Asynchronous Behavior Trees with Memory aimed at Aerial Vehicles with Redundancy in Flight Controller

Safronov, Evgenii; Vilzmann, Michael; Tsetserukou, Dzmitry; Kondak, Konstantin

doi:10.1109/iros40897.2019.8967928

Cited by 15 publications

(11 citation statements)

References 12 publications

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“…In this paper, we present a novel action client and server model that builds on the BT. In contrast to studies [16], [20], [21], [23], [24], [26], [27] that planned multiple tasks of only one robot using a single BT, we show how to use multiple BTs to operate multiple tasks of multiple robots in a separate task planning unit. To operate multiple BTs, action splitting is proposed to enable DDS communication between BTs (Lemma 1 in Section II).…”

Section: Introductioncontrasting

confidence: 68%

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Behavior Tree-Based Task Planning for Multiple Mobile Robots using a Data Distribution Service

Jeong¹,

Ga²,

Inhwan³

et al. 2022

Preprint

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In this study, we propose task planning framework for multiple robots that builds on a behavior tree (BT). BTs communicate with a data distribution service (DDS) to send and receive data. Since the standard BT derived from one root node with a single tick is unsuitable for multiple robots, a novel type of BT action and improved nodes are proposed to control multiple robots through a DDS asynchronously. To plan tasks for robots efficiently, a single task planning unit is implemented with the proposed task types. The task planning unit assigns tasks to each robot simultaneously through a single coalesced BT. If any robot falls into a fault while performing its assigned task, another BT embedded in the robot is executed; the robot enters the recovery mode in order to overcome the fault. To perform this function, the action in the BT corresponding to the task is defined as a variable, which is shared with the DDS so that any action can be exchanged between the task planning unit and robots. To show the feasibility of our framework in a real-world application, three mobile robots were experimentally coordinated for them to travel alternately to four goal positions by the proposed single task planning unit via a DDS.

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Section: Introductioncontrasting

confidence: 68%

“…Some studies have added memory functions to BTs. For example, a flight control computer was constructed by introducing memory variables into a BT [27]. Memory variables were maintained by a latch that remembered the successes or failures returned by subtrees and did not reevaluate extra subtrees without a reset.…”

Section: Introductionmentioning

confidence: 99%

Behavior Tree-Based Task Planning for Multiple Mobile Robots using a Data Distribution Service

Jeong¹,

Ga²,

Inhwan³

et al. 2022

Preprint

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“…Robotics applications of BTs span from manipulation [2]- [4] to non-expert programming [5]- [7]. Other works include task planning [8], human-robot interaction [9]- [11], learning [12]- [15], UAV [16]- [21], multi-robot systems [22]- [25], and system analysis [26]- [28]. The Boston Dynamics's Spot uses BTs to model the robot's mission [29], the Navigation Stack and the task planner of ROS2 uses BTs to encode the high level robot's behavior [30], [31].…”

Section: Introductionmentioning

confidence: 99%

Handling Concurrency in Behavior Trees

Colledanchise,

Natale

2021

Preprint

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This paper addresses the concurrency issues affecting Behavior Trees (BTs), a popular tool to model the behaviors of autonomous agents in the video game and the robotics industry.BT designers can easily build complex behaviors composing simpler ones, which represents a key advantage of BTs. The parallel composition of BTs expresses a way to combine concurrent behaviors that has high potential, since composing pre-existing BTs in parallel results easier than composing in parallel classical control architectures, as finite state machines or teleo-reactive programs. However, BT designers rarely use such composition due to the underlying concurrency problems similar to the ones faced in concurrent programming. As a result, the parallel composition, despite its potential, finds application only in the composition of simple behaviors or where the designer can guarantee the absence of conflicts by design.In this paper, we define two new BT nodes to tackle the concurrency problems in BTs and we show how to exploit them to create predictable behaviors. In addition, we introduce measures to assess execution performance and show how different design choices affect them. We validate our approach in both simulations and the real world. Simulated experiments provide statisticallysignificant data, whereas real-world experiments show the applicability of our method on real robots. We provide an opensource implementation of the novel BT formulation and published all the source code to reproduce the numerical examples and experiments.

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“…For example, the condition object close becomes observable only after the robot finds the object or if the robot knows the object position a priori. In this paper, following the recent advances of BT semantic [4], we allow the conditions to be either true, false, or unknown. Doing so, we can construct a new BT (in Figure 1b) that, in addition to the functionalities of the original BT, it performs a action that looks for the object whenever the value of its position relative to the robot is unknown.…”

Section: Introductionmentioning

confidence: 99%

“…We follow conventional BT syntax with few important differences. The literature includes detailed descriptions [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Task Planning with Belief Behavior Trees

Safronov¹,

Colledanchise²,

Natale³

2020

Preprint

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In this paper, we propose Belief Behavior Trees (BBTs), an extension to Behavior Trees (BTs) that allows to automatically create a policy that controls a robot in partially observable environments. We extend the semantic of BTs to account for the uncertainty that affects both the conditions and action nodes of the BT. The tree gets synthesized following a planning strategy for BTs proposed recently: from a set of goal conditions we iteratively select a goal and find the action, or in general the subtree, that satisfies it. Such action may have preconditions that do not hold. For those preconditions, we find an action or subtree in the same fashion. We extend this approach by including, in the planner, actions that have the purpose to reduce the uncertainty that affects the value of a condition node in the BT (for example, turning on the lights to have better lighting conditions). We demonstrate that BBTs allows task planning with non-deterministic outcomes for actions. We provide experimental validation of our approach in a real robotic scenario and -for sake of reproducibilityin a simulated one.

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Asynchronous Behavior Trees with Memory aimed at Aerial Vehicles with Redundancy in Flight Controller

Cited by 15 publications

References 12 publications

Behavior Tree-Based Task Planning for Multiple Mobile Robots using a Data Distribution Service

Behavior Tree-Based Task Planning for Multiple Mobile Robots using a Data Distribution Service

Handling Concurrency in Behavior Trees

Task Planning with Belief Behavior Trees

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