text: Multi-robot teams offer possibilities of improved performance and fault tolerance, compared to single robot solutions. In this paper, we show how to realize those possibilities when starting from a single robot system controlled by a Behavior Tree (BT). By extending the single robot BT to a multi-robot BT, we are able to combine the fault tolerant properties of the BT, in terms of built-in fallbacks, with the fault tolerance inherent in multi-robot approaches, in terms of a faulty robot being replaced by another one. Furthermore, we improve performance by identifying and taking advantage of the opportunities of parallel task execution, that are present in the single robot BT. Analyzing the proposed approach, we present results regarding how mission performance is affected by minor faults (a robot losing one capability) as well as major faults (a robot losing all its capabilities). Finally, a detailed example is provided to illustrate the approach.
  @cite_9: We refer to social autonomy in a collaborative relationship among agents based on delegation and help. We address the problem of adjustable autonomy. We stress in particular the role played in autonomy by: 1) the degree of "openness" of delegation; 2) the allowed initiative in (re)starting negotiation; 3) the degree and kind of control; and 4) the strength of delegation with respect to interaction. We show how the adjustability of delegation and autonomy is actually "bilateral". Adjustment is also "bidirectional" and multidimensional. Finally, we analyze some reasons for modifying the assigned autonomy and show how the adjustment of autonomy depends: on the delegator's side; on a "crisis of trust" and vice versa; the delegee's adjustment of its own autonomy depends on some disagreement about the trust received from the delegator, and, in particular, either a higher or lower confidence in itself or in external circumstances. Some preliminary hints about necessary protocols for adjusting the interaction with agents are provided. This work is aimed at providing a theoretical framework, i.e., the conceptual instruments necessary for analyzing and understanding interaction with autonomous entities.
  @cite_1: This paper proposes a novel version of the vehicle routing problem (VRP). Instead of servicing all the customers, feasible solutions of the VRP instance are forced to satisfy a set of complex high-level tasks given as a metric temporal logic (MTL) specification, which allows complex quantitative timing constraints to be incorporated into the problem. For the resulting vehicle routing problem with metric temporal logic specifications (VRPMTL), a mixed-integer linear programming (MILP) based algorithm is provided that solves the problem to optimality. Examples for optimal multi-UAV mission planning are provided where MTL is used as a high level language to specify complex mission tasks.
  @cite_6: A major goal in robot motion planning and control is to be able to specify a task in a high-level, expressive language and have the robot(s) to automatically convert the specification into a set of low-level primitives, such as feedback controllers and communication protocols, to accomplish the task. The robots can vary from manipulator arms used in manufacturing or surgery, to autonomous vehicles used in search and rescue or in planetary exploration, and to smart wheel chairs for disabled people. They are subject to mechanical constraints (e.g., a carlike robot cannot move sideways,an airplane cannot stop in place) and have limited computation, sensing, and communication capabilities. The environments can be cluttered with possibly moving and shape-changing obstacles and can con tain dynamic (moving, appearing, or disappearing) targets. One of the major challenges in this area is the development of a computationally efficient frame work accommodating both the robot constraints and the complexity of the environment, while, at the same time, allowing for a large spectrum of task specifications.
  @cite_0: In the next decades, practically viable robotic agent systems are going to be mixed-initiative in nature. Humans will request help from such systems and such systems will request help from humans in achieving the complex mission tasks required. Pragmatically, one requires a distributed task specification language to define tasks and a suitable data structure which satisfies the specification and can be used flexibly by collaborative multi-agent robotic systems. This paper defines such a task specification language and an abstract data structure called Task Specification Trees which has many of the requisite properties required for mixed-initiative problem solving and adjustable autonomy in a distributed context. A prototype system has been implemented for this delegation framework and has been used practically with collaborative unmanned aircraft systems.
  @cite_19: In this paper, we consider the automated planning of optimal paths for a robotic team satisfying a high level mission specification. Each robot in the team is modeled as a weighted transition system where the weights have associated deviation values that capture the non-determinism in the traveling times of the robot during its deployment. The mission is given as a Linear Temporal Logic (LTL) formula over a set of propositions satisfied at the regions of the environment. Additionally, we have an optimizing proposition capturing some particular task that must be repeatedly completed by the team. The goal is to minimize the maximum time between successive satisfying instances of the optimizing proposition while guaranteeing that the mission is satisfied even under non-deterministic traveling times. After computing a set of optimal satisfying paths for the members of the team, we also compute a set of synchronization sequences for each robot to ensure that the LTL formula is never violated during deployment. We implement and experimentally evaluate our method considering a persistent monitoring task in a road network environment.
  @cite_10: We describe the software components of a robotics system designed to autonomously grasp objects and perform dexterous manipulation tasks with only high-level supervision. The system is centered on the tight integration of several core functionalities, including perception, planning and control, with the logical structuring of tasks driven by a Behavior Tree architecture. The advantage of the implementation is to reduce the execution time while integrating advanced algorithms for autonomous manipulation. We describe our approach to 3-D perception, real-time planning, force compliant motions, and audio processing. Performance results for object grasping and complex manipulation tasks of in-house tests and of an independent evaluation team are presented.
summary: In this work, we show how a plan for a multi-robot system can be addressed in a BT fashion, gaining all the advantages aforementioned in addition to the scalability that distinguishes a general multiagent system. Many existing works @cite_6 @cite_1 @cite_19 stressed the problem of defining local tasks to achieve a global specification emphasizing the advantages of having a team of robots working towards a global goal. Moreover, @cite_10 @cite_9 introduce the concept of task delegation among agents in a multi-agent system, dividing task specification in , where goal and plan are predefined, and where either only the goal is specified while the plan can be chosen by the agent, or the specified plan describes abstractly what actions have to be taken, giving to the agent some freedom in terms of how to perform the delegated task. In @cite_0 , it is shown how verifying the truth of preconditions on single agents becomes equivalent to checking the fulfillment of a global robot network through recursive calls, using a tree structure called .