Towards computing low-makespan solutions for multi-arm multi-task planning problems

We propose an approach to find low-makespan solutions to multi-robot multi-task planning problems in environments where robots block each other from completing tasks simultaneously. We introduce a formulation of the problem that allows for an approach based on greedy descent with random restarts for generation of the task assignment and task sequence. We then use a multi-agent path planner to evaluate the makespan of a given assignment and sequence. The planner decomposes the problem into multiple simple subproblems that only contain a single robots and a single task, and can thus be solved quickly to produce a solution for a fixed task sequence. The solutions to the subproblems are then combined to form a valid solution to the original problem. We showcase the approach on robotic stippling and robotic bin picking with up to 4 robot arms. The makespan of the solutions found by our algorithm are up to 30% lower compared to a greedy approach.Comment: Workshop for Planning and Robotics (PlanRob), International Conference on Automated Planning and Scheduling (ICAPS), 202

UAV Pathfinding in Dynamic Obstacle Avoidance with Multi-agent Reinforcement Learning

Multi-agent reinforcement learning based methods are significant for online planning of feasible and safe paths for agents in dynamic and uncertain scenarios. Although some methods like fully centralized and fully decentralized methods achieve a certain measure of success, they also encounter problems such as dimension explosion and poor convergence, respectively. In this paper, we propose a novel centralized training with decentralized execution method based on multi-agent reinforcement learning to solve the dynamic obstacle avoidance problem online. In this approach, each agent communicates only with the central planner or only with its neighbors, respectively, to plan feasible and safe paths online. We improve our methods based on the idea of model predictive control to increase the training efficiency and sample utilization of agents. The experimental results in both simulation, indoor, and outdoor environments validate the effectiveness of our method. The video is available at https://www.bilibili.com/video/BV1gw41197hV/?vd_source=9de61aecdd9fb684e546d032ef7fe7b

Time-Independent Planning for Multiple Moving Agents

Typical Multi-agent Path Finding (MAPF) solvers assume that agents move synchronously, thus neglecting the reality gap in timing assumptions, e.g., delays caused by an imperfect execution of asynchronous moves. So far, two policies enforce a robust execution of MAPF plans taken as input: either by forcing agents to synchronize or by executing plans while preserving temporal dependencies. This paper proposes an alternative approach, called time-independent planning, which is both online and distributed. We represent reality as a transition system that changes configurations according to atomic actions of agents, and use it to generate a time-independent schedule. Empirical results in a simulated environment with stochastic delays of agents' moves support the validity of our proposal.Comment: 10 pages, 5 figures, to be presented at AAAI-21, Feb 2021, Virtual Conferenc

Autonomous object harvesting using synchronized optoelectronic microrobots

Optoelectronic tweezer-driven microrobots (OETdMs) are a versatile micromanipulation technology based on the application of light induced dielectrophoresis to move small dielectric structures (microrobots) across a photoconductive substrate. The microrobots in turn can be used to exert forces on secondary objects and carry out a wide range of micromanipulation operations, including collecting, transporting and depositing microscopic cargos. In contrast to alternative (direct) micromanipulation techniques, OETdMs are relatively gentle, making them particularly well suited to interacting with sensitive objects such as biological cells. However, at present such systems are used exclusively under manual control by a human operator. This limits the capacity for simultaneous control of multiple microrobots, reducing both experimental throughput and the possibility of cooperative multi-robot operations. In this article, we describe an approach to automated targeting and path planning to enable open-loop control of multiple microrobots. We demonstrate the performance of the method in practice, using microrobots to simultaneously collect, transport and deposit silica microspheres. Using computational simulations based on real microscopic image data, we investigate the capacity of microrobots to collect target cells from within a dissociated tissue culture. Our results indicate the feasibility of using OETdMs to autonomously carry out micromanipulation tasks within complex, unstructured environments

Adaptive Autonomous Navigation of Multiple Optoelectronic Microrobots in Dynamic Environments

The optoelectronic microrobot is an advanced light-controlled micromanipulation technology which has particular promise for collecting and transporting sensitive microscopic objects such as biological cells. However, wider application of the technology is currently limited by a reliance on manual control and a lack of methods for simultaneous manipulation of multiple microrobotic actuators. In this article, we present a computational framework for autonomous navigation of multiple optoelectronic microrobots in dynamic environments. Combining closed-loop visual-servoing, SLAM, real-time visual detection of microrobots and obstacles, dynamic path-finding and adaptive motion behaviors, this approach allows microrobots to avoid static and moving obstacles and perform a range of tasks in real-world dynamic environments. The capabilities of the system are demonstrated through micromanipulation experiments in simulation and in real conditions using a custom built optoelectronic tweezer system

On Computing Universal Plans for Partially Observable Multi-Agent Path Finding

Multi-agent routing problems have drawn significant attention nowadays due to their broad industrial applications in, e.g., warehouse robots, logistics automation, and traffic control. Conventionally, they are modelled as classical planning problems. In this paper, we argue that it is beneficial to formulate them as universal planning problems. We therefore propose universal plans, also known as policies, as the solution concepts, and implement a system called ASP-MAUPF (Answer Set Programming for Multi-Agent Universal Plan Finding) for computing them. Given an arbitrary two-dimensional map and a profile of goals for the agents, the system finds a feasible universal plan for each agent that ensures no collision with others. We use the system to conduct some experiments, and make some observations on the types of goal profiles and environments that will have feasible policies, and how they may depend on agents' sensors. We also demonstrate how users can customize action preferences to compute more efficient policies, even (near-)optimal ones

Layered controller synthesis for dynamic multi-agent systems

In this paper we present a layered approach for multi-agent control problem, decomposed into three stages, each building upon the results of the previous one. First, a high-level plan for a coarse abstraction of the system is computed, relying on parametric timed automata augmented with stopwatches as they allow to efficiently model simplified dynamics of such systems. In the second stage, the high-level plan, based on SMT-formulation, mainly handles the combinatorial aspects of the problem, provides a more dynamically accurate solution. These stages are collectively referred to as the SWA-SMT solver. They are correct by construction but lack a crucial feature: they cannot be executed in real time. To overcome this, we use SWA-SMT solutions as the initial training dataset for our last stage, which aims at obtaining a neural network control policy. We use reinforcement learning to train the policy, and show that the initial dataset is crucial for the overall success of the method

Agent-based modeling of tsunami evacuation at Figueirinha Beach, Setubal, Portugal

Previous tsunami numerical model results show that the 1755 tsunami reached the Figueirinha beach 35 min after the earthquake, resulting in the inundation of the beach, the parking lot, and two sections of the road on the beach. Thus, an effective evacuation plan for the beach must be identified. However, conducting drills and evacuation exercises is costly and time-consuming. As an alternative, this study develops an agent-based model (ABM) to simulate the evacuation of beach users. The findings from this study reveal that, across the six considered scenarios, it is not feasible to evacuate all beach users in less than 35 min. The results also show there are only two routes available for evacuation—the left and right sides—with the left side offering a shorter evacuation time. However, both evacuation options come with advantages and disadvantages. The results of this study will be disseminated to local stakeholders.info:eu-repo/semantics/publishedVersio

Agent-Based Modeling of Tsunami Evacuation at Figueirinha Beach, Setubal, Portugal

Previous tsunami numerical model results show that the 1755 tsunami reached the Figueirinha beach 35 min after the earthquake, resulting in the inundation of the beach, the parking lot, and two sections of the road on the beach. Thus, an effective evacuation plan for the beach must be identified. However, conducting drills and evacuation exercises is costly and time-consuming. As an alternative, this study develops an agent-based model (ABM) to simulate the evacuation of beach users. The findings from this study reveal that, across the six considered scenarios, it is not feasible to evacuate all beach users in less than 35 min. The results also show there are only two routes available for evacuation—the left and right sides—with the left side offering a shorter evacuation time. However, both evacuation options come with advantages and disadvantages. The results of this study will be disseminated to local stakeholders.info:eu-repo/semantics/publishedVersio