3,458 research outputs found
Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns
This paper presents a real-time path planning algorithm which can guarantee
probabilistic feasibility for autonomous robots subject to process noise and an
uncertain environment, including dynamic obstacles with uncertain motion
patterns. The key contribution of the work is the
integration of a novel method for modeling dynamic obstacles with uncertain future
trajectories. The method, denoted as RR-GP, uses a learned motion pattern model
of the dynamic obstacles to make long-term predictions of their future paths. This is done by combining the
flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach,
a sampling-based reachability computation method which ensures dynamic
feasibility. This prediction model is then utilized within chance-constrained rapidly-exploring random
trees (CC-RRT), which uses chance constraints to explicitly achieve probabilistic
constraint satisfaction while maintaining the computational
benefits of sampling-based algorithms. With RR-GP embedded in the CC-RRT framework, theoretical guarantees
can be demonstrated for linear systems subject to Gaussian uncertainty,
though the extension to nonlinear systems is also considered. Simulation results
show that the resulting approach can be used in real-time to efficiently and
accurately execute safe paths
Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
To plan safe trajectories in urban environments, autonomous vehicles must be
able to quickly assess the future intentions of dynamic agents. Pedestrians are
particularly challenging to model, as their motion patterns are often uncertain
and/or unknown a priori. This paper presents a novel changepoint detection and
clustering algorithm that, when coupled with offline unsupervised learning of a
Gaussian process mixture model (DPGP), enables quick detection of changes in
intent and online learning of motion patterns not seen in prior training data.
The resulting long-term movement predictions demonstrate improved accuracy
relative to offline learning alone, in terms of both intent and trajectory
prediction. By embedding these predictions within a chance-constrained motion
planner, trajectories which are probabilistically safe to pedestrian motions
can be identified in real-time. Hardware experiments demonstrate that this
approach can accurately predict pedestrian motion patterns from onboard
sensor/perception data and facilitate robust navigation within a dynamic
environment.Comment: Submitted to 2014 International Workshop on the Algorithmic
Foundations of Robotic
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