We present a methodology for the generation of mobile robot controllers which offer probabilistic time-bounded guarantees on successful task completion, whilst also trying to satisfy soft goals. The approach is based on a stochastic model of the robot’s environment and action execution times, a set of soft goals, and a formal task specification in co-safe linear temporal logic, which are analysed using multi-objective model checking techniques for Markov decision processes. For efficiency, we propose a novel two-step approach. First, we explore policies on the Pareto front for minimising expected task execution time whilst optimising the achievement of soft goals. Then, we use this to prune a model with more detailed timing information, yielding a time-dependent policy for which more fine-grained probabilistic guarantees can be provided. We illustrate and evaluate the generation of policies on a delivery task in a care home scenario, where the robot also tries to engage in entertainment activities with the patients.

This paper presents a framework developed for an industrial robotics system that utilises two different planning components. At a high level, a multi-robot mission planner interfaces with a fleet and environment manager and uses multiagent planning techniques to build mission assignments to be distributed to a robot fleet. On each robot, a task planner automatically converts the robot's world model and skill definitions into a planning problem which is then solved to find a sequence of actions that the robot should perform to complete its mission. This framework is demonstrated on an industrial kitting task in a real-world factory environment.

Parking is a key component for efficient urban mobility with implications on congestion and the urban landscape. We propose planning strategies for automated, high-density parking lot systems that efficiently execute (i) selection of vehicle destination and (ii) conflict-free motion planning for vehicle input and output operations. The practical performance of the system is demonstrated through simulation using an empirical dataset. The system allows halving the space requirements for parking vehicles while providing fast access to vehicles and keeping low the in-park travel distances when comparing with conventional parking lots.

In cooperative multiagent planning, it can often be beneficial for an agent to make commitments about aspects of its behavior to others, allowing them in turn to plan their own behaviors without taking the agent's detailed behavior into account. Extending previous work in the Bayesian setting, we consider instead a worst-case setting in which the agent has a set of possible environments (MDPs) it could be in, and develop a commitment semantics that allows for probabilistic guarantees on the agent's behavior in any of the environments it could end up facing. Crucially, an agent receives observations (of reward and state transitions) that allow it to potentially eliminate possible environments and thus obtain higher utility by adapting its policy to the history of observations. We develop algorithms and provide theory and some preliminary empirical results showing that they ensure an agent meets its commitments with history-dependent policies while minimizing maximum regret over the possible environments.

When planning actions to take on behalf of its human operator, a robot might be uncertain about its operator's reward function. We address the problem of how the robot should formulate an (approximately) optimal query to pose to the operator, given how its uncertainty affects which policies it should plan to pursue. We explain how a robot whose queries ask the operator to choose the best from among k choices can, without loss of optimality, restrict consideration to choices only over alternative policies. Further, we present a method for constructing an approximately-optimal policy query that enjoys a performance bound, where the method need not enumerate all policies. Finally, because queries posed to the operator of a robotic system are often expressed in terms of preferences over trajectories rather than policies, we show how our constructed policy query can be projected into the space of trajectory queries. Our empirical results demonstrate that our projection technique can outperform other known techniques for choosing trajectory queries, particularly when the number of trajectories the operator is asked to compare is small.

Recent work in multi-agent path planning has provided a number of optimal and suboptimal solvers that can efficiently find solutions to problems of growing complexity. Solvers based on Conflict-Based Search (CBS) combine single-agent solvers with shared constraints between agents to find feasible solutions. Suboptimal variants of CBS introduce alternate heuristics to avoid conflicts. In this paper we study the multi-agent planning problem in the context of non-holonomic vehicles planning on a lattice. We propose that in addition to using heuristics to avoid conflicts, we can plan using a hierarchy of movement constraints to efficiently avoid conflicts. We develop a new extension to the CBS algorithm, CBS with constraint layering (CBS+CL), which iteratively applies different movement constraint models during the CBS planning process. Our results show that this approach allows us to solve for about 2.4 times more agents in the same amount of time when compared to regular CBS without using a constraint hierarchy.

Multi-agent planning using MA-STRIPS-related models is often motivated by the preservation of private information. Such motivation is not only natural for multi-agent systems, but it is one of the main reasons, why multi-agent planning (MAP) problems cannot be solved centrally. In this paper, we analyze privacy-preserving multi-agent planning (PP-MAP) from the perspective of secure multiparty computation (MPC). We discuss the concept of strong privacy and its implications and present two variants of a novel planner, provably strong privacy-preserving in general. As the main contribution, we formulate the limits of strong privacy-preserving planning in the terms of privacy, completeness and efficiency and show that, for a wide class of planning algorithms, all three properties are not achievable at once. Moreover, we provide a restricted variant of strong privacy based on equivalence classes of planning problems and show that an efficient, complete and strong privacy-preserving planner exists for such restriction.

Autonomous exploration and search have important applications in robotics. One interesting application is cooperative control of mobile robotic/sensor networks to achieve uniform coverage of a domain. Ergodic coverage is one solution for this problem in which control laws for the agents are derived so that the agents uniformly cover a target area while maintaining coordination with each other. Prior approaches have assumed the target regions contain no obstacles. In this work, we tackle the problem of static and dynamic obstacle avoidance while maintaining an ergodic coverage goal. We pursue a vector-field-based obstacle avoidance approach and define control laws for idealized kinematic and dynamic systems that avoid static and dynamic obstacles while maintaining ergodicity. We demonstrate this obstacle avoidance methodology via numerical simulation and show how ergodicity is maintained. Keywords-- Multi-agent planning, centralized robot control, ergodic theory, uniform coverage, obstacle avoidance.

The problem of planning with partial observability in the presence of a single agent has been addressed as a contingent or POMDP problem. Since the task is computationally hard, on-line approaches have also been developed that just compute the action to do next rather than full policies. In this work, we address a similar problem but in a multiagent setting where agents share a common goal and plan with beliefs which are about the world and the possibly nested beliefs of other agents. For this, we extend the belief tracking formulation of Kominis and Geffner to the on-line setting where plans are supposed to work for the true hidden state as revealed by the observations, and develop an alternative translation into classical planning that is used within a plan-execute-observe-and-replan cycle. Planning is done from the perspective of the agents, and there is a single planning agent in each replanning episode that can change across episodes. We present empirical results and show that interesting agent dialogues arise in this setting where agents collaborate by requesting or volunteering information in a goal-directed manner.

Agents operating in a multi-agent environment must consider not just their own actions, but also those of the other agents in the system. Artificial social systems are a well known means for coordinating a set of agents, without requiring centralized planning or online negotiation between agents. Artificial social systems enact a social law which restricts the agents from performing some actions under some circumstances. A good social law prevents the agents from interfering with each other, but does not prevent them from achieving their goals. However, designing good social laws, or even checking whether a proposed social law is good, are hard questions. In this paper, we take a first step towards automating these processes, by formulating criteria for good social laws in a multi-agent planning framework. We then describe an automated technique for verifying if a proposed social law meets these criteria, based on a compilation to classical planning.