Andrew Kwok

Unicycle Coverage Control Via Hybrid Modeling

This technical note presents gradient-descent coverage algorithms for a group of nonholonomic vehicles. Similarly to previous approaches, the deployment strategy relies on Locational Optimization techniques and algorithms are distributed in the sense of the Delaunay graph. In order to deal with unicycle dynamics and guarantee performance, we introduce several vehicle modes and integrate them in a hybrid system. We then analyze the algorithms with a recently introduced invariance principle for hybrid systems.

Energy-balancing cooperative strategies for sensor deployment

This paper proposes power-aware coverage algorithms for mobile sensor networks. In order to balance the energy expenditure across the network and make nodes with high power compensate for those with low power, we propose two modified Lloyd-like algorithms. The first limits the velocity at which each node can travel, while the second incorporates a new definition of region of dominance depending on energy content. We introduce a power aware aggregate cost function and analyze the algorithms performance with regards to it. Various simulations illustrate this performance and compare both algorithms.

A Distributed Deterministic Annealing Algorithm for Limited-Range Sensor Coverage

This paper presents a distributed coverage algorithm for a network of mobile agents. Unlike previous work that uses a simple gradient descent algorithm, here we employ an existing deterministic annealing (DA) technique to achieve more optimal convergence values since typical coverage objective functions contain many local minima. We replicate the results of the classical DA algorithm while imposing a limited-range constraint to sensors. As the temperature is decreased, phase changes lead to a regrouping of agents, which is decided through a distributed task allocation algorithm. While simple gradient descent algorithms are heavily dependent on initial conditions for such non-convex coverage objective functions, annealing techniques are generally less prone to this phenomena. The results of our simulations confirm this fact, as we show in the manuscript.

A distributed deterministic annealing algorithm for limited-range sensor coverage

This paper presents a distributed coverage algorithm for a network of mobile agents. Unlike previous work that uses a simple gradient descent algorithm, here we employ an existing deterministic annealing (DA) technique to achieve more optimal convergence values. We replicate the results of the classical DA algorithm while imposing a limited-range constraint to sensors. As the temperature is decreased, phase changes lead to a regrouping of agents, which is decided through a distributed task allocation algorithm. While simple gradient descent algorithms are heavily dependent on initial conditions, annealing techniques are generally less prone to this phenomena. The results of our simulations confirm this fact, as we show in the manuscript.

Deployment of drifters in a piecewise-constant flow environment

We study a deployment strategy for a group of vehicles that drift in an ambient flow field. Specifically, we address the case where the flow is always greater in magnitude than an individual vehicles speed, thus individual agents are not able to maintain constant positions. For analytic tractability, we make the assumption that the flow can be approximated as a piecewise constant vector field. This results in interesting phenomena regarding minimum-time optimal solutions at the interface between two different constant flows. We characterize these optimal trajectories and then specify an area maximizing cooperative coverage algorithm that agents in the flow can implement in a distributed manner.

Coverage Maximization with Autonomous Agents in Fast Flow Environments

This work examines the cooperative motion of a group of autonomous vehicles in a fast flow environment. The magnitude of the flow velocity is assumed to be greater than the available actuation to each agent. Collectively, the agents wish to maximize total coverage area defined as the set of points reachable by any agent within T time. The reachable set of an agent in a fast flow is characterized using optimal control techniques. Specifically, this work addresses the complementary cases where the static flow field is smooth, and where the flow field is piecewise constant. The latter case arises as a proposed approximation of a smooth flow that remains analytically tractable. Furthermore, the techniques used in the piecewise constant flow case enable treatment for obstacles in the environment. In both cases, a gradient ascent method is derived to maximize the total coverage area in a distributed fashion. Simulations show that such a network is able to maximize the coverage area in a fast flow.

Coverage control with unicycles via hybrid modeling

This paper presents a gradient-descent coverage algorithm for a group of nonholonomic vehicles. Similar to previous approaches, the deployment strategy relies on locational optimization techniques to assign Voronoi regions to vehicles. The algorithm is distributed in the sense of the Delaunay graph. In order to deal with unicycle dynamics and guarantee performance, we introduce several vehicle modes; i.e., forward movement, rotation in place and resting. The analysis of the algorithm relies on a recently introduced invariance principle for hybrid systems.