Ayan Dutta

ModRED: Hardware design and reconfiguration planning for a high dexterity modular self-reconfigurable robot for extra-terrestrial exploration

Abstract This paper presents a homogeneous modular robot system design based on four per-module degrees of freedom (DOF), including a prismatic DOF to increase the versatility of its reconfiguration and locomotion capabilities. The ModRED (Modular Robot for Exploration and Discovery) modules are developed with rotary-plate genderless single sided docking mechanisms (RoGenSiD) that allow chain-type configurations and lead towards hybrid-type configurations. Various locomotion gaits are simulated through the Webots robot simulator and implemented in the real ModRED system. This work also addresses the problem of dynamic reconfiguration in a modular self-reconfigurable robot (MSR). The self-reconfiguration problem is modeled as an instance of the graph-based coalition formation problem. We formulate the problem as a linear program that finds the “best” partition or coalition structure among a set of ModRED modules. The technique is verified experimentally for a variety of settings on an accurately simulated model of the ModRED robot within the Webots robot simulator. Our experimental results show that our technique can find the best partition with a reasonably low computational overhead.

Circle formation by asynchronous fat robots with limited visibility

This paper proposes a distributed algorithm for circle formation by multiple autonomous mobile robots. The vision of each robot is limited to a maximum distance. The robots do not store past actions or records of past data. They are anonymous and cannot be distinguished by their appearances. All robots agree on a common origin and axes. Earlier works report algorithms for gathering of multiple autonomous mobile robots in limited visibility considering the robots to be dimensionless or points. This paper models a robot as a unit disc (fat robot). The algorithm presented in this paper also assures that there is no collision among the robots. The robots do not share a common clock. They execute the algorithm asynchronously.

Circle Formation by Asynchronous Transparent Fat Robots

This paper proposes a distributed algorithm for circle formation by a system of mobile robots. Each robot observes the positions of other robots and moves to a new position. Eventually they form a circle. The robots do not store past actions. They are anonymous and cannot be distinguished by their appearance and do not have a common coordinate system (origin and axis) and chirality (common handedness). Most of the earlier works assume the robots to be dimensionless (points). In this paper a robot is represented as a unit disc (fat robot). The robots are assumed to be transparent in order to achieve full visibility. However, a robot is considered as a physical obstacle for another robot. The robots execute the algorithm asynchronously.

Coordination of Modular Robots by Means of Topology Discovery and Leader Election: Improvement of the Locomotion Case

An important aspect of successful locomotion in Modular Self-reconfigurable Robots (MSRs) is to be able to autonomously coordinate the movement of the modules so that the robot can move towards the goal. We consider the locomotion problem in a partially distributed setting where multiple MSRs (disconnected groups of connected modules) are within the communication range of each other and modules do not have a priori information about other modules that belong to the same configuration. Coordinating the movement of modules in such a setting becomes a challenging problem because of the limited perception and computation resources available on each module. To address these problems, we propose a strategy that first combines neighbor-to-neighbor message passing techniques via infrared and wireless communication to enable each module to autonomously determine the set of modules that belong to the same MSR. The strategy then uses a distributed leader election algorithm to identify the leader, which thereafter coordinates the actions of the modules in its configuration. We have verified the performance of our approach using an accurately simulated model of the ModRED MSR within the Webots simulator and in the embedded system of ModRED (This work was done as part of the ModRED project which is supported by NASA EPSCoR grant no. NNH11ZHA003C.). It is shown that our strategy can successfully determine the set of connected modules, elect a leader for each configuration and coordinate the locomotion of MSRs for different numbers of modules.

A Fast Coalition Structure Search Algorithm for Modular Robot Reconfiguration Planning under Uncertainty

We consider the problem of reconfiguration planning in modular robots. Current techniques for reconfiguration planning usually specify the destination configuration for a modular robot explicitly.We posit that in uncertain environments the desirable configuration for a modular robot is not known beforehand and has to be determined dynamically. In this paper, we consider this problem of how to identify a new ‘best’ configuration when a modular robot is unable to continue operating efficiently in its current configuration.We build on a technique that enumerates all the possible partitions of a set of modules requiring reconfiguring as a coalition structure graph (CSG) and finds the ‘best’ node in that graph. We propose a new data structure called an uncertain CSG (UCSG) that augments the CSG to handle uncertainty originating from the motion and performance of the robot. We then propose a new search algorithm called search UCSG that intelligently prunes nodes from the UCSG using a modified branch and bound technique. Experimental results show that our algorithm is able to find a node that is within a worst bound of 80% of the optimal or best node in the UCSG while exploring only half the nodes in the UCSG. The time taken by our algorithm in terms of the number of nodes explored is also consistently lower than existing algorithms (that do not model uncertainty) for searching a CSG.

SearchUCSG: A fast coalition structure search algorithm for modular robot reconfiguration under uncertainty

We consider the problem of dynamic reconfiguration by modular self-reconfigurable robots (MSRs) in the presence of uncertainty in their motion and the environment. Specifically, we consider the situation where the MSR is unable to continue its motion in its current configuration and needs to identify a new configuration among the existing modules, which would be the most configuration suitable for performing the robot’s assigned task under the current circumstances. To address this problem, we propose a new data structure called an uncertain coalition structure graph (UCSG) that accommodates uncertainty in the MSR’s motion and the environment, using a framework from cooperative game theory called the coalition structure graph. We then propose a new search algorithm called searchUCSG that intelligently prunes nodes from the UCSG using a modified branch-and-bound technique. We have shown analytically that our algorithm is anytime, that is, if it terminates arbitrarily, it returns the best solution found thus far, which is guaranteed to be within a constant bound from the optimal solution. We have verified the performance of our algorithm experimentally in simulation and shown that it is able to find a solution that is within the worst bound of 80% of the optimal solution while exploring only half of the nodes in the UCSG. Our algorithm also takes lesser computation time than the existing algorithms (that do not model uncertainty) for solving similar problems. Finally, to verify the operation of our algorithm, we have implemented it to partition a set of mobile e-puck robots into clusters and shown how different number of robots and different robot motion uncertainty parameters affect the formed clusters.

Distributed Adaptive Locomotion Learning in ModRED Modular Self-reconfigurable Robot

We study the problem of adaptive locomotion learning for modular self-reconfigurable robots (MSRs). MSRs are mostly used in unknown and difficult-to-navigate environments where they can take a completely new shape to accomplish the current task at hand. Therefore it is almost impossible to develop the control sequences for all possible configurations with varying shape and size. The modules have to learn and adapt their locomotion in dynamic time to be more robust in nature. In this paper, we propose a Q-learning based locomotion adaptation strategy which balances exploration versus exploitation in a more sophisticated fashion. We have applied our proposed strategy mainly on the ModRED modular robot within the Webots simulator environment. To show the generalizability of our approach, we have also applied it on a Yamor modular robot. Experimental results show that our proposed technique outperforms a random locomotion strategy and it is able to adapt to module failures.

Ensemble Learning With Weak Classifiers for Fast and Reliable Unknown Terrain Classification Using Mobile Robots

We propose a lightweight and fast learning algorithm for classifying the features of an unknown terrain that a robot is navigating in. Most of the existing research on unknown terrain classification by mobile robots relies on a single powerful classifier to correctly identify the terrain using sensor data from a single sensor like laser or camera. In contrast, our proposed approach uses multiple modalities of sensed data and multiple, weak but less-complex classifiers for classifying the terrain types. The classifiers are combined using an ensemble learning algorithm to improve the algorithm’s training rate as compared to an individual classifier. Our algorithm was tested with data collected by navigating a four-wheeled, autonomous robot, called Explorer, over different terrains including brick, grass, rock, sand, and concrete. Our results show that our proposed approach performs better with up to 63% better prediction accuracy for some terrains as compared to a support vector machine (SVM)-based learning technique that uses sensor data from a single sensor. Despite using multiple classifiers, our algorithm takes only a fraction (1/65) of the time on average, as compared to the SVM technique.

A Bottom-Up Search Algorithm for Dynamic Reformation of Agent Coalitions under Coalition Size Constraints

We consider the problem of dynamic coalition formation among a set of agents where the value function of the agents is constrained by the size of a coalition - larger coalitions are able to get higher value but only up to a certain fixed coalition size, denoted by n_max. The objective of the coalition formation problem is to determine a partition of the agent set that gives the highest utility to the agents. This problem is non-trivial as the set of partitions that needs to be explored grows exponentially with the number of agents and an exhaustive search in the space of partitions makes the problem intractable. To address this problem, we first provide a formal framework to model the coalition formation problem using a coalition structure graph (CSG). We then propose a branch and bound based algorithm called bottom Up CSG Search that searches for the optimal partitions or coalition structures among the nodes of CSG while pruning nodes that are not going to lead to the optimal coalition structure. We have provided analytical results related to the completeness, anytime-nature and time complexity of our algorithm. We have also verified the performance of our algorithm for a dynamic reformation problem where a set of physical e-puck robots starting from arbitrary positions form sub-teams that maximize their utility. Our experimental results show that our proposed algorithm performs better in terms of number of nodes generated and the time required to find the optimal coalition structure or partition than existing CSG-search algorithms.

Distributed configuration formation with modular robots using (sub)graph isomorphism-based approach

We consider the problem of configuration formation in modular robot systems where a set of modules that are initially in different configurations and located at different locations are required to assume appropriate positions so that they can get into a new, user-specified, target configuration. We propose a novel algorithm based on graph isomorphism, where the modules select locations or spots in the target configuration using a utility-based framework, while retaining their original configuration to the greatest extent possible, to reduce the time and energy required by the modules to assume the target configuration. We have shown analytically that our proposed algorithm is complete and guarantees a Pareto-optimal allocation. Experimental simulations of our algorithm with different number of modules in different initial configurations and located initially at different locations, show that the planning time of our algorithm is nominal (order of msec for 100 modules). We have also compared our algorithm against a market-based allocation algorithm and shown that our proposed algorithm performs better in terms of time and number of messages exchanged.