Likhachev, Maxim

Email Address
ORCID
Disciplines
Research Projects
Organizational Units
Position
Introduction
Research Interests

Search Results

Now showing 1 - 3 of 3
  • Publication
    Path Clearance
    (2009-06-01) Likhachev, Maxim; Stentz, Anthony
    In military scenarios, agents (i.e., troops of soldiers, convoys, and unmanned vehicles) may often have to traverse environments with only a limited intelligence about the locations of adversaries. We study a particular instance of this problem that we refer to as path clearance problem.This article presents a survey of our work on scalable and suitable for real-time use approaches to solving the path clearance problem. In particular, in the first part of the article, we show that the path clearance problem exhibits clear preferences on uncertainty. It turns out that these clear preferences can be used to develop an efficient algorithm called probabilistic planning with clear preferences (PPCP). The algorithm is anytime usable, converges to an optimal solution under certain conditions, and scales well to large-scale problems. We briefly describe the PPCP algorithm and show how it can be used to solve the path clearance problem when no scouts are present. In the second part of the article, we show several strategies for how to use the PPCP algorithm in case multiple scouting unmanned aerial vehicles (UAVs) are available. The experimental analysis shows that planning with PPCP results in a substantially smaller execution cost than when ignoring uncertainty, and employing scouts can decrease this execution cost even further.
  • Publication
    Time-bounded Lattice for Efficient Planning in Dynamic Environments
    (2009-05-12) Kushleyev, Aleksandr; Likhachev, Maxim
    For vehicles navigating initially unknown cluttered environments, current state-of-the-art planning algorithms are able to plan and re-plan dynamically-feasible paths efficiently and robustly. It is still a challenge, however, to deal well with the surroundings that are both cluttered and highly dynamic. Planning under these conditions is more difficult for two reasons. First, tracking and predicting the trajectories of moving objects (i.e., cars, humans) is very noisy. Second, the planning process is computationally more expensive because of the increased dimensionality of the state-space, with time as an additional variable. Moreover, re-planning needs to be invoked more often since the trajectories of moving obstacles need to be constantly re-estimated. In this paper, we develop a path planning algorithm that addresses these challenges. First, we choose a representation of dynamic obstacles that efficiently models their predicted trajectories and the uncertainty associated with the predictions. Second, to provide real-time guarantees on the performance of planning with dynamic obstacles, we propose to utilize a novel data structure for planning - a time-bounded lattice - that merges together short-term planning in time with longterm planning without time. We demonstrate the effectiveness of the approach in both simulations with up to 30 dynamic obstacles and on real robots.
  • Publication
    A Reasoning Framework for Autonomous Urban Driving
    (2008-06-04) Ferguson, Dave; Baker, Christopher; Likhachev, Maxim; Dolan, John
    Urban driving is a demanding task for autonomous vehicles as it requires the development and integration of several challenging capabilities, including high-level route planning, interaction with other vehicles, complex maneuvers, and ultra-reliability. In this paper, we present a reasoning framework for an autonomous vehicle navigating through urban environments. Our approach combines route-level planning, context-sensitive local decision making, and sophisticated motion planning to produce safe, intelligent actions for the vehicle. We provide examples from an implementation on an autonomous passenger vehicle that has driven over 3000 autonomous kilometers and competed in, and won, the Urban Challenge.