic algorithm provides relatively good solution for a small radius turn vehicle, while the genetic algorithm offers a good trade-off between computational load and performance. © 2017, Springer Science+Business Media B.V.

Journal of Intelligent and Robotic Systems: Theory and Applications

Multi-Target Motion Planning Amidst Obstacles for Autonomous Aerial and Ground Vehicles

This paper considers a pursuit-evasion game for non-holonomic systems where a group of pursuers attempts to capture an evader in a bounded connected domain. The problem is challenging because all vehicles have the same maneuvering capability in terms of speed and turn radius constraint. The paper initially discusses a simple approach for holonomic systems that is based on the minimization of the safe-reachable area (the area containing the set of points to where an evader can travel without being caught). This idea is then extended to develop a pursuit-evasion strategy for non-holonomic systems. However, solving such a problem is computationally intractable. Therefore, we propose a computationally efficient algorithm to obtain approximate solutions. This paper also proposes an alternative approach to obtain a simple yet effective solution to the cooperative pursuit problem that is based on missile guidance laws. As there is no analytical proof of capture, we empirically evaluate the performance of the algorithms and perform a comparative study using solutions obtained from umpteen simulations. A total of four different cooperative pursuit strategies and three different evader strategies are taken into account for the comparative study. In the process, an evader strategy which is superior to that based on the optimization of safe-reachable area is also identified. © 2016, Springer Science+Business Media Dordrecht.

Cooperative Multiple Pursuers against a Single Evader

A team of unmanned aerial vehicles (UAVs) with limited communication sensing ranges, communication delays, and finite resources are deployed search and destroy targets in a given region. The targets can be static or moving.

Decentralized Multi-UAV Coalition Formation with Limited Communication Ranges

This paper considers a pursuit-evasion game for non-holonomic systems where a number of pursuers attempt to capture a single evader in a bounded connected domain. The problem is challenging because all vehicles have the same manoeuvring capability and are subject to turn radius constraints making them non-holonomic systems. The paper initially presents simple and alternate proofs for results existing in the literature that guarantee capture for holonomic systems. These results that are based on the minimization of safe-reachable area (the set of points where an evader can travel without being caught) are then extended to non-holonomic systems. However, solving such a problem exactly is computationally intractable. Therefore, the paper proposes a computationally efficient algorithm to obtain an approximate solution to the safe-reachable area minimization problem where the pursuers aim to minimize the safe-reachable area of the evader, while the evader chooses control actions to maximize it. Also proposed is an alternative approach that uses a cooperative strategy based on a pure proportional navigation law to capture the evader. In the process, an evader strategy which is superior to those based on the minimization of safe-reachable area is identified. The paper evaluates the proposed algorithms through numerical simulations. © IFAC.

IFAC Proceedings Volumes (IFAC-PapersOnline)

A cooperative pursuit-evasion game for non-holonomic systems

This paper addresses the problem of multiple unmanned aerial vehicle (UAV) rendezvous when the UAVs have to perform maneuvers to avoid collisions with other UAVs. The proposed solution consists of using velocity control and a wandering maneuver, if needed, of the UAVs based on a consensus among them on the estimated time of arrival at the point of the rendezvous. This algorithm, with a slight modification is shown to be useful in tracking stationary or slowly moving targets with a standoff distance. The proposed algorithm is simple and computationally efficient. The simulation results demonstrate the efficacy of the proposed approach. © 2012 American Society of Civil Engineers.

Journal of Aerospace Engineering

Rendezvous of multiple UAVs with collision avoidance using consensus

Unmanned aerial vehicles (UAVs) have the potential to carry resources in support of search and prosecute operations. Often to completely prosecute a target, UAVs may have to simultaneously attack the target with various resources with different capacities. However, the UAVs are capable of carrying only limited resources in small quantities, hence, a group of UAVs (coalition) needs to be assigned that satisfies the target resource requirement. The assigned coalition must be such that it minimizes the target prosecution delay and the size of the coalition. The problem of forming coalitions is computationally intensive due to the combinatorial nature of the problem, but for real-time applications computationally cheap solutions are required. In this paper, we propose decentralized sub-optimal (polynomial time) and decentralized optimal coalition formation algorithms that generate coalitions for a single target with low computational complexity. We compare the performance of the proposed algorithms to that of a global optimal solution for which we need to solve a centralized combinatorial optimization problem. This problem is computationally intensive because the solution has to (a) provide a coalition for each target, (b) design a sequence in which targets need to be prosecuted, and (c) take into account reduction of UAV resources with usage. To solve this problem we use the Particle Swarm Optimization (PSO) technique. Through simulations, we study the performance of the proposed algorithms in terms of mission performance, complexity of the algorithms and the time taken to form the coalition. The simulation results show that the solution provided by the proposed algorithms is close to the global optimal solution and requires far less computational resources.

Multiple UAV Coalitions for a Search and Prosecute Mission

Multiple UAVs are deployed to carry out a search and destroy mission in a bounded region. The UAVs have limited sensor range and can carry limited resources which reduce with use. The UAVs perform a search task to detect targets. When a target is detected which requires different type and quantities of resources to completely destroy, then a team of UAVs called as a coalition is formed to attack the target. The coalition members have to modify their route to attack the target, in the process, the search task is affected, as search and destroy tasks are coupled. The performance of the mission is a function of the search and the task allocation strategies. Therefore, for a given task allocation strategy, we need to devise search strategies that are efficient. In this paper, we propose three different search strategies namely; random search strategy, lanes based search strategy and grid based search strategy and analyze their performance through Monte-Carlo simulations. The results show that the grid based search strategy performs the best but with high information overhead. © 2010 Springer Science+Business Media B.V.

Search strategies for multiple UAV search and destroy missions

Purpose - Unmanned aerial vehicles (UAVs) have a wide variety of applications such as surveillance and search. Many of these tasks are better executed by multiple UAVs acting as a group. One of the main problems to be tackled in a high-density UAV traffic scenario is that of collision avoidance among UAVs. The purpose of this paper is to give a collision avoidance algorithm to detect and resolve the conflicts of projected path among UAVs. Design/methodology/approach - The collision avoidance algorithm developed in the paper handles multiple UAV conflicts by considering only the most imminent predicted collision and doing a maneuver to increase the line-of-sight rate to avoid that conflict. After the collision avoidance maneuver, the UAVs fly to their destinations via Dubins shortest path to minimize time to reach destination. The algorithm is tested on realistic six degree of freedom UAV models augmented with proportional-integral controllers to hold altitude, velocity, and commanded bank angles. Findings - The paper shows, through extensive simulations, that the proposed collision avoidance algorithm gives a good performance in high-density UAV traffic scenarios. The proposed collision avoidance algorithm is simple to implement and is computationally efficient. Practical implications - The algorithm developed in this paper can be easily implemented on actual UAVs. Originality/value - There are only a few works in the literature that address multiple UAV collision avoidance in very high-density traffic situations. This paper addresses very high-density multiple UAV conflict resolution with realistic UAV models. © 2011 Emerald Group Publishing Limited. All rights reserved.

Aircraft Engineering and Aerospace Technology

Reactive collision avoidance of multiple realistic UAVs

team of unmanned aerial vehicles (UAVs) with limited communication ranges, communication delays and limited resources are deployed to search and prosecute static and maneuvering targets in a region. When a UAV detects a target, depending on the target resource requirement, it has to form a coalition for prosecution. Forming a feasible coalition over a time varying network formed by the UAVs in constant flight and prosecuting a maneuvering target are difficult tasks. In this paper, we develop a mechanism to find potential coalition members over a dynamic UAV network and propose a prosecution method where the UAVs cooperate with each other and strike the target at different time instants until the target is destroyed. Simulation results are presented to show how coalitions are formed over a dynamic network with moving targets. We also carry out Monte-Carlo simulations to study the effects of communication ranges and delays. The results show that, when communication delay is less, finding potential coalition members in the network is beneficial, but when the communication delay is significant, determining coalition members from immediate neighborhood is sufficient. Copyright © 2010 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Coalition FormationWith Communication Delays and Maneuvering Targets

In this paper we study representation of KL-divergence minimization, in the cases where integer sufficient statistics exists, using tools from polynomial algebra. We show that the estimation of parametric statistical models in this case can be transformed to solving a system of polynomial equations. In particular, we also study the case of Kullback-Csisźar iteration scheme. We present implicit descriptions of these models and show that implicitization preserves specialization of prior distribution. This result leads us to a Gröbner bases method to compute an implicit representation of minimum KL-divergence models. © 2010 Springer-Verlag Berlin Heidelberg.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

An algebraic implicitization and specialization of minimum KL-divergence models

A team of unmanned aerial vehicles (UAVs) with limited communication ranges and limited resources are deployed in a region to search and destroy stationary and moving targets. When a UAV detects a target, depending on the target resource requirement, it is tasked to form a coalition over the dynamic network formed by the UAVs. In this paper, we develop a mechanism to find potential coalition members over the network using principles from internet protocol and introduce an algorithm using Particle Swarm Optimization to generate a coalition that destroys the target is minimum time. Monte-Carlo simulations are carried out to study how coalition are formed and the effects of coalition process delays. © 2010 AACC.

Proceedings of the 2010 American Control Conference, ACC 2010

Coalition formation with communication ranges and moving targets

While performing a mission, multiple Unmanned Aerial Vehicles (UAVs) need to avoid each other to prevent collisions among them. In this paper, we design a collision avoidance algorithm to resolve the conflict among UAVs that are on a collision course while flying to their respective destinations. The collision avoidance algorithm consist of each UAV that is on a collision course reactively executing a maneuver that will, as in 'inverse' Proportional Navigation (PN), increase Line of Sight (LOS) rate between them, resulting in a 'pulling out' of collision course. The algorithm is tested for high density traffic scenarios as well as for robustness in the presence of noise. © 2009 AACC.

A reactive inverse PN algorithm for collision avoidance among multiple unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have the potential to carry munitions in support of battlefield operations, however they have limited sensor range and can carry only small quantities of resources. Often, to fully prosecute a target, a variety of assets may be required, and it may be necessary to deliver these assets simultaneously. Therefore, a team of UAVs that satisfies the target resource requirement needs to be assigned to a single target, and this team is called a coalition. Other desired requirements for the coalition are (i) minimize the target prosecution delay and (ii) minimize the size of the coalition. In this paper, we propose a two-stage optimal coalition formation algorithm that assigns appropriate numbers of UAVs satisfying the desired requirements. We developed a Dubins curves based simultaneous strike scheme. Simulation results are presented to show that the two-stage coalition formation algorithm has low computational overhead and can be applied in real-time. ©2008 AACC.