Vishnu S. Chipade

Vishnu S. Chipade, Abhishek, Mangal Kothari et al.

This paper discusses the conceptual design and proof-of-concept flight demonstration of a novel variable pitch quadrotor biplane Unmanned Aerial Vehicle concept for payload delivery. The proposed design combines vertical takeoff and landing (VTOL), precise hover capabilities of a quadrotor helicopter and high range, endurance and high forward cruise speed characteristics of a fixed wing aircraft. The proposed UAV is designed for a mission requirement of carrying and delivering 6 kg payload to a destination at 16 km from the point of origin. First, the design of proprotors is carried out using a physics based modified Blade Element Momentum Theory (BEMT) analysis, which is validated using experimental data generated for the purpose. Proprotors have conflicting requirement for optimal hover and forward flight performance. Next, the biplane wings are designed using simple lifting line theory. The airframe design is followed by power plant selection and transmission design. Finally, weight estimation is carried out to complete the design process. The proprotor design with 24 deg preset angle and -24 deg twist is designed based on 70% weightage to forward flight and 30% weightage to hovering flight conditions. The operating RPM of the proprotors is reduced from 3200 during hover to 2000 during forward flight to ensure optimal performance during cruise flight. The estimated power consumption during forward flight mode is 64% less than that required for hover, establishing the benefit of this hybrid concept. A proof-of-concept scaled prototype is fabricated using commercial-off-the-shelf parts. A PID controller is developed and implemented on the PixHawk board to enable stable hovering flight and attitude tracking.

Vishnu S. Chipade, Xinyi Wang, Dimitra Panagou

In the prior literature on multi-agent area defense games, the assignments of the defenders to the attackers are done based on a cost metric associated only with the interception of the attackers. In contrast to that, this paper presents an Inter-Defender Collision-Aware Interception Strategy (IDCAIS) for defenders to intercept attackers in order to defend a protected area, such that the defender-to-attacker assignment protocol not only takes into account an interception-related cost but also takes into account any possible future collisions among the defenders on their optimal interception trajectories. In particular, in this paper, the defenders are assigned to intercept attackers using a mixed-integer quadratic program (MIQP) that: 1) minimizes the sum of times taken by defenders to capture the attackers under time-optimal control, as well as 2) helps eliminate or delay possible future collisions among the defenders on the optimal trajectories. To prevent inevitable collisions on optimal trajectories or collisions arising due to time-sub-optimal behavior by the attackers, a minimally augmented control using exponential control barrier function (ECBF) is also provided. Simulations show the efficacy of the approach.

Vishnu S. Chipade, Dimitra Panagou

This article provides extensions to existing path-velocity decomposition based time optimal trajectory planning algorithm \cite{kant1986toward} to scenarios in which agents move in 2D obstacle environment under double integrator dynamics with drag term (damped double integrator). Particularly, we extend the idea of a tangent graph \cite{liu1992path} to $\calC^1$-Tangent graph to find continuously differentiable ($\calC^1$) shortest path between any two points. $\calC^1$-Tangent graph has a continuously differentiable ($\calC^1$) path between any two nodes. We also provide analytical expressions for a near time-optimal velocity profile for an agent moving on these shortest paths under the damped double integrator with bounded acceleration.

Vishnu S. Chipade, Dimitra Panagou

This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employ a closed formation (`StringNet') of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The control design relies on the assumption that the adversarial agents remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders towards the attackers. We use a `Density-based Spatial Clustering of Application with Noise (DBSCAN)' algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. Simulations are provided to demonstrate the effectiveness of the approach.