42,284 research outputs found
Memory Augmented Control Networks
Planning problems in partially observable environments cannot be solved
directly with convolutional networks and require some form of memory. But, even
memory networks with sophisticated addressing schemes are unable to learn
intelligent reasoning satisfactorily due to the complexity of simultaneously
learning to access memory and plan. To mitigate these challenges we introduce
the Memory Augmented Control Network (MACN). The proposed network architecture
consists of three main parts. The first part uses convolutions to extract
features and the second part uses a neural network-based planning module to
pre-plan in the environment. The third part uses a network controller that
learns to store those specific instances of past information that are necessary
for planning. The performance of the network is evaluated in discrete grid
world environments for path planning in the presence of simple and complex
obstacles. We show that our network learns to plan and can generalize to new
environments
Sampling-based optimal kinodynamic planning with motion primitives
This paper proposes a novel sampling-based motion planner, which integrates
in RRT* (Rapidly exploring Random Tree star) a database of pre-computed motion
primitives to alleviate its computational load and allow for motion planning in
a dynamic or partially known environment. The database is built by considering
a set of initial and final state pairs in some grid space, and determining for
each pair an optimal trajectory that is compatible with the system dynamics and
constraints, while minimizing a cost. Nodes are progressively added to the tree
{of feasible trajectories in the RRT* by extracting at random a sample in the
gridded state space and selecting the best obstacle-free motion primitive in
the database that joins it to an existing node. The tree is rewired if some
nodes can be reached from the new sampled state through an obstacle-free motion
primitive with lower cost. The computationally more intensive part of motion
planning is thus moved to the preliminary offline phase of the database
construction at the price of some performance degradation due to gridding. Grid
resolution can be tuned so as to compromise between (sub)optimality and size of
the database. The planner is shown to be asymptotically optimal as the grid
resolution goes to zero and the number of sampled states grows to infinity
Practical application of pseudospectral optimization to robot path planning
To obtain minimum time or minimum energy trajectories for robots it is necessary to employ planning methods which adequately consider the platformās dynamic properties. A variety of sampling, graph-based or local receding-horizon optimisation methods have previously been proposed. These typically use simpliļ¬ed kino-dynamic models to avoid the signiļ¬cant computational burden of solving this problem in a high dimensional state-space. In this paper we investigate solutions from the class of pseudospectral optimisation methods which have grown in favour amongst the optimal control community in recent years. These methods have high computational efficiency and rapid convergence properties. We present a practical application of such an approach to the robot path planning problem to provide a trajectory considering the robotās dynamic properties. We extend the existing literature by augmenting the path constraints with sensed obstacles rather than predeļ¬ned analytical functions to enable real world application
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