An integrated system for perception-driven autonomy with modular robots

Abstract

The theoretical ability of modular robots to reconfigure in response to complex tasks in a priori unknown environments has frequently been cited as an advantage and remains a major motivator for work in the field.We present a modular robot system capable of autonomously completing highlevel tasks by reactively reconfiguring to meet the needs of a perceived, a priori unknown environment. The system integrates perception, high-level planning, and modular hardware, and is validated in three hardware demonstrations. Given a high-level task specification, a modular robot autonomously explores an unknown environment, decides when and how to reconfigure, and manipulates objects to complete its task.The system architecture balances distributed mechanical elements with centralized perception, planning, and control. By providing an example of how a modular robot system can be designed to leverage reactive reconfigurability in unknown environments, we have begun to lay the groundwork for modular self-reconfigurable robots to address tasks in the real world. arXiv:1709.05435v2 [cs.RO] 13 Dec 2018Modular self-reconfigurable robot (MSRR) systems are composed of repeated robot elements (called modules) that connect together to form larger robotic structures, and can self-reconfigure, changing the connective arrangement of their own modules to form different structures with different capabilities. Since the field was in its nascence, researchers have presented a vision that promised flexible, reactive systems capable of operating in unknown environments. MSRRs would be able to enter unknown environments, assess their surroundings, and self-reconfigure to take on a form suitable to the task and environment at hand [1]. Today, this vision remains a major motivator for work in the field [2].Continued research in MSRR has resulted in substantial advancement. Existing research has demonstrated MSRR self-reconfiguring, assuming interesting morphologies, and exhibiting various forms of locomotion, as well as methods for programming, controlling, and simulating modular robots [1,3,4,5,6,7,8,9,10,11,12,13,14,15]. However, achieving autonomous operation of a self-reconfigurable robot in unknown environments requires a system with the ability to explore, gather information about the environment, consider the requirements of a high-level task, select configurations whose capabilities match the requirements of task and environment, transform, and perform actions (such as manipulating objects) to complete tasks. Existing systems provide partial sets of these capabilities. Many systems have demonstrated limited autonomy, relying on beacons for mapping [16,17] and human input for high-level decision making [18,19]. Others have demonstrated swarm self-assembly to address basic tasks such as hill-climbing and gap-crossing [20,21]. While these existing systems all represent advancements, none have demonstrated fully autonomous, reactive self-reconfiguration to address high-level tasks.This paper presents a system allowing modular robots to complet...

Accomplishing high-level tasks with modular robots

“…recently, improved docking strategies for SMORES-EP have been developed that succeed about 90% of the time. [5]. Table 6 Discussion and Future Work…”

“…Properties are also used to describe the environmental conditions required for the behavior to run as expected. For example, the property p = (ObjectWeight, [2,5]) indicates that the behavior can appropriately interact with an object if its weight is between 2 and 5 module-weights. In this case, the property is a quantitative description of the environment.…”

“…Moreover, incorporation of on-board sensing will allow our system to operate autonomously in unknown environments. At the time of writing, a "brain module" has been developed that allows SMORES-EP clusters to carry an RGB-D camera and computer unit [5]. Using sensing information to decide joint values for EAP behaviors creates robust closed-loop behaviors.…”

Perception-Informed Autonomous Environment Augmentation with Modular Robots

“…These robots can self-reconfigure, rearranging their constituent modules to form different morphologies, and changing their abilities to match the needs of the task and environment [11]. Our work leverages recent systems that integrate the low-level capabilities of an MSRR system into a design library, accomplish high-level user-specified tasks by synthesizing library elements into a reactive state machine [8], and operate autonomously in unknown environments using perception tools for environment exploration and characterization [9].…”

“…Each state in the controller is labeled with a desired robot action, and each transition is labeled with perceived environment information; for example, the "climb drawer" action is specified to be any behavior from the library with properties climb in a ledge environment. In our previous framework [9], the high-level planner could choose to reconfigure the robot whenever needed to satisfy the required properties of the current action and environment.…”