2,387 research outputs found
Socially Aware Motion Planning with Deep Reinforcement Learning
For robotic vehicles to navigate safely and efficiently in pedestrian-rich
environments, it is important to model subtle human behaviors and navigation
rules (e.g., passing on the right). However, while instinctive to humans,
socially compliant navigation is still difficult to quantify due to the
stochasticity in people's behaviors. Existing works are mostly focused on using
feature-matching techniques to describe and imitate human paths, but often do
not generalize well since the feature values can vary from person to person,
and even run to run. This work notes that while it is challenging to directly
specify the details of what to do (precise mechanisms of human navigation), it
is straightforward to specify what not to do (violations of social norms).
Specifically, using deep reinforcement learning, this work develops a
time-efficient navigation policy that respects common social norms. The
proposed method is shown to enable fully autonomous navigation of a robotic
vehicle moving at human walking speed in an environment with many pedestrians.Comment: 8 page
Safety Barrier Certificates for Heterogeneous Multi-Robot Systems
This paper presents a formal framework for collision avoidance in multi-robot
systems, wherein an existing controller is modified in a minimally invasive
fashion to ensure safety. We build this framework through the use of control
barrier functions (CBFs) which guarantee forward invariance of a safe set;
these yield safety barrier certificates in the context of heterogeneous robot
dynamics subject to acceleration bounds. Moreover, safety barrier certificates
are extended to a distributed control framework, wherein neighboring agent
dynamics are unknown, through local parameter identification. The end result is
an optimization-based controller that formally guarantees collision free
behavior in heterogeneous multi-agent systems by minimally modifying the
desired controller via safety barrier constraints. This formal result is
verified in simulation on a multi-robot system consisting of both cumbersome
and agile robots, is demonstrated experimentally on a system with a Magellan
Pro robot and three Khepera III robots.Comment: 8 pages version of 2016ACC conference paper, experimental results
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Collision-aware Task Assignment for Multi-Robot Systems
We propose a novel formulation of the collision-aware task assignment (CATA)
problem and a decentralized auction-based algorithm to solve the problem with
optimality bound. Using a collision cone, we predict potential collisions and
introduce a binary decision variable into the local reward function for task
bidding. We further improve CATA by implementing a receding collision horizon
to address the stopping robot scenario, i.e. when robots are confined to their
task location and become static obstacles to other moving robots. The
auction-based algorithm encourages the robots to bid for tasks with collision
mitigation considerations. We validate the improved task assignment solution
with both simulation and experimental results, which show significant reduction
of overlapping paths as well as deadlocks
Multi-agent Collision Avoidance Using Interval Analysis and Symbolic Modelling with its Application to the Novel Polycopter
Coordination is fundamental component of autonomy when a system is defined by multiple mobile agents. For unmanned aerial systems (UAS), challenges originate from their low-level systems, such as their flight dynamics, which are often complex. The thesis begins by examining these low-level dynamics in an analysis of several well known UAS using a novel symbolic component-based framework. It is shown how this approach is used effectively to define key model and performance properties necessary of UAS trajectory control. This is demonstrated initially under the context of linear quadratic regulation (LQR) and model predictive control (MPC) of a quadcopter.
The symbolic framework is later extended in the proposal of a novel UAS platform, referred to as the ``Polycopter" for its morphing nature. This dual-tilt axis system has unique authority over is thrust vector, in addition to an ability to actively augment its stability and aerodynamic characteristics. This presents several opportunities in exploitative control design.
With an approach to low-level UAS modelling and control proposed, the focus of the thesis shifts to investigate the challenges associated with local trajectory generation for the purpose of multi-agent collision avoidance. This begins with a novel survey of the state-of-the-art geometric approaches with respect to performance, scalability and tolerance to uncertainty. From this survey, the interval avoidance (IA) method is proposed, to incorporate trajectory uncertainty in the geometric derivation of escape trajectories. The method is shown to be more effective in ensuring safe separation in several of the presented conditions, however performance is shown to deteriorate in denser conflicts.
Finally, it is shown how by re-framing the IA problem, three dimensional (3D) collision avoidance is achieved. The novel 3D IA method is shown to out perform the original method in three conflict cases by maintaining separation under the effects of uncertainty and in scenarios with multiple obstacles. The performance, scalability and uncertainty tolerance of each presented method is then examined in a set of scenarios resembling typical coordinated UAS operations in an exhaustive Monte-Carlo analysis
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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