1,136 research outputs found
Anytime Point-Based Approximations for Large POMDPs
The Partially Observable Markov Decision Process has long been recognized as
a rich framework for real-world planning and control problems, especially in
robotics. However exact solutions in this framework are typically
computationally intractable for all but the smallest problems. A well-known
technique for speeding up POMDP solving involves performing value backups at
specific belief points, rather than over the entire belief simplex. The
efficiency of this approach, however, depends greatly on the selection of
points. This paper presents a set of novel techniques for selecting informative
belief points which work well in practice. The point selection procedure is
combined with point-based value backups to form an effective anytime POMDP
algorithm called Point-Based Value Iteration (PBVI). The first aim of this
paper is to introduce this algorithm and present a theoretical analysis
justifying the choice of belief selection technique. The second aim of this
paper is to provide a thorough empirical comparison between PBVI and other
state-of-the-art POMDP methods, in particular the Perseus algorithm, in an
effort to highlight their similarities and differences. Evaluation is performed
using both standard POMDP domains and realistic robotic tasks
Search-based optimal motion planning for automated driving
This paper presents a framework for fast and robust motion planning designed
to facilitate automated driving. The framework allows for real-time computation
even for horizons of several hundred meters and thus enabling automated driving
in urban conditions. This is achieved through several features. Firstly, a
convenient geometrical representation of both the search space and driving
constraints enables the use of classical path planning approach. Thus, a wide
variety of constraints can be tackled simultaneously (other vehicles, traffic
lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed
problem, is then used by A*-based algorithm with model predictive flavour in
order to compute the optimal motion trajectory. The algorithm takes into
account both distance and time horizons. The approach is validated within a
simulation study with realistic traffic scenarios. We demonstrate the
capability of the algorithm to devise plans both in fast and slow driving
conditions, even when full stop is required.Comment: Preprint accepted to 2018 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2018). A supplementary video is
available at https://youtu.be/D5XJ5ncSuq
An Efficient Paradigm for Feasibility Guarantees in Legged Locomotion
Developing feasible body trajectories for legged systems on arbitrary
terrains is a challenging task. Given some contact points, the trajectories for
the Center of Mass (CoM) and body orientation, designed to move the robot, must
satisfy crucial constraints to maintain balance, and to avoid violating
physical actuation and kinematic limits. In this paper, we present a paradigm
that allows to design feasible trajectories in an efficient manner. In
continuation to our previous work, we extend the notion of the 2D feasible
region, where static balance and the satisfaction of actuation limits were
guaranteed, whenever the projection of the CoM lies inside the proposed
admissible region. We here develop a general formulation of the improved
feasible region to guarantee dynamic balance alongside the satisfaction of both
actuation and kinematic limits for arbitrary terrains in an efficient manner.
To incorporate the feasibility of the kinematic limits, we introduce an
algorithm that computes the reachable region of the CoM. Furthermore, we
propose an efficient planning strategy that utilizes the improved feasible
region to design feasible CoM and body orientation trajectories. Finally, we
validate the capabilities of the improved feasible region and the effectiveness
of the proposed planning strategy, using simulations and experiments on the HyQ
robot and comparing them to a previously developed heuristic approach. Various
scenarios and terrains that mimic confined and challenging environments are
used for the validation.Comment: 17 pages, 13 figures, submitted to Transaction on Robotic
Optimization And Learning For Rough Terrain Legged Locomotion
We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds, along with the dynamic body motions required to execute them. Components within the planning framework coordinate to exchange plans, cost-to-go estimates, and \u27certificates\u27 that ensure the output of an abstract high-level planner can be realized by lower layers of the hierarchy. The burden of careful engineering of cost functions to achieve desired performance is substantially mitigated by a simple inverse optimal control technique. Robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the successful application of our approach in guiding the LittleDog quadruped robot over a variety of types of rough terrain. Other novel aspects of our past research efforts include a variety of pioneering inverse optimal control techniques as well as a system for planning using arbitrary pre-recorded robot behavior
Belief State Planning for Autonomous Driving: Planning with Interaction, Uncertain Prediction and Uncertain Perception
This thesis presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The uncertainty in the environment arises by the fact that the intentions as well as the future trajectories of the surrounding drivers cannot be measured directly but can only be estimated in a probabilistic fashion. Even the perception of objects is uncertain due to sensor noise or possible occlusions. When driving in such environments, the autonomous car must predict the behavior of the other drivers and plan safe, comfortable and legal trajectories. Planning such trajectories requires robust decision making when several high-level options are available for the autonomous car.
Current planning algorithms for automated driving split the problem into different subproblems, ranging from discrete, high-level decision making to prediction and continuous trajectory planning. This separation of one problem into several subproblems, combined with rule-based decision making, leads to sub-optimal behavior.
This thesis presents a global, closed-loop formulation for the motion planning problem which intertwines action selection and corresponding prediction of the other agents in one optimization problem. The global formulation allows the planning algorithm to make the decision for certain high-level options implicitly. Furthermore, the closed-loop manner of the algorithm optimizes the solution for various, future scenarios concerning the future behavior of the other agents. Formulating prediction and planning as an intertwined problem allows for modeling interaction, i.e. the future reaction of the other drivers to the behavior of the autonomous car.
The problem is modeled as a partially observable Markov decision process (POMDP) with a discrete action and a continuous state and observation space. The solution to the POMDP is a policy over belief states, which contains different reactive plans for possible future scenarios. Surrounding drivers are modeled with interactive, probabilistic agent models to account for their prediction uncertainty. The field of view of the autonomous car is simulated ahead over the whole planning horizon during the optimization of the policy. Simulating the possible, corresponding, future observations allows the algorithm to select actions that actively reduce the uncertainty of the world state. Depending on the scenario, the behavior of the autonomous car is optimized in (combined lateral and) longitudinal direction. The algorithm is formulated in a generic way and solved online, which allows for applying the algorithm on various road layouts and scenarios.
While such a generic problem formulation is intractable to solve exactly, this thesis demonstrates how a sufficiently good approximation to the optimal policy can be found online. The problem is solved by combining state of the art Monte Carlo tree search algorithms with near-optimal, domain specific roll-outs.
The algorithm is evaluated in scenarios such as the crossing of intersections under unknown intentions of other crossing vehicles, interactive lane changes in narrow gaps and decision making at intersections with large occluded areas. It is shown that the behavior of the closed-loop planner is less conservative than comparable open-loop planners. More precisely, it is even demonstrated that the policy enables the autonomous car to drive in a similar way as an omniscient planner with full knowledge of the scene. It is also demonstrated how the autonomous car executes actions to actively gather more information about the surrounding and to reduce the uncertainty of its belief state
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