We study the problem of reducing the amount of communication in decentralized target tracking. We focus on the scenario, where a team of robots is allowed to move on the boundary of the environment. Their goal is to seek a formation so as to best track a target moving in the interior of the environment. The robots are capable of measuring distances to the target. Decentralized control strategies have been proposed in the past, which guarantees that the robots asymptotically converge to the optimal formation. However, existing methods require that the robots exchange information with their neighbors at all time steps. Instead, we focus on decentralized strategies to reduce the amount of communication among robots. We propose a self-triggered communication strategy that decides when a particular robot should seek up-to-date information from its neighbors and when it is safe to operate with possibly outdated information. We prove that this strategy converges asymptotically to the desired formation when the target is stationary. For the case of a mobile target, we use a decentralized Kalman filter with covariance intersection to share the beliefs of neighboring robots. We evaluate all the approaches through simulations and a proof-of-concept experiment.
more »
« less
Multirobot Control Strategies for Collective Transport
One potential application of multirobot systems is collective transport, a task in which multiple robots collaboratively move a payload that is too large or heavy for a single robot. In this review, we highlight a variety of control strategies for collective transport that have been developed over the past three decades. We characterize the problem scenarios that have been addressed in terms of the control objective, the robot platform and its interaction with the payload, and the robots’ capabilities and information about the payload and environment. We categorize the control strategies according to whether their sensing, computation, and communication functions are performed by a centralized supervisor or specialized robot or autonomously by the robots. We provide an overview of progress toward control strategies that can be implemented on robots with expanded autonomous functionality in uncertain environments using limited information, and we suggest directions for future work on developing such controllers.
more »
« less
- Award ID(s):
- 1828010
- NSF-PAR ID:
- 10344132
- Date Published:
- Journal Name:
- Annual Review of Control, Robotics, and Autonomous Systems
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2573-5144
- Page Range / eLocation ID:
- 205 to 219
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
We study the problem of reducing the amount of communication in a distributed target tracking problem. We focus on the scenario where a team of robots are allowed to move on the boundary of the environment. Their goal is to seek a formation so as to best track a target moving in the interior of the environment. The robots are capable of measuring distances to the target. Decentralized control strategies have been proposed in the past that guarantee that the robots asymptotically converge to the optimal formation. However, existing methods require that the robots exchange information with their neighbors at all time steps. Instead, we focus on reducing the amount of communication among robots. We propose a self-triggered communication strategy that decides when a particular robot should seek up-to-date information from its neighbors and when it is safe to operate with possibly outdated information from the neighbor. We prove that this strategy converges to an optimal formation. We compare the two approaches (constant communication and self-triggered communication) through simulations of tracking stationary and mobile targets.more » « less
-
—Robots often have to perform manipulation tasks in close proximity to people (Fig 1). As such, it is desirable to use a robot arm that has limited joint torques so as to not injure the nearby person. Unfortunately, these limited torques then limit the payload capability of the arm. By using contact with the environment, robots can expand their reachable workspace that, otherwise, would be inaccessible due to exceeding actuator torque limits. We adapt our recently developed INSAT algorithm [1] to tackle the problem of torque-limited whole arm manipulation planning through contact. INSAT requires no prior over contact mode sequence and no initial template or seed for trajectory optimization. INSAT achieves this by interleaving graph search to explore the manipulator joint configuration space with incre- mental trajectory optimizations seeded by neighborhood solutions to find a dynamically feasible trajectory through contact. We demonstrate our results on a variety of manipulators and scenarios in simulation. We also experimentally show our planner exploiting robot-environment contact for the pick and place of a payload using a Kinova Gen3 robot. In comparison to the same trajectory running in free space, we experimentally show that the utilization of bracing contacts reduces the overall torque required to execute the trajectory.more » « less
-
more » « less
Abstract The placement of SMD components is usually performed with Cartesian type robots, a task known as pick-and-place (P&P). Small Selective Compliance Articulated Robot Arm (SCARA) robots are also growing in popularity for this use because of their quick and accurate performance. This paper describes the use of the Lean Robotic Micromanufacturing (LRM) framework applied on a large, 10kg payload, industrial SCARA robot for PCB assembly. The LRM framework guided the precision evaluation of the PCB assembly process and provided a prediction of the placement precision and yield. We experimentally evaluated the repeatability of the system, as well as the resulting collective errors during the assembly. Results confirm that the P&P task can achieve the required assembly tolerance of 200 microns without employing closed-loop visual servoing, therefore considerably decreasing the system complexity and assembly time.
-
null (Ed.)At the macroscale, controlling robotic swarms typically uses substantial memory, processing power, and coordination unavailable at the microscale, e.g., for colloidal robots, which could be useful for fighting disease, fabricating intelligent textiles, and designing nanocomputers. To develop principles that can leverage physical interactions and thus be used across scales, we take a two-pronged approach: a theoretical abstraction of self-organizing particle systems and an experimental robot system of active cohesive granular matter that intentionally lacks digital electronic computation and communication, using minimal (or no) sensing and control. As predicted by theory, as interparticle attraction increases, the collective transitions from dispersed to a compact phase. When aggregated, the collective can transport non-robot “impurities,” thus performing an emergent task driven by the physics underlying the transition. These results reveal a fruitful interplay between algorithm design and active matter robophysics that can result in principles for programming collectives without the need for complex algorithms or capabilities.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1146/annurev-control-042920-095844
