iHERO: Interactive Human-oriented Exploration and Supervision Under Scarce Communication

Exploration of unknown scenes before human entry is essential for safety and efficiency in numerous scenarios, e.g., subterranean exploration, reconnaissance, search and rescue missions. Fleets of autonomous robots are particularly suitable for this task, via concurrent exploration, multi-sensory perception and autonomous navigation. Communication however among the robots can be severely restricted to only close-range exchange via ad-hoc networks. Although some recent works have addressed the problem of collaborative exploration under restricted communication, the crucial role of the human operator has been mostly neglected. Indeed, the operator may: (i) require timely update regarding the exploration progress and fleet status; (ii) prioritize certain regions; and (iii) dynamically move within the explored area; To facilitate these requests, this work proposes an interactive human-oriented online coordination framework for collaborative exploration and supervision under scarce communication (iHERO). The robots switch smoothly and optimally among fast exploration, intermittent exchange of map and sensory data, and return to the operator for status update. It is ensured that these requests are fulfilled online interactively with a pre-specified latency. Extensive large-scale human-in-the-loop simulations and hardware experiments are performed over numerous challenging scenes, which signify its performance such as explored area and efficiency, and validate its potential applicability to real-world scenarios.

Collaborative Planar Pushing of Polytopic Objects with Multiple Robots in Complex Scenes

Pushing is a simple yet effective skill for robots to interact with and further change the environment. Related work has been mostly focused on utilizing it as a non-prehensile manipulation primitive for a robotic manipulator. However, it can also be beneficial for low-cost mobile robots that are not equipped with a manipulator. This work tackles the general problem of controlling a team of mobile robots to push collaboratively polytopic objects within complex obstacle-cluttered environments. It incorporates several characteristic challenges for contact-rich tasks such as the hybrid switching among different contact modes and under-actuation due to constrained contact forces. The proposed method is based on hybrid optimization over a sequence of possible modes and the associated pushing forces, where (i) a set of sufficient modes is generated with a multi-directional feasibility estimation, based on quasi-static analyses for general objects and any number of robots; (ii) a hierarchical hybrid search algorithm is designed to iteratively decompose the navigation path via arch segments and select the optimal parameterized mode; and (iii) a nonlinear model predictive controller is proposed to track the desired pushing velocities adaptively online for each robot. The proposed framework is complete under mild assumptions. Its efficiency and effectiveness are validated in high-fidelity simulations and hardware experiments. Robustness to motion and actuation uncertainties is also demonstrated.

Uncertainty-bounded Active Monitoring of Unknown Dynamic Targets in Road-networks with Minimum Fleet

Fleets of unmanned robots can be beneficial for the long-term monitoring of large areas, e.g., to monitor wild flocks, detect intruders, search and rescue. Monitoring numerous dynamic targets in a collaborative and efficient way is a challenging problem that requires online coordination and information fusion. The majority of existing works either assume a passive all-to-all observation model to minimize the summed uncertainties over all targets by all robots, or optimize over the jointed discrete actions while neglecting the dynamic constraints of the robots and unknown behaviors of the targets. This work proposes an online task and motion coordination algorithm that ensures an explicitly-bounded estimation uncertainty for the target states, while minimizing the average number of active robots. The robots have a limited-range perception to actively track a limited number of targets simultaneously, of which their future control decisions are all unknown. It includes: (i) the assignment of monitoring tasks, modeled as a flexible size multiple vehicle routing problem with time windows (m-MVRPTW), given the predicted target trajectories with uncertainty measure in the road-networks; (ii) the nonlinear model predictive control (NMPC) for optimizing the robot trajectories under uncertainty and safety constraints. It is shown that the robots can switch between active and inactive roles dynamically online as required by the unknown monitoring task. The proposed methods are validated via large-scale simulations of up to $100$ robots and targets.

Multi-agent Coordination Under Temporal Logic Tasks and Team-Wise Intermittent Communication

Multi-agent systems outperform single agent in complex collaborative tasks. However, in large-scale scenarios, ensuring timely information exchange during decentralized task execution remains a challenge. This work presents an online decentralized coordination scheme for multi-agent systems under complex local tasks and intermittent communication constraints. Unlike existing strategies that enforce all-time or intermittent connectivity, our approach allows agents to join or leave communication networks at aperiodic intervals, as deemed optimal by their online task execution. This scheme concurrently determines local plans and refines the communication strategy, i.e., where and when to communicate as a team. A decentralized potential game is modeled among agents, for which a Nash equilibrium is generated iteratively through online local search. It guarantees local task completion and intermittent communication constraints. Extensive numerical simulations are conducted against several strong baselines.

Multi-UAV Deployment in Obstacle-Cluttered Environments with LOS Connectivity

A reliable communication network is essential for multiple UAVs operating within obstacle-cluttered environments, where limited communication due to obstructions often occurs. A common solution is to deploy intermediate UAVs to relay information via a multi-hop network, which introduces two challenges: (i) how to design the structure of multi-hop networks; and (ii) how to maintain connectivity during collaborative motion. To this end, this work first proposes an efficient constrained search method based on the minimum-edge RRT$^\star$ algorithm, to find a spanning-tree topology that requires a less number of UAVs for the deployment task. To achieve this deployment, a distributed model predictive control strategy is proposed for the online motion coordination. It explicitly incorporates not only the inter-UAV and UAV-obstacle distance constraints, but also the line-of-sight (LOS) connectivity constraint. These constraints are well-known to be nonlinear and often tackled by various approximations. In contrast, this work provides a theoretical guarantee that all agent trajectories are ensured to be collision-free with a team-wise LOS connectivity at all time. Numerous simulations are performed in 3D valley-like environments, while hardware experiments validate its dynamic adaptation when the deployment position changes online.

Tackling the Curse of Dimensionality in Large-scale Multi-agent LTL Task Planning via Poset Product

Linear Temporal Logic (LTL) formulas have been used to describe complex tasks for multi-agent systems, with both spatial and temporal constraints. However, since the planning complexity grows exponentially with the number of agents and the length of the task formula, existing applications are mostly limited to small artificial cases. To address this issue, a new planning algorithm is proposed for task formulas specified as sc-LTL formulas. It avoids two common bottlenecks in the model-checking-based planning methods, i.e., (i) the direct translation of the complete task formula to the associated B\"uchi automaton; and (ii) the synchronized product between the B\"uchi automaton and the transition models of all agents. In particular, each conjuncted sub-formula is first converted to the associated R-posets as an abstraction of the temporal dependencies among the subtasks. Then, an efficient algorithm is proposed to compute the product of these R-posets, which retains their dependencies and resolves potential conflicts. Furthermore, the proposed approach is applied to dynamic scenes where new tasks are generated online. It is capable of deriving the first valid plan with a polynomial time and memory complexity w.r.t. the system size and the formula length. Our method can plan for task formulas with a length of more than 60 and a system with more than 35 agents, while most existing methods fail at the formula length of 20. The proposed method is validated on large fleets of service robots in both simulation and hardware experiments.

Hybrid and Oriented Harmonic Potentials for Safe Task Execution in Unknown Environment

Harmonic potentials provide globally convergent potential fields that are provably free of local minima. Due to its analytical format, it is particularly suitable for generating safe and reliable robot navigation policies. However, for complex environments that consist of a large number of overlapping non-sphere obstacles, the computation of associated transformation functions can be tedious. This becomes more apparent when: (i) the workspace is initially unknown and the underlying potential fields are updated constantly as the robot explores it; (ii) the high-level mission consists of sequential navigation tasks among numerous regions, requiring the robot to switch between different potentials. Thus, this work proposes an efficient and automated scheme to construct harmonic potentials incrementally online as guided by the task automaton. A novel two-layer harmonic tree (HT) structure is introduced that facilitates the hybrid combination of oriented search algorithms for task planning and harmonic-based navigation controllers for non-holonomic robots. Both layers are adapted efficiently and jointly during online execution to reflect the actual feasibility and cost of navigation within the updated workspace. Global safety and convergence are ensured both for the high-level task plan and the low-level robot trajectory. Known issues such as oscillation or long-detours for purely potential-based methods and sharp-turns or high computation complexity for purely search-based methods are prevented. Extensive numerical simulation and hardware experiments are conducted against several strong baselines.

Accelerated K-Serial Stable Coalition for Dynamic Capture and Resource Defense

Coalition is an important mean of multi-robot systems to collaborate on common tasks. An effective and adaptive coalition strategy is essential for the online performance in dynamic and unknown environments. In this work, the problem of territory defense by large-scale heterogeneous robotic teams is considered. The tasks include surveillance, capture of dynamic targets, and perimeter defense over valuable resources. Since each robot can choose among many tasks, it remains a challenging problem to coordinate jointly these robots such that the overall utility is maximized. This work proposes a generic coalition strategy called K-serial stable coalition algorithm (KS-COAL). Different from centralized approaches, it is distributed and anytime, meaning that only local communication is required and a K-serial Nash-stable solution is ensured. Furthermore, to accelerate adaptation to dynamic targets and resource distribution that are only perceived online, a heterogeneous graph attention network (HGAN)-based heuristic is learned to select more appropriate parameters and promising initial solutions during local optimization. Compared with manual heuristics or end-to-end predictors, it is shown to both improve online adaptability and retain the quality guarantee. The proposed methods are validated rigorously via large-scale simulations with hundreds of robots, against several strong baselines including GreedyNE and FastMaxSum.

Combinatorial-hybrid Optimization for Multi-agent Systems under Collaborative Tasks

Multi-agent systems can be extremely efficient when working concurrently and collaboratively, e.g., for transportation, maintenance, search and rescue. Coordination of such teams often involves two aspects: (i) selecting appropriate sub-teams for different tasks; (ii) designing collaborative control strategies to execute these tasks. The former aspect can be combinatorial w.r.t. the team size, while the latter requires optimization over joint state-spaces under geometric and dynamic constraints. Existing work often tackles one aspect by assuming the other is given, while ignoring their close dependency. This work formulates such problems as combinatorial-hybrid optimizations (CHO), where both the discrete modes of collaboration and the continuous control parameters are optimized simultaneously and iteratively. The proposed framework consists of two interleaved layers: the dynamic formation of task coalitions and the hybrid optimization of collaborative behaviors. Overall feasibility and costs of different coalitions performing various tasks are approximated at different granularities to improve the computational efficiency. At last, a Nash-stable strategy for both task assignment and execution is derived with provable guarantee on the feasibility and quality. Two non-trivial applications of collaborative transportation and dynamic capture are studied against several baselines.

The e-Bike Motor Assembly: Towards Advanced Robotic Manipulation for Flexible Manufacturing

Robotic manipulation is currently undergoing a profound paradigm shift due to the increasing needs for flexible manufacturing systems, and at the same time, because of the advances in enabling technologies such as sensing, learning, optimization, and hardware. This demands for robots that can observe and reason about their workspace, and that are skillfull enough to complete various assembly processes in weakly-structured settings. Moreover, it remains a great challenge to enable operators for teaching robots on-site, while managing the inherent complexity of perception, control, motion planning and reaction to unexpected situations. Motivated by real-world industrial applications, this paper demonstrates the potential of such a paradigm shift in robotics on the industrial case of an e-Bike motor assembly. The paper presents a concept for teaching and programming adaptive robots on-site and demonstrates their potential for the named applications. The framework includes: (i) a method to teach perception systems onsite in a self-supervised manner, (ii) a general representation of object-centric motion skills and force-sensitive assembly skills, both learned from demonstration, (iii) a sequencing approach that exploits a human-designed plan to perform complex tasks, and (iv) a system solution for adapting and optimizing skills online. The aforementioned components are interfaced through a four-layer software architecture that makes our framework a tangible industrial technology. To demonstrate the generality of the proposed framework, we provide, in addition to the motivating e-Bike motor assembly, a further case study on dense box packing for logistics automation.