865 research outputs found
Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing
Recent research in multi-robot exploration and mapping has focused on
sampling environmental fields, which are typically modeled using the Gaussian
process (GP). Existing information-theoretic exploration strategies for
learning GP-based environmental field maps adopt the non-Markovian problem
structure and consequently scale poorly with the length of history of
observations. Hence, it becomes computationally impractical to use these
strategies for in situ, real-time active sampling. To ease this computational
burden, this paper presents a Markov-based approach to efficient
information-theoretic path planning for active sampling of GP-based fields. We
analyze the time complexity of solving the Markov-based path planning problem,
and demonstrate analytically that it scales better than that of deriving the
non-Markovian strategies with increasing length of planning horizon. For a
class of exploration tasks called the transect sampling task, we provide
theoretical guarantees on the active sampling performance of our Markov-based
policy, from which ideal environmental field conditions and sampling task
settings can be established to limit its performance degradation due to
violation of the Markov assumption. Empirical evaluation on real-world
temperature and plankton density field data shows that our Markov-based policy
can generally achieve active sampling performance comparable to that of the
widely-used non-Markovian greedy policies under less favorable realistic field
conditions and task settings while enjoying significant computational gain over
them.Comment: 10th International Conference on Autonomous Agents and Multiagent
Systems (AAMAS 2011), Extended version with proofs, 11 page
Spline-Based Minimum-Curvature Trajectory Optimization for Autonomous Racing
We propose a novel B-spline trajectory optimization method for autonomous
racing. We consider the unavailability of sophisticated race car and race track
dynamics in early-stage autonomous motorsports development and derive methods
that work with limited dynamics data and additional conservative constraints.
We formulate a minimum-curvature optimization problem with only the spline
control points as optimization variables. We then compare the current
state-of-the-art method with our optimization result, which achieves a similar
level of optimality with a 90% reduction on the decision variable dimension,
and in addition offers mathematical smoothness guarantee and flexible
manipulation options. We concurrently reduce the problem computation time from
seconds to milliseconds for a long race track, enabling future online
adaptation of the previously offline technique.Comment: Submitted to ICRA 202
Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation
Decentralized control schemes are increasingly favored in various domains
that involve multi-agent systems due to the need for computational efficiency
as well as general applicability to large-scale systems. However, in the
absence of an explicit global coordinator, it is hard for distributed agents to
determine how to efficiently interact with others. In this paper, we present a
risk-aware decentralized control framework that provides guidance on how much
relative responsibility share (a percentage) an individual agent should take to
avoid collisions with others while moving efficiently without direct
communications. We propose a novel Control Barrier Function (CBF)-inspired risk
measurement to characterize the aggregate risk agents face from potential
collisions under motion uncertainty. We use this measurement to allocate
responsibility shares among agents dynamically and develop risk-aware
decentralized safe controllers. In this way, we are able to leverage the
flexibility of robots with lower risk to improve the motion flexibility for
those with higher risk, thus achieving improved collective safety. We
demonstrate the validity and efficiency of our proposed approach through two
examples: ramp merging in autonomous driving and a multi-agent
position-swapping game
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