Evolving Behaviour Trees for Supervisory Control of Robot Swarms

Elliott Hogg1†, Sabine Hauert1, David Harvey, Arthur Richards1

1Bristol Robotics Laboratory, University of Bristol, UK.E-mail: eh17402@bristol.ac.uk, arthur.richards@bristol.ac.uk

Abstract: Supervisory control of swarms is essential to their deployment in real world scenarios to both monitor theiroperation and provide guidance. We explore mechanisms by which humans might provide supervisory control to swarmsand improve their performance. Rather than have humans guess the correct form of supervisory control, we use artificialevolution to learn effective human-readable strategies to help human operators understanding and performance when con-trolling swarms. These strategies can be thoroughly tested and can provide knowledge to be used in the future in a varietyof scenarios. Behaviour trees are applied to represent human readable decision strategies which are produced throughevolution. We investigate a simulated set of scenarios where a swarm of robots have to explore varying environments andreach sets of objectives. Effective supervisory control strategies are evolved to explore each environment using differentlocal swarm behaviours. The evolved behaviour trees are animated alongside swarm simulations to enable clear under-standing of the supervisory strategies. We conclude by identifying the strengths in accelerated testing and the benefits ofthis approach for scenario exploration and training of human operators.

Keywords: Human Swarm Interaction; Swarm; Artificial Evolution; Supervisory Control; Behaviour Trees

1. INTRODUCTION

Growing interest in the use of swarm systems has ledto new questions regarding effective design for real-worldenvironments [5]. Although their use has great poten-tial in areas ranging from search and rescue to automatedagriculture, there are still few examples of real-world de-ployment. Human supervision has been proposed to im-prove the performance of swarming by maintaining thescalability and robustness of swarms [4] whilst taking ad-vantage of human intelligence and understanding.

Human Swarm Interaction (HSI) aims to adapt swarmmodels into hybrid systems which operate with the aid ofa human operator. The operator can compensate for thelimitations of swarming behaviours and increase perfor-mance by interacting with the swarm. The fundamentalassumption is that the operator has information regardingthe task that the swarm cannot perceive. Based on thisknowledge, the operator can identify when the swarm be-haviour requires adjustment. This overcomes the swarm’slimited capability to understand its environment and thenuances of a given task. This control scheme allowsthe human to correct for poor performance and researchhas highlighted significant improvements over purely au-tonomous swarms [1, 8].

In HSI, research has investigated ways in which an op-erator can infer high level knowledge to the swarm to im-prove performance. Initial answers have defined methodsto directly control how the swarm behaves. If the operatorhas information that the swarm cannot perceive, then theoperator can take action to account for the swarm’s lackof knowledge. These types of control have been catego-rized into four different types as discussed in Kolling’ssurvey on HSI [14].• Algorithmic: Changing the emergent behaviour of theswarm [1, 13].

† Elliott Hogg is the presenter of this paper.

• Parametric: Adjusting the characteristics of an emer-gent behaviour [6, 12].• Environmental: The operator highlights points of inter-est within an environment [8, 26].• Agent Control: The operator can take control of anagent and perform manual movements to influence itsneighbors [22, 23].

Each control method has shown the ability to posi-tively affect the swarms performance in varying scenar-ios, however, it is difficult to identify the best choice ofcontrol method for a given scenario. We need to considerhow the type of application and conditions should influ-ence the way we choose to control the swarm. Other fac-tors that also need consideration include cognitive limita-tions of the operator [16], the optimal timing for humaninteraction [20], the operators knowledge of swarm dy-namics [11], and how the level of interaction affects therobustness of the swarm [25]. Due to the high dimension-ality of these scenarios it is difficult to understand howHSI systems should be designed.

There is currently no way to explore supervisory con-trol of swarms systematically. HSI studies are currentlyinvestigated using human trials. This is beneficial for un-derstanding how humans behave when learning to con-trol swarms, however, here we focus on exploring thebroader context of swarm control and how to systemat-ically design HSI systems. Rather than having the hu-man guess the rules, we propose artificial evolution ofdecision-making to act as a surrogate for the human. Inthis investigation we generate a swarm supervisor whichcan view and control the swarm at accelerated simulationspeed. Using this approach we can exploit the ability totest rapidly in different simulated conditions to gain bet-ter understanding of HSI strategies. Solutions can be usedto infer training of human operators and also inform ourknowledge of swarm control and therefore we considerits viability in scenario exploration within HSI.

Fig. 1 Example behaviour tree. Numbering shows theorder in which the tree is ticked.

The following section will discuss the background ofthe methodologies that have been used. Section 3 thendiscusses a subset of swarming scenarios that we haveexplored and how artificial evolution has been applied toproduce control strategies. We then analyse and discussour findings in sections 4 and 5.

2. BACKGROUND

BT models have been chosen for their human read-ability and modularity. Trees can be modified and re-ordered without any need to redesign individual nodes,this is useful for artificial evolution where structures canbe modified through crossover and mutation. Examplesof applications are given in [10, 17]. Human readabilityof Bts also enables us to represent strategies in an easy tounderstand form when presented to human operators fortraining.

2.1. Behaviour TreesA BT is a hierarchical model which consists of actions,

conditions, and operators connected by directed edges[3, 21]. BTs can represent many decision-making sys-tems, such as finite state automata and subsumption ar-chitectures. An example tree is shown in figure 1. Thesetrees are modular and can be constructed from a set ofaction and condition functions which are independent oforder. This makes them well suited to artificial evolution.A BT is executed in a particular sequence, each step isreferred to as a tick which occurs at periodic time steps.The tree is ticked starting from the root of the tree, pass-ing down from parent to child. When the tick arrives ata leaf (A node with no children), which can either be anaction or condition node, a computation is made whichwill return either success, failure or running. The valuereturned then passes back up the tree depending on thestructure of the tree. This process is shown in figure 1.Trees are ticked depth first and from left right, hence,nodes on the left have higher priority.

The operator nodes can be thought of as logical gateswhich tick their children in steps. There are selectors, se-quences, and parallel operators. A selector node will re-turn a success once one of its children succeeds and willreturn a failure if all children fail. A sequence only suc-ceeds if all children succeed. As soon as one child failsthe sequence will return failure. A parallel operator ticksall of its children at once and will succeed if a certainnumber of children succeed and otherwise will return afailure. The root of a tree will always be an operator node.

When an action node is ticked, it will return a success ifthe computation has been completed and returns failureif it could not be completed. For a condition node, the re-turn value is dependent on the condition statement. Referto Colledanchise and Ogren’s book for greater coverageof behaviour tree concepts [3].

2.2. Genetic Programming of Behaviour TreesGenetic programming (GP) of behaviour trees has

been performed for a variety of applications [9, 17, 18].The aim of GP is to evolve the structure of computer pro-grams represented in the form of hierarchies [15]. Weapply common practices in GP to evolve behaviour treesin this investigation. In GP the genome is the completestructure of the computer program. Because of this, weapply evolutionary operators which account for this struc-ture. The modular nature of BTs allows us to modify thestructure of a tree and still produce a valid executable BT.This makes them applicable to evolution which is relianton random manipulation of genomes. We apply threetypes, single point crossover, single node mutation, andsub-tree growth. These techniques allow us to modifyand combine genomes to explore different solutions.

3. METHODOLOGYThe following section will detail how artificial evolu-

tion has been applied to develop swarm control strategiesfor a search and rescue (SAR) scenario. In this work, weuse BTs to represent a particular strategy to control theswarm that is evolved to increase task performance.

3.1. Simulation ArchitectureWe use our own custom simulator programmed in

Python. The simulator consists of 2-D environmentswhere a scenario is specified, the swarm size, and areas ofinterest to explore. The agents are modelled as particleswhich can move in any direction with a degree of randommotion, and thus, the swarms behaviour is not determin-istic. Each agent follows a vector trajectory which is de-pendant on the emergent behaviour of the swarm. Poten-tial field forces act as additional vectors to avoid obstaclesand the bounds of the environment. The agents travel ata speed of 0.5 m/s. This system has been deployed on asuper-computer cluster which has allowed us to evaluatemany scenarios in parallel. This system is beneficial aswe can rapidly assess new conditions simultaneously.

3.2. ScenarioA swarm of ten agents is tasked to search an environ-

ment to find a set of objectives as quickly as possible. Weinvestigate performance over a range of environments andobjective positions as shown in figure 2. For each map,we test for each different set of objective positions, re-sulting in a total set of 15 unique scenarios. This task ischallenging as the total area is sufficiently large in rela-tion to the swarm size and will require efficient coverageto find the objectives quickly. The swarm supervisor hasno knowledge of where the objectives are located or theshape of the environment. This will require the swarm

Fig. 2 Environments and objectives under investigation. The starting position for the swarm is shown in blue. Objectivesare shown in red. The area of each environment is 40mx40m.

supervisor to identify the prior knowledge of the objec-tive positions and nature of the environment. For eachevaluation the swarm is initialized in a random startingposition within a 3mx3m area at the origin.

The task of the supervisor is to utilize a set of emer-gent swarm behaviours based on the observation of theswarm state to efficiently search for the different areas ofinterest. We will now discuss the design of the supervisorin greater detail.

3.3. Swarm SupervisorIn order to produce control strategies a set of actions

and decisions need to be defined that will allow the super-visor to interact with the swarm and can be represented asa BT.

3.3.1. ActionsThe actions allow the swarm supervisor to change the

swarms behaviour. Only forms of algorithmic control areconsidered. This form of control is commonly used inHSI systems as it is easy for untrained operators to under-stand and enables effective control of the swarm withoutreducing swarm autonomy [1]. The actions available willnow be detailed.

The first behaviour is dispersion. When initially clus-tered together, agents are repelled by an exponential forcefrom one another in order to spread into open spaces.Each agent is repelled from every other agent by an ex-ponential force represented as a vector. The sum of thesevectors determines the direction of travel. When signif-icantly spread out agents will travel with a random walk[7, 19]. Dispersion was chosen as a natural means forspreading out and filling an area which is suitable for aSAR based task.

The remaining set of actions are defined as directedfields. Directed potential fields cause the swarm to dis-perse in a specific direction. The swarm will travel inthe specified direction whilst avoiding obstacles and re-pelling from nearby agents. We enable eight varyingforms of this behaviour such that the swarm can be di-rected north, south, east, and west. As well as, north west,

north east, south west, and south east. We considered thatthese behaviours can be used to direct the swarm to par-ticular regions. The operator may have better knowledgeof the area to cover and may want to direct the swarmusing this behaviour. Despite this, good use of these be-haviours relies on the correct choice of the direction oftravel.

3.3.2. ConditionsIn order for the swarm supervisor to decide which ac-

tions to take, some knowledge of the swarm state is re-quired. In this scenario the swarm supervisor can ob-serve the center of mass and spread of the swarm. Withthis knowledge the swarm supervisor can make decisionsbased on the general location of the swarm and a senseof formation through its spread. Using the center of massthe supervisor can direct the swarm to specific regionswhere objectives are located. Understanding spread willenable solutions which promote the swarm to spread outto gain greater coverage.

This high level representation means we don’t needknowledge of the whole swarm to enable control. Thismay be more suitable for human control by reducing cog-nitive load and scenarios where we don’t have completeswarm visibility [24]. In these initial results, the super-visor is given access to two simple, bulk measures ofthe swarm’s behaviour. Future work will introduce othermetrics and evaluate their utility. The center of mass ofthe swarm is calculated as the average over all agent po-sitions in the x direction µx and y direction µy , where nis the total number of agents. Each agent ordinate is de-fined as xn and yn. Spread, σ, is defined as the averagedistance from agent to agent as shown in equation 2.

µx =1

n

n∑1

xn , µy =1

n

n∑1

yn (1)

σ =1

n ∗ (n− 1)

k=n∑k=1

i=n∑i=1:i 6=k

((xi−xk)2−(yi−yk)2)12 (2)

Decisions can be constructed using these metrics with

respect to defined thresholds shown in table 1. Here weenable simple decisions to be made by comparing the realtime metric value to a defined threshold. The thresholdsthat can be selected for the center of mass are boundedwithin the size of the environment, and similarly, thespread is limited up to the highest level that they can fillwithin the bounds of the environment.

We can represent the actions and conditions in nodeform that can be executed within a BT. Evolution will aimto find the optimal combination of decisions and actionsfor each of the scenarios in our investigation.

3.4. Evolving the Swarm SupervisorThis section will further detail the design of the swarm

supervisor and the evolutionary algorithm used to explorepossible solutions within the problem space.

3.4.1. Genetic ProgrammingWe define certain constraints on the types of trees

that can be generated as well as bounds for mutation.Trees generated for the initial population have a maxi-mum depth of two levels down from the root of the tree.We also limit the number of possible children that an op-erator may have between 2 and 5. Given the complexityof this search scenario and the number of node choices,we expect that the sizes of trees evolved should matchthese constraints. Despite this, we can still grow trees tofurther depths through crossover and so the initial popula-tion does not define the depths of all following BTs. Table1 represents the available nodes to construct trees. In par-ticular we show the limits that the conditional statementscan take and the evolution is bounded to these limits whengenerating random trees and performing mutations.

3.4.2. Evolutionary AlgorithmWe evaluate the fitness of individuals based on the

number of objectives that are found and the time takento find them. A objective is classified as detected whenan agents distance from the objective is less than a mag-nitude of 5 in both the x and y directions. Each objectivehas an associated reward when detected. The maximumreward is 1 point which decays over the duration of thesearch. The reward decay function is defined by equation3 where, t is the current time-step and dist, is the distancebetween the objective position and the origin.

ObjectiveReward = 0.95t/dist (3)

Fitness =1

total objectives

∑objective rewards (4)

This decay function reduces the reward associated toeach individual objective over time, therefore, promot-ing fast search strategies. The rate of decay is dependenton the distance of the objective from the starting positionof the swarm. This means that objectives that are easy tofind and require less exploration have a faster decay. Sim-ilarly, objectives that are further from the origin decayslower. The individual fitness is defined as the summationof all objective rewards collected over the time limit nor-malized against the maximum possible score based on the

total number of objectives as shown by equation 4. Thisfitness is averaged over 5 attempts for each individual.We also add a condition that limits tree size to contain atmost 35 nodes. In this case, evolution aims to maximizefitness.

Tournament selection is used for groups of three indi-viduals followed by single point crossover, single pointmutation, and sub-tree growth with probabilities shownin table 2. Elitism is used to save the best 10 individualsfrom each generation. We implement a population size of100 individuals. For each individual, we set a time limitof 800 seconds. This time limit is significantly long toallow the swarm to fully explore the area.

Table 2 Evolutionary parameters.

Parameter ValuePopulation size 100

Test limit 800 secondsElitism size 10

Tournament size 3Single Point Mutation probability 0.1

Sub-tree Growth probability 0.05

4. FINDINGS AND ANALYSISWe have evolved control strategies in 15 different sce-

narios. These are based on all pairwise combinationsof environments and objective sets (figure 2). We willdiscuss the fittest individuals evolved for each scenario.These are presented in figure 3. The behaviours of eachindividual are shown in figure 4. We represent the be-haviours through the trails of the swarm agents move-ments over the set trial duration. The colour of the pointsindicate the position in time, initially plotted as blue andshifting over time towards red when reaching the timelimit. Objectives that have been found are indicated bygreen crosses and objectives that are not found are shownas red circles.

In all cases the evolved solutions use combinations ofdifferent swarm behaviours whilst observing the swarmsstate to maximize performance. In no case were singlebehaviours used. Out of the available behaviours, the di-rected fields were used in the majority of solutions. Dis-persion was used in a small number of trees but showedlittle impact on the overall behaviour. Solutions usedcombinations of center of mass and spread to control theswarm whilst in some, just one or the other. For each sce-nario, we see that solutions are correctly specializing toeach task and achieving high performance. Differencesin strategies are largely dependant on the type of environ-ment used, with additional influence based on the objec-tive sets.

4.1. Scenario Specific SolutionsFor open environment X, all solutions scored highly.

We see that solutions b and c formed similar strategiesto sweep across the entirety of the environment and findall objectives. Both solutions used spread only to control

Table 1 The limits defined by the GP algorithm for the types of nodes that can be selected to produce BTs.

Node type Selection ChoicesOperator Selector / Sequence (Between 2-5 children)Action node Dispersion / Aggregation / North / South / East / West / North East / North West /

South East / South WestCondition node µx>-18, µx>-16, µx>-14, ... increment by 2 ..., µx>14, µx>16, µx>18

µx18µy16σ

objective set a, the solution scores well however, many ofthe objectives are not found and a similar behaviour to dand e is formed rather than a complete sweep to find allobjectives.

For map Y we see more complicated behaviours. Inmap X, strategies consisted of two phases of movements,where as in map Y, there are up to 4 phases. This is alsothe case of map Z. In scenarios a through to c, we seevery good performance as full coverage of the environ-ment is generated. Each of these behaviours have 3 to4 phases of movement and use a combination of centerof mass and spread to direct the swarm. For example,solution b sends the swarm west, east, south, and finallywest. In cases d and e, again the swarm is directed to thecorners where the objectives are placed however, in thismap the solutions don’t attempt to reach both corners andsubsequently achieve low scores.

In map Z, we see that the solutions score lower overallthan the previous map due to further increased difficulty.In scenarios a to c, we see that good coverage is formedbut some objectives are not found. Solution e again onlyattempts to reach one objective as appose to both. How-ever, solution d generates very good coverage and is ableto reach both ends of the environment. We also see in-teresting behaviour where the swarm is split between thewalls to reach each ends of the map faster. This approachis seen across many solutions in map Y and Z and high-lights that these are useful, non-intuitive scenarios to hu-mans.

4.2. Human Understandable StrategiesNot only can we observe the movement of the swarm,

we can observe the states of each BT and learn how thebehaviour is formed. We see that solution y-b performsvery well and generates full map coverage. By examin-ing the human readable tree we can quickly understandhow the swarm is controlled. In addition, we have theability to animate these BTs in real-time to quickly iden-tify when nodes are triggered. Figure 5 shows the keystates of the BT that controls the swarm in scenario Y-b.The colours of the nodes indicate the state of the node,green for success, red for failure, and white if they havenot been triggered.

With this visualization we can break the strategy downinto easy to understand key stages of commands. Ini-tially the swarm is directed west until the center of massis greater than 2 in the y direction. This enables the swarmto access the upper corridor. Using the y center of mass,the tree observes when the swarm reaches the end walland has caused some agents to head north. Once trig-gered, the swarm heads east in phase 2, leaving someagents in the top corridor and some in the central corridor.This is an interesting approach to reach the top corridorwhilst simultaneously getting reading to explore the bot-tom corridor. Once the center of mass is greater than 14in the x direction and the swarm is at the right hand sidewall, the tree directs the swarm south in phase 3 to en-ter the bottom corridor. Finally, when the spread is nolonger greater than 15, the swarm heads west in phase 4

Fig. 5 The 4 stages of decision making in solution y-b. This is achieved in real-time with tools we havecreated to animate the states of the BT.

to travel down the last corridor. This visualization makesit very easy to understand the key decisions that are madeto change the swarms movement.

4.3. Scenario CoverageBased on the behaviours of the evolved solutions we

see that strategies specialize to their trained scenarios.We also assessed each solution in all other scenarios tosee whether solutions could generalize to other scenar-ios. We summarize the performance of all solutions infigure 6. The trained solutions are shown by rows and thecolumns denote which scenario they were tested against.Each tile shows the average performance of each evolvedsolution in a given scenario. Each tile represents the fit-ness of the individual averaged over 300 trial runs.

We observe that for map X it is easy to achieve highscores. We see that for maps Y and Z the scores de-cline due to increasing difficulty whilst still performingwell. Similarly we observe that performance declines aswe move from objective set a towards e. If we considerthe performance of solutions in different maps we see theexpected trend that solutions trained in map X do not per-form well when assessed in maps Y and Z. However, forsolutions trained in maps Y and Z we see that they per-form far better when applied to other environments. Be-cause of this, we see that overall higher fitness falls underthe leading diagonal of the heat map.

We also see a number of solutions that generalize wellacross many scenarios. In particular, Y-a, Y-b, and Z-d.We see that a solution trained on scenario Y-a performswell on all map X and Y scenarios, with a slight declinein map Z. Alternatively, Y-b struggles in map X but per-forms very well in map Y and map Z. Y-b does not per-form well in map X as the first decision making step isfitted to the fact that there are walls in environment Y.The tree directs the swarm west and waits for the condi-

Fig. 6 We evaluate each evolved solution (rows) againstall scenarios investigated (columns). High perfor-mance is associated with higher fitness.

tion, µy>2, to trigger before moving to the next action.Without a wall, µy will approximate to zero when head-ing west and reaching the wall. Hence no further actionwill be triggered resulting in low performance when ex-ecuted in map X. Alternatively, Y-a similarly sweeps eastand west but relies only on observing when the swarmreaches each end wall using µx to decide when to changedirection. This means that the strategy relies on the di-rected field behaviours and the autonomous nature of theswarm to navigate around any obstacles rather than cre-ating a specialized strategy that learns the nature of theenvironment. This approach is a good balance of super-visory control whilst taking advantage of the emergentproperties of the swarm.

From this finding we can also identify that spread con-ditions inherently specialize to particular environments.We see similar sweeping behaviours evolved in map X asthose created by Y-a but using spread as the main decisionmetric, however, these kinds of strategies perform poorlyin the walled environments. This is because the swarmsspread is directly linked to the shape of the environmentwhilst certain conditions using center of mass are far lessdependent such as those in Y-a. Therefore we can con-clude by stating that to produce generalizable solutions,center of mass is a more suitable metric in comparison tospread.

5. DISCUSSION

The ability to continuously develop swarm controlstrategies through artificial evolution makes the explo-ration of many different scenarios and constraints rapidand systematic. In this case, we were able to explore awide set of conditions that could be used to control theswarm and explore how this affects the types of strategiesthat are evolved. Because of this we were able to identifya number of solutions that generalize as an outcome of

these scenarios. In the same way we could explore higherlevel questions such as the affect of communication lim-itations in HSI systems across many varying conditions.We would be able to assess a large set of scenarios thatcould help us identify certain rules or patterns when con-trolling swarms with limited communications.

Findings that are produced through this approach canbe useful to dictate how we design HSI systems as awhole and aid in training of human operators. It has beenhighlighted that an operators understanding of swarm dy-namics and time spent controlling swarms can have sig-nificant affects on the swarms performance [11]. By pre-senting solutions that we have artificially evolved, oper-ators can use these solutions as a starting strategy and ifneeded, make adjustments if improvements can be made.This is made easier with BTs as they are human readable.

In this scenario we could use the knowledge we havecollected to modify the operators display. Our solutionhas shown that the swarm can be controlled with knowl-edge of only the center of mass and spread. Thereforewe could modify the interface to represent the swarm asa single entity represented by a centroid and a circular re-gion representing the swarms spread. The centroid wouldmove in real time to show the position of the swarm andthe red circular region would grow and shrink to showchanges in the formation. This would make control ofthe swarm simpler as the swarm is presented as a singleentity and reduces the need to understand the swarm’s dy-namics. This idea has been explored in Becker’s work onHSI using crowd-sourcing techniques [2].

6. CONCLUSION AND FUTURE WORK

We expect that the evolution of supervisory strategieswill be a useful tool to help train operators by inferringpotential solutions and predicting the best approach forcertain scenarios. This creates a structured approach tooperator training and HSI system design towards achiev-ing better performance and ultimately real world deploy-ment. Our results showed that we could produce strate-gies which generated systematic environment coverageand specialized to their scenarios. This was achievedthrough observation of the swarm state and identifyingthe prior knowledge of the objective locations. We wereable to compare the types of strategies evolved and identi-fied a set of unique solutions which had high performanceacross different scenarios.

Future work will explore how further limitations inavailable knowledge of the swarm will change evolvedstrategies. We can then identify a distribution of per-formance under increasingly limited swarm state knowl-edge. This is important as real world communication lim-itations will affect our ability to view and subsequentlycontrol swarms. We also aim to explore additional met-rics to represent the swarm state and how this could gen-erate different solutions. This could identify priorities to-wards certain metrics and determine what data is crucialfor swarm control. Human trials will be performed in thefuture to test the effect of performance given knowledge

of evolved solutions. Their performance can then be com-pared against a control group of individuals that have noprior knowledge of potential solutions. This will allow usto assess the benefit of evolved approaches and direct thefocus of future investigations.

7. ACKNOWLEDGEMENTSThis work was funded and delivered in partnership be-

tween the Thales Group and the University of Bristol, andwith the support of the UK Engineering and Physical Sci-ences Research Council Grant Award EP/R004757/1 en-titled ”Thales-Bristol Partnership in Hybrid AutonomousSystems Engineering (T-B PHASE)”.

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