96 research outputs found
Efficient Multi-Agent Motion Planning in Continuous Workspaces Using Medial-Axis-Based Swap Graphs
We present an algorithm for homogeneous, labeled, and disk-shaped multi-agent
motion planning in continuous workspaces with arbitrarily-shaped obstacles. Our
method consists of two steps. First, we convert the continuous free space into
a discrete graph where agents are placed on vertices and move along edges. On
the graph, a set of swap operations are defined and we ensure that performing
these swap operations will not lead to collisions between agents or with
obstacles. Second, we prove that it is possible for agents' locations to be
arbitrarily permuted on graph vertices using our swap operations, as long as
these graph vertices are not fully occupied. In other words, a multi-agent
motion planning problem on our graph is always solvable. Finally, we show that
such continuous-to-discrete conversion can be performed efficiently with the
help of a medial axis analysis and can be performed robustly for workspaces
with arbitrarily-shaped obstacles. Moreover, the resulting graph has many
vertices and can accommodate a large number of densely packed agents (up to
of the volume of free space), and motion plans can be computed
faster using our swap operations compared to state-of-the-art
methods
Custom optimization algorithms for efficient hardware implementation
The focus is on real-time optimal decision making with application in advanced control
systems. These computationally intensive schemes, which involve the repeated solution of
(convex) optimization problems within a sampling interval, require more efficient computational
methods than currently available for extending their application to highly dynamical
systems and setups with resource-constrained embedded computing platforms.
A range of techniques are proposed to exploit synergies between digital hardware, numerical
analysis and algorithm design. These techniques build on top of parameterisable
hardware code generation tools that generate VHDL code describing custom computing
architectures for interior-point methods and a range of first-order constrained optimization
methods. Since memory limitations are often important in embedded implementations we
develop a custom storage scheme for KKT matrices arising in interior-point methods for
control, which reduces memory requirements significantly and prevents I/O bandwidth
limitations from affecting the performance in our implementations. To take advantage of
the trend towards parallel computing architectures and to exploit the special characteristics
of our custom architectures we propose several high-level parallel optimal control
schemes that can reduce computation time. A novel optimization formulation was devised
for reducing the computational effort in solving certain problems independent of the computing
platform used. In order to be able to solve optimization problems in fixed-point
arithmetic, which is significantly more resource-efficient than floating-point, tailored linear
algebra algorithms were developed for solving the linear systems that form the computational
bottleneck in many optimization methods. These methods come with guarantees
for reliable operation. We also provide finite-precision error analysis for fixed-point implementations
of first-order methods that can be used to minimize the use of resources while
meeting accuracy specifications. The suggested techniques are demonstrated on several
practical examples, including a hardware-in-the-loop setup for optimization-based control
of a large airliner.Open Acces
LIPIcs, Volume 248, ISAAC 2022, Complete Volume
LIPIcs, Volume 248, ISAAC 2022, Complete Volum
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