Consensus-Based Decentralized Auctions for Robust Task Allocation
- Han-Lim Choi, L. Brunet, J. How
- Computer ScienceIEEE Transactions on robotics
- 1 June 2009
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…
Real-Time Motion Planning With Applications to Autonomous Urban Driving
- Y. Kuwata, S. Karaman, Justin Teo, Emilio Frazzoli, J. How, Gaston A. Fiore
- Computer ScienceIEEE Transactions on Control Systems Technology
- 28 July 2009
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.
Aircraft trajectory planning with collision avoidance using mixed integer linear programming
- A. Richards, J. How
- BusinessProceedings of the American Control Conference…
- 8 May 2002
Describes a method for finding optimal trajectories for multiple aircraft avoiding collisions. Developments in spacecraft path-planning have shown that trajectory optimization including collision…
Decision Making Under Uncertainty: Theory and Application
- Mykel J. Kochenderfer, Chris Amato, J. Vian
- Computer Science
- 17 July 2015
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.
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
- Michael Everett, Yu Fan Chen, J. How
- Computer ScienceIEEE/RJS International Conference on Intelligent…
- 4 May 2018
This work extends the previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules, and introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size.
Socially aware motion planning with deep reinforcement learning
- Yu Fan Chen, Michael Everett, Miao Liu, J. How
- Computer ScienceIEEE/RJS International Conference on Intelligent…
- 26 March 2017
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.
Performance and Lyapunov Stability of a Nonlinear Path Following Guidance Method
- Sanghyuk Park, J. Deyst, J. How
- Engineering
- 1 November 2007
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…
A New Nonlinear Guidance Logic for Trajectory Tracking
- Sanghyuk Park, J. Deyst, J. How
- Engineering
- 16 August 2004
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…
Mixed integer programming for multi-vehicle path planning
- T. Schouwenaars, B. Moor, E. Feron, J. How
- Computer ScienceEuropean Control Conference
- 2001
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
- A. Hassibi, J. How, Stephen P. Boyd
- MathematicsProceedings of the American Control Conference…
- 2 June 1999
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…
...
...