228 research outputs found
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Developing a safe and efficient collision avoidance policy for multiple
robots is challenging in the decentralized scenarios where each robot generate
its paths without observing other robots' states and intents. While other
distributed multi-robot collision avoidance systems exist, they often require
extracting agent-level features to plan a local collision-free action, which
can be computationally prohibitive and not robust. More importantly, in
practice the performance of these methods are much lower than their centralized
counterparts.
We present a decentralized sensor-level collision avoidance policy for
multi-robot systems, which directly maps raw sensor measurements to an agent's
steering commands in terms of movement velocity. As a first step toward
reducing the performance gap between decentralized and centralized methods, we
present a multi-scenario multi-stage training framework to find an optimal
policy which is trained over a large number of robots on rich, complex
environments simultaneously using a policy gradient based reinforcement
learning algorithm. We validate the learned sensor-level collision avoidance
policy in a variety of simulated scenarios with thorough performance
evaluations and show that the final learned policy is able to find time
efficient, collision-free paths for a large-scale robot system. We also
demonstrate that the learned policy can be well generalized to new scenarios
that do not appear in the entire training period, including navigating a
heterogeneous group of robots and a large-scale scenario with 100 robots.
Videos are available at https://sites.google.com/view/drlmac
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
Cooperative Collision Avoidance in Mobile Robots using Dynamic Vortex Potential Fields
In this paper, the collision avoidance problem for non-holonomic robots
moving at constant linear speeds in the 2-D plane is considered. The maneuvers
to avoid collisions are designed using dynamic vortex potential fields (PFs)
and their negative gradients; this formulation leads to a reciprocal behaviour
between the robots, denoted as being cooperative. The repulsive field is
selected as a function of the velocity and position of a robot relative to
another and introducing vorticity in its definition guarantees the absence of
local minima. Such a repulsive field is activated by a robot only when it is on
a collision path with other mobile robots or stationary obstacles. By analysing
the kinematics-based engagement dynamics in polar coordinates, it is shown that
a cooperative robot is able to avoid collisions with non-cooperating robots,
such as stationary and constant velocity robots, as well as those actively
seeking to collide with it. Conditions on the PF parameters are identified that
ensure collision avoidance for all cases. Experimental results acquired using a
mobile robot platform support the theoretical contributions
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