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Consensus-Based Decentralized Auctions for Robust Task Allocation
This paper addresses task allocation to coordinate a fleet of autonomous vehicles by presenting two decentralized algorithms: the consensus-based auction algorithm (CBAA) and its generalization to
Decision Making Under Uncertainty: Theory and Application
This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective and presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance.
Performance and Lyapunov Stability of a Nonlinear Path Following Guidance Method
Performance and stability are demonstrated for a nonlinear path-following guidance method for unmanned air vehicles. The method was adapted from a pure pursuit-based path following, which has been
Real-Time Motion Planning With Applications to Autonomous Urban Driving
The proposed algorithm was at the core of the planning and control software for Team MIT's entry for the 2007 DARPA Urban Challenge, where the vehicle demonstrated the ability to complete a 60 mile simulated military supply mission, while safely interacting with other autonomous and human driven vehicles.
Mixed integer programming for multi-vehicle path planning
A new approach to fuel-optimal path planning of multiple vehicles using a combination of linear and integer programming and the framework of mixed integer/linear programming is well suited for path planning and collision avoidance problems.
A path-following method for solving BMI problems in control
We present a path-following (homotopy) method for (locally) solving bilinear matrix inequality (BMI) problems in control. The method is to linearize the BMI using a first order perturbation
A New Nonlinear Guidance Logic for Trajectory Tracking
A new nonlinear guidance logic, that has demonstrated superior performance in guiding unmanned air vehicles (UAVs) on curved trajectories, is presented. The logic approximates a
Spacecraft Formation Flying: Dynamics, Control and Navigation
Space agencies are now realizing that much of what has previously been achieved using hugely complex and costly single platform projects-large unmanned and manned satellites (including the present
Socially aware motion planning with deep reinforcement learning
Using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms and is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.
Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning
This work presents a decentralized multiagent collision avoidance algorithm based on a novel application of deep reinforcement learning, which effectively offloads the online computation (for predicting interaction patterns) to an offline learning procedure.