Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]

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1 DYNAMIC SHORTEST PATH SEARCH AND SYNCHRONIZED TASK SWITCHING Jay Wagenpfel, Adran Trachte 2 Outlne Shortest Communcaton Path Searchng Bellmann Ford algorthm Algorthm for dynamc case Modfcatons to our algorthm Synchronzed Task Swtchng Combnng tasks An algorthm for synchronzed task swtchng Tme complexty Summary Communcaton 4 Bellman Ford Algorthm Algorthm Restrctons n Dynamc Case Setup: Network of agents, transmttng data to the base. Communcaton costs, whch ncrease wth ncreasng dstance between agents, should be kept low Routng protocol needed, to fnd the shortest path to the base Bellman Ford Shortest Path [] Bellman Ford Example 6 Setup: Set of edges whch are connected over vertces. Goal: Fnd shortest path from each agent to base. Notaton: 2 2 c = communcaton cost to base of agent N = set of neghbors of agent vj = com. cost from agent to agent j wth j N d = downstream neghbor of agent Update rule for every agent: c = mn( cj + vj) j N d = arg(mn( c + v )) j N j j 2 CCtB = + 2 = 2 6. Intalzaton a) c B = b) c = 2. Agents search for possble new shortest CCtB = + 2 = path to base. 2 CCtB = + = 2 c = mn( cj + vj) j N d = arg(mn( c + v )) j j j N [] Rchard Bellman On a Routng Problem 9

2 Bellman Ford Example 7. Intalzaton a) c B = b) c = 2. Agents search for possble new shortest path to base.. Shortest path s found after maxmum of N teratons Bellman Ford Algorthm Algorthm Restrctons n Dynamc Case Dynamc shortest path search Loopng n dynamc case 9 Changes to statc case: Topology of network changes Weghtngs of vertces vary over tme Certan problems occur n dynamc case loopng communcaton loops occur, connecton to base gets lost longest path search because of old nformaton n the network, agents choose wrong path to base ) Agents n steady state 2) Downstream Neghbor moves away communcaton cost ncreases ) Agents search for new shortest path Loopng n dynamc case ) Agents n steady state 2) Downstream Neghbor moves away communcaton cost ncreases ) Agents search for new shortest path 2 Longest path search

3 Idea of Dynamc Shortest Path Search 4 Dynamc Shortest Path Search Regular Search Hgh frequency search Problem: changng weghts and tme delayed nformaton propagaton leads to loops and wrong pathes But: No problem n statc case, because here, the communcaton cost only decreases whle convergng to shortest path Idea: Fx the communcaton costs and topology between agents and use statc computaton to fnd shortest path Advantages: Fnds real shortest path for gven setup (no longest path, loops) Dsadvantages: Needs tme to converge, durng ths tme, not optmal path Realzaton of Dynamc Shortest Path Search Realzaton of Dynamc Shortest Path Search 6 Procedure: ) Fx communcaton costs to all negbor agents 2) Start new statc shortest path search ) When shortest path search s fnshed, set new found downstream negbhor as new d.n. 4) Go to frst step. Two Problems: How do agents know when to fnsh the shortest path search and start wth a new one wth updated communcaton costs? How assure that new found shortest path downstream neghbor s stll n communcaton range? Frst Problem: Idea: Base s central processng unt and therefore can be used as a quas synchronsaton module to start the new search. New search should start, after shortest path to base was found. Wat worst case tme for shortest path search. New search sgnal wll be propagated over whole communcaton range from each agent. New search sgnal s faster than shortest path search. Base sends new search sgnal to all agents n communcaton range after worst case computaton tme for shortest path Realzaton of Dynamc Shortest Path Search Realzaton of Dynamc Shortest Path Search 7 Worst case tme for new shortest path to base: Worst case topology s connected chan Worst case update s, f agents farest away from base update frst. Second Problem: Idea: Agents should not move out of communcaton range whle shortest path search assume maxmum speed of agent v max worst case f agents move n opposte drecton wth maxmum speed, durng whole worst case computaton tme R S R R S = 2 * v max * (N ) * t R t maxmum tme wthn communcator updates all communcators update at least once wthn tmespan t v max v 2 * v max max R S = R 2 * v max * (N ) * t t total = (N ) * t s worst case tme to fnd shortest path 2 * v max * (N ) * t

4 N = 4 + t =.2s v max = m/s R = 2m 2 Dynamc Shortest Path Search Analyss R S = m t total =.s TP: L [2] + KTF 9 Dynamc shortest path search [2] L et al Dstrbuted Cooperatve Coverage Control of Sensor Networks 2 Dynamc Shortest Path Search Lmtatons 2 Worst case watng tme: (N ) * t 22 Dynamc Shortest Path Search Increases wth growng number of agents N and computaton tme Δt. Tme between new searches becomes to bg, and therefore the error ncreases. Regular Search Hgh frequency search Lmted neghbor range: R S = R 2 * v max * (N ) * t Decreases wth growng number of Agents N, computaton tme Δt and v max. Maybe R S becomes to small for a proper shortest path search. 2 Hgh search frequency Motvaton N = 4 + t =.2s v max = m/s R = 2m Idea: Increase the frequency wth whch a new search starts. Securty Radus R S would be bgger and tme between searches smaller R S = 2m t total =.s TP: L [2] + KTF steps 7 steps 24 Hgher new search frequency [2] L et al Dstrbuted Cooperatve Coverage Control of Sensor Networks 2

5 Hgh search frequency Hgh search frequency 2 26 Error n % of optmal communcaton cost For hgh frequency search, shortest path s not guaranteed! realcctb - optcctb * optcctb 27 Dscusson Regular search Postve: Fnds shortest path usng only local nformaton (no loopng etc.) Negatve: Strong dependence on number of agents etc. Error whle watng for worst case convergence tme Hgh frequency search Postve: Reduces dstance to optmum Negatve: Shortest path s not guaranteed to be found n computaton tme Rough knowledge of topology needed 2 Synchronzed Task Swtchng Combnng Tasks An Algorthm for Synchronzed Task Swtchng Tme Complexty Smulaton Results Combnng Tasks Combnng Tasks 29 Coverage control: Maxmzng the probablty of detectng events. Most mportant areas of the msson space are well covered. Exploraton of the msson space: Use of deployment algorthms. Maxmze the area covered by all agents. Combne both tasks: Frst explore the msson space. Then cover the most mportant areas. Enables the agents to cover areas unreachable f only usng coverage control. Swtch task when the exploraton task s fnshed.

6 Combnng Tasks How do the agents know that the exploraton task s fnshed? For each agent, only local nformaton s avalable. But, nformaton about all agents (=global nformaton) s necessary. Use of consensus lke algorthms Enables each agent to determne the state of the network. Task swtch s performed, when all agents agree that the exploraton task s fnshed. 2 Notatons B drectonal communcaton between agent and ts neghbors N N( k) = { j {,.., n} s s < R} j Communcaton topology s undrected graph G: A Adjacency matrx of G aj = a = f j N ( k) j D Degree matrx of G d = aj j State varables for consensus: z Task state of agents z = x Consensus state f agent has fnshed frst task An Algorthm Assumptons: There exsts a tme k such that A(k)=A(k ) for all k k. There exsts a tme k k s.t. z(k)= for all k k. If A(k+)A(k), that s there exsts s.t. N (k+)n (k), then z (k+)= even f agent has fnshed frst task. More Notatons: Z dag(z(k)) I nn dentty matrx n vector wth all elements equal to d d = N s the cardnalty of the set N 4 An Algorthm Algorthm: If z =, set each agent s consensus varable x to the average value of the sum of ts own task state z and the consensus states of ts neghbors. Else, set x =. Update rule: For each agent: x( k+ ) = z xj( k) + d + j N Whole network: xk ( + ) = Z ( D+ I) [ A xk ( ) + ] An Algorthm State of the network and task stwtch: If at least one agent has not fnshed the frst task, x (k)< for all agents. If all agents have fnshed the frst task, z(k)= and for every agent, x (k) for k. Perform task swtch f x s suffcently close to. Convergence of Algorthm: For constant z(k), system s an asymptotcally stable LTI system wth constant nput. There s always one unque equlbrum pont x EP and x EP = for z=. 6 Threshold for Task Swtch When s x suffcently close to? Task swtch f x > δ, wth δ < If δ s too small, false task swtch mght happen. How to determne δ? Derve from statc case where no topology changes happen. Show that even under swtchng topology, x (k) s never larger than max x wc n the worst case statc topology.

7 Tme Complexty Tme Complexty 7 In [] the term Tme Complexty s ntroduced: The Tme Complexty TC s the tme an algorthm needs to perform, dependng on the number of agents n. For the task swtch, a sensble noton s the tme from when the last agent fnshes the frst task untl the last agent starts wth the second task. Upper bound: An upper bound to the order of the tme complexty s gven by: ln( δ ( n)) TCA O( ) n ln ( ) n Proof: At step k, let be the agent such that x mn (k ) := x (k ) x j (k ) for all agents j. Then n the next step for agent : x( k+ ) = xj( k) + z ( k) ( d x( k) + ) d + j N ( ) k d x ( ) + k = T The smallest possble value for x (k +) s acheved by maxmzng the number of neghbors. x( k+ ) ( ( n ) x( k) + ) n [] Martnez, Bullo, Cortes, Frazzol On synchronous robotc networks Part II: Tme complexty of rendezvous and deployment algorthms Tme Complexty Tme Complexty 9 4 Proof: The value x mn (k +) := x (k +) provdes a lower bound on the consensus values of all agents j n step k +: xj ( k + ) xmn ( k + ) = ( ( n ) xmn n ( k) + ) Ths can easly be seen: Suppose there exsts x l (k +) < x mn (k +) xl( k+ ) = xj( k) + ( dl xmn dl + j N ( ) k dl + ( k) + ) ( ( n ) xmn N ( k ) + ) = xmn ( k + ) Proof of Tme Complexty: Ths lower bound on the consensus value of all agents can be generally descrbed by: xmn ( k+ ) = ( ( n ) xmn ( k) + ) n The soluton to ths dfference equaton for k k s: k k n xmn ( k) = ( xmn ( kt )) n Wth the swtchng condton x > δ and k T the step when all agents have swtched to the second task t follows: ln ( δ ( n) ) TC = kt k n ln ( n ) Tme Complexty Tme Complexty 4 42 Smulatons: Smulaton of task swtch for dfferent topologes and numbers of agents n task before swtch. Chan topology: Watng for only one agent: Watng for all agents: Smulatons: Random topology: Watng for one agent: Watng for all agents:

8 Tme Complexty 4 Smulatons: Random topology: Watng for one agent: Watng for all agents: Smulaton Results: No Exploraton Smulaton Results 46 Jont Event Detecton Rate Wthout Exploraton Wth Exploraton Task-Swtch Smulaton Results: Wth Exploraton Summary Summary We dscussed problems n searchng the shortest communcaton path n the dynamc case. We presented an algorthm to compute the shortest path n the dynamc case. We ntroduced an algorthm to synchronze a task swtch n a dstrbuted network. We dscussed the tme complexty of the presented algorthm. We presented a example smulaton to show the mproved performance of the combned tasks.