In this work, we present a decentralized motion planning and control architecture for the cooperative loading task using heterogeneous robotic agents operating in a cluttered workspace with static obstacles. Initially, we tackle the problem of calculating a set of feasible loading configurations via a Probabilistic Road Maps technique. Next, an optimal loading configuration is selected considering the connectivity of the space and the Euclidean distance between the robotic agents. A motion control scheme for each agent is designed and implemented in order to autonomously guide each robot to the desired loading configuration with guaranteed obstacle avoidance and convergence properties. The performance and the applicability of the proposed strategy is experimentally verified in a variety of loading scenarios using a redundant static manipulator and a mobile platform.

This work addresses the problem of robot navigation under timed temporal specifications in workspaces cluttered with obstacles. We propose a hybrid control strategy that guarantees the accomplishment of a high-level specification expressed as a timed temporal logic formula, while preserving safety (i.e., obstacle avoidance) of the system. In particular, we utilize a motion controller that achieves safe navigation inside the workspace in predetermined time, thus allowing us to abstract the motion of the agent as a finite timed transition system among certain regions of interest. Next, we employ standard formal verification and convex optimization techniques to derive high-level timed plans that satisfy the agent's specifications. A simulation study illustrates and clarifies the proposed scheme.

In this work, a hybrid control scheme is presented in order to address the navigation problem for a planar robotic platform of arbitrary shape that is moving inside an obstacle cluttered workspace. Given an initial and desired robot configuration, we propose a methodology based on approximate configuration space decomposition techniques that makes use of heuristics to adaptively refine a partition of the configuration space into non-overlapping, adjacent slices. Furthermore, we employ appropriate workspace transformations and adaptive potential field based control laws that integrate elegantly with the type of configuration space representation used, in order to safely navigate within a given cell and successfully cross over to the next, for almost all initial configurations, until the desired configuration is reached. Finally, we present simulation results that demonstrate the efficacy of the proposed control scheme.

Artificial Potential Fields (APFs) constitute an intuitive tool for designing autonomous robot navigation control schemes, though they generally suffer from the existence of local minima which may trap the robot away from its desired configuration, an issue usually addressed by appropriate offline “tuning” of the potential field's parameters. On the other side, most APF based approaches rely on a diffeomorphism to sphere worlds to handle realistic scenarios, which may be either costly to compute (e.g., conformal mappings) or requires some sort of preconditioning of the workspace (e.g., decomposition of complex geometries to simple elementary components). In this work, we first propose a constructive procedure to map multiply connected compact 2D workspaces to one or more punctured disks based on harmonic maps. Subsequently, we design an APF based control scheme along with an adaptive law for its parameters that requires no offline tuning to guarantee safe convergence to its goal configuration. Finally, an extensive simulation study is conducted to demonstrate the efficacy of the proposed control scheme.

In this paper, we consider a networked multi-robot system operating in an obstacle populated planar workspace under a single leader-multiple followers architecture. We propose a decentralized reconfiguration strategy of the set of connectivity and formation specifications that assures convergence to the desired point, while guaranteeing global connectivity. In particular, we construct a low-level Decentralized Navigation Functions based controller that encodes the goals and safety requirements of the system. However, owing to topological obstructions, stable critical points other than the desired one may appear. In such case, we employ a high-level distributed discrete procedure which attempts to solve a Distributed Constraint Satisfaction Problem on a local Voronoi partition, providing the necessary reconfiguration for the system to progress towards its goal. Eventually, we show that the system either converges to the desired point or attains a tree configuration with respect to the formation topology, in which case the system switches to a novel controller based on the Prescribed Performance technique, that eventually guarantees convergence. Finally, a simulation study clarifies and verifies the approach.