11 research outputs found
Safe open-loop strategies for handling intermittent communications in multi-robot systems
The objective of this thesis is to develop a strategy that allows robots to safely execute open-loop motion patterns for pre-computed time durations when facing interruptions in
communication. By computing the time horizon in which collisions with other robots are impossible, this method allows the robots to move safely despite having no updated
information about the environment. As the complexity of multi-robot systems increase, communication failures in the form of packet losses, saturated network channels and hardware failures are inevitable. This thesis is motivated by the need to increase the robustness of operation in the face of such failures. The advantage of this strategy is that it prevents the jerky and unpredictable motion behaviour which often plague robotic systems experiencing communication issues. To compute the safe time horizon, the first step involves constructing reachable sets around the robots to determine the set of all positions that can be reached by the robot in a given amount of time. In order to avoid complications arising from the non-convexity of these reachable sets, analytical expressions for minimum area ellipses enclosing the reachable sets are obtained. By using a fast gradient descent based technique, intersections are computed between a robot’s trajectory and the reachable sets of other robots. This information is then used to compute the safe time horizon for each robot in real time. To this end, provable safety guarantees are formulated to ensure
collision avoidance. This strategy has been verified in simulation as well as on a team of two-wheeled differential drive robots on a multi-robot testbed.M.S
Beyond Jacobian-based tasks: Extended set-based tasks for multi-task execution and prioritization
The ability of executing multiple tasks simultaneously is an important
feature of redundant robotic systems. As a matter of fact, complex behaviors
can often be obtained as a result of the execution of several tasks. Moreover,
in safety-critical applications, tasks designed to ensure the safety of the
robot and its surroundings have to be executed along with other nominal tasks.
In such cases, it is also important to prioritize the former over the latter.
In this paper, we formalize the definition of extended set-based tasks, i.e.,
tasks which can be executed by rendering subsets of the task space
asymptotically stable or forward invariant. We propose a mathematical
representation of such tasks that allows for the execution of more complex and
time-varying prioritized stacks of tasks using kinematic and dynamic robot
models alike. We present and analyze an optimization-based framework which is
computationally efficient, accounts for input bounds, and allows for the stable
execution of time-varying prioritized stacks of extended set-based tasks. The
proposed framework is validated using extensive simulations and experiments
with robotic manipulators
Local encounters in robot swarms: From localization to density regulation
In naturally occurring swarms---living as well as non-living---local proximity encounters among individuals or particles in the collective facilitate a broad range of emergent phenomena. In the context of robot swarms operating with limited sensing and communication capabilities, this thesis demonstrates how the systematic analysis of inter-robot encounters can enable the swarm to perform useful functions without the presence of a central coordinator. We combine ideas from stochastic geometry, statistical mechanics, and biology to develop mathematical models which characterize the nature and frequency of inter-robot encounters occurring in a robot swarm. These models allow the swarm to perform functions like localization, task allocation, and density regulation, while only requiring individual robots to measure the presence of other robots in the immediate vicinity---either via contact sensors or binary proximity detectors. Moreover, the resulting encounter-based algorithms require no communication among the robots or the presence of a central coordinator, and are robust to individual robot failures occurring in the swarm. Throughout the thesis, experiments conducted on real robot swarms vindicate the idea that inter-robot encounters can be advantageously leveraged by individuals in the swarm.Ph.D