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Route Planning under Uncertainty: The Canadian Traveller Problem
, 2008
"... The Canadian Traveller problem is a stochastic shortest paths problem in which one learns the cost of an edge only when arriving at one of its endpoints. The goal is to find an optimal policy that minimizes the expected cost of travel. The problem is known to be #Phard. Since there has been no sign ..."
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Cited by 22 (0 self)
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The Canadian Traveller problem is a stochastic shortest paths problem in which one learns the cost of an edge only when arriving at one of its endpoints. The goal is to find an optimal policy that minimizes the expected cost of travel. The problem is known to be #Phard. Since there has been no significant progress on approximation algorithms for several decades, we have chosen to seek out special cases for which exact solutions exist, in the hope of demonstrating techniques that could lead to further progress. Applying a mix of techniques from algorithm analysis and the theory of Markov Decision Processes, we provide efficient exact algorithms for directed acyclic graphs and (undirected) graphs of disjoint paths from source to destination with random twovalued edge costs. We also give worstcase performance analysis and experimental data for two natural heuristics.
Team cornell’s skynet: Robust perception and planning in an urban environment
 Journal of Field Robotics
, 2008
"... DARPA Urban Challenge. Skynet consists of many unique subsystems, including actuation and power distribution designed inhouse, a tightly coupled attitude and position estimator, a novel obstacle detection and tracking system, a system for augmenting position estimates with visionbased detection al ..."
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Cited by 20 (2 self)
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DARPA Urban Challenge. Skynet consists of many unique subsystems, including actuation and power distribution designed inhouse, a tightly coupled attitude and position estimator, a novel obstacle detection and tracking system, a system for augmenting position estimates with visionbased detection algorithms, a path planner based on physical vehicle constraints and a nonlinear optimization routine, and a statebased reasoning agent for obeying traffic laws. This paper describes these subsystems in detail before discussing the system’s overall performance in the National Qualifying Event and the Urban Challenge. Logged data recorded at the National Qualifying Event and the Urban Challenge are presented and used to analyze the system’s performance. C ○ 2008 Wiley Periodicals, Inc.
HighQuality Policies for the Canadian Traveler’s Problem (Extended Abstract)
, 2010
"... The Canadian Traveler’s Problem (CTP; Papadimitriou and Yannakakis 1991) is a path planning problem with imperfect information about the roadmap. We consider its stochastic version, which has drawn considerable attention from researchers in AI search (e. g., Nikolova and Karger 2008) and is closel ..."
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Cited by 19 (2 self)
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The Canadian Traveler’s Problem (CTP; Papadimitriou and Yannakakis 1991) is a path planning problem with imperfect information about the roadmap. We consider its stochastic version, which has drawn considerable attention from researchers in AI search (e. g., Nikolova and Karger 2008) and is closely related to navigation tasks in uncertain terrain considered in the robotics literature (e. g., Koenig and Likhachev 2002; Likhachev and Stentz 2006). 1The objective in the CTP is to travel from the initial location v0 to some goal location v ⋆ on a roadmap given as an undirected weighted graph with vertex set V (locations) and edge set E (roads). Complicating things, only a subset of roads W ⊆ E, called the weather, is actually traversable. The weather remains static while the agent traverses the graph. The agent does not know the weather; however, it does knows with which probability each road
Planning under Partial Observability by Classical Replanning: Theory and Experiments
"... Planning with partial observability can be formulated as a nondeterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequenc ..."
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Cited by 18 (5 self)
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Planning with partial observability can be formulated as a nondeterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequences. In this work, we develop a framework for partial observability that avoids these limitations and leads to a planner that scales up to larger problems. For this, the class of problems is restricted to those in which 1) the nonunary clauses representing the uncertainty about the initial situation are invariant, and 2) variables that are hidden in the initial situation do not appear in the body of conditional effects, which are all assumed to be deterministic. We show that such problems can be translated in linear time into equivalent fully observable nondeterministic planning problems, and that an slight extension of this translation renders the problem solvable by means of classical planners. The whole approach is sound and complete provided that in addition, the statespace is connected. Experiments are also reported. 1
Planning with uncertainty in position using highresolution maps
, 2008
"... Navigating autonomously is one of the most important problems facing outdoor mobile robots. This task is extremely difficult if no prior information is available and is trivial if perfect prior information is available and the position of the robot is precisely known. Perfect prior maps are rare, bu ..."
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Cited by 13 (2 self)
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Navigating autonomously is one of the most important problems facing outdoor mobile robots. This task is extremely difficult if no prior information is available and is trivial if perfect prior information is available and the position of the robot is precisely known. Perfect prior maps are rare, but goodquality, highresolution prior maps are increasingly available. Although the position of the robot is usually known through the use of the Global Position System (GPS), there are many scenarios in which GPS is not available, or its reliability is compromised by different types of interference such as mountains, buildings, foliage or jamming. If GPS is not available, the position estimate of the robot depends on deadreckoning alone, which drifts with time and can accrue very large errors. Most existing approaches to path planning and navigation for outdoor environments are unable to use prior maps if the position of the robot is not precisely known. Often these approaches end up performing the much harder task of navigating without prior information. This thesis addresses the problem of planning paths with uncertainty in position for large outdoor environments. The objective is to be able to reliably navigate autonomously
Ppcp: Efficient probabilistic planning with clear preferences in partiallyknown environments
 In AAAI
, 2006
"... For most realworld problems the agent operates in only partiallyknown environments. Probabilistic planners can reason over the missing information and produce plans that take into account the uncertainty about the environment. Unfortunately though, they can rarely scale up to the problems that ar ..."
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Cited by 12 (5 self)
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For most realworld problems the agent operates in only partiallyknown environments. Probabilistic planners can reason over the missing information and produce plans that take into account the uncertainty about the environment. Unfortunately though, they can rarely scale up to the problems that are of interest in realworld. In this paper, however, we show that for a certain subset of problems we can develop a very efficient probabilistic planner. The proposed planner, called PPCP, is applicable to the problems for which it is clear what values of the missing information would result in the best plan. In other words, there exists a clear preference for the actual values of the missing information. For example, in the problem of robot navigation in partiallyknown environments it is always preferred to find out that an initially unknown location is traversable rather than not. The planner we propose exploits this property by using a series of deterministic A*like searches to construct and refine a policy in anytime fashion. On the theoretical side, we show that once converged, the policy is guaranteed to be optimal under certain conditions. On the experimental side, we show the power of PPCP on the problem of robot navigation in partiallyknown terrains. The planner can scale up to very large environments with thousands of initially unknown locations. We believe that this is several orders of magnitude more unknowns than what the current probabilistic planners developed for the same problem can handle. Also, despite the fact that the problem we experimented on in general does not satisfy the conditions for the solution optimality, PPCP still produces the solutions that are nearly always optimal.
Probabilistic Planning with Clear Preferences on Missing Information
, 2008
"... For many realworld problems, environments at the time of planning are only partiallyknown. For example, robots often have to navigate partiallyknown terrains, planes often have to be scheduled under changing weather conditions, and car routefinders often have to figure out paths with only partial ..."
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Cited by 11 (4 self)
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For many realworld problems, environments at the time of planning are only partiallyknown. For example, robots often have to navigate partiallyknown terrains, planes often have to be scheduled under changing weather conditions, and car routefinders often have to figure out paths with only partial knowledge of traffic congestions. While general decisiontheoretic planning that takes into account the uncertainty about the environment is hard to scale to large problems, many such problems exhibit a special property: one can clearly identify beforehand the best (called clearly preferred) values for the variables that represent the unknowns in the environment. For example, in the robot navigation problem, it is always preferred to find out that an initially unknown location is traversable rather than not, in the plane scheduling problem, it is always preferred for the weather to remain a good flying weather, and in routefinding problem, it is always preferred for the road of interest to be clear of traffic. It turns out that the existence of the clear preferences can be used to construct an efficient planner, called PPCP (Probabilistic Planning with Clear Preferences), that solves these planning problems by running a series of deterministic lowdimensional
The MIT–Cornell Collision and Why It Happened
, 2008
"... robot “Skynet ” collided in a lowspeed accident. This accident was one of the first collisions between fullsized autonomous road vehicles. Fortunately, both vehicles went on to finish the race and the collision was thoroughly documented in the vehicle logs. This collaborative study between MIT and ..."
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robot “Skynet ” collided in a lowspeed accident. This accident was one of the first collisions between fullsized autonomous road vehicles. Fortunately, both vehicles went on to finish the race and the collision was thoroughly documented in the vehicle logs. This collaborative study between MIT and Cornell traces the confluence of events that preceded the collision and examines its root causes. A summary of robot–robot interactions during the race is presented. The logs from both vehicles are used to show the gulf between robot and humandriver behavior at close vehicle proximities. Contributing factors are shown to be (1) difficulties in sensor data association leading to an inability to detect slowmoving vehicles and phantom obstacles, (2) failure to anticipate vehicle intent, and (3) an overemphasis on lane constraints versus vehicle proximity in motion planning. Finally, we discuss approaches that could address these issues in future systems, such as intervehicle communication, vehicle detection, and prioritized motion planning. C ○ 2008 Wiley Periodicals, Inc.
Disambiguation protocols based on risk simulation
 IEEE Transactions on Systems, Man, and Cybernetics, Part A
, 2007
"... Suppose there is a need to swiftly navigate through a spatial arrangement of possibly forbidden regions, each region marked with the probability that it is indeed forbidden. In close proximity to any of these regions, you have the dynamic capability of disambiguating the region and learning for cert ..."
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Cited by 6 (5 self)
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Suppose there is a need to swiftly navigate through a spatial arrangement of possibly forbidden regions, each region marked with the probability that it is indeed forbidden. In close proximity to any of these regions, you have the dynamic capability of disambiguating the region and learning for certain whether or not the region is forbidden—only in the latter case may you proceed through that region. The central issue is how to most effectively exploit this disambiguation capability to minimize the expected length of the traversal. Regions are never entered while they are possibly forbidden and, thus, no risk is ever actually incurred. Nonetheless, for the sole purpose of deciding where to disambiguate, it may be advantageous to simulate risk, temporarily pretending that possibly forbidden regions are riskily traversable, and each potential traversal is weighted with its level of undesirability, which is a function of its traversal length and traversal risk. Introduced in this paper is the simulated risk disambiguation protocol, which has you follow along a shortest traversal—in this undesirability sense—until an ambiguous region is about to be entered; at that location, a disambiguation is performed on this ambiguous region. (The process is then repeated from
Planning with Imperfect Information
 In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS
, 2004
"... Abstract — We describe an efficient method for planning in environments for which prior maps are plagued with uncertainty. Our approach processes the map to determine key areas whose uncertainty is crucial to the planning task. It then incorporates the uncertainty associated with these areas using t ..."
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Cited by 4 (1 self)
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Abstract — We describe an efficient method for planning in environments for which prior maps are plagued with uncertainty. Our approach processes the map to determine key areas whose uncertainty is crucial to the planning task. It then incorporates the uncertainty associated with these areas using the recently developed PAO * algorithm to produce a fast, robust solution to the original planning task. I.