外文翻譯--機器人傳感器的網(wǎng)絡(luò)-25頁[中英word]【中英文文獻譯文】,中英word,中英文文獻譯文,外文,翻譯,機器人,傳感器,網(wǎng)絡(luò),25,word,中英文,文獻,譯文 附錄Ⅰ 機器人傳感器的網(wǎng)絡(luò) 一般的機械手工程自動化測知和知覺實驗室 賓夕凡尼亞州，費城， PA 的大學(xué), 美國 摘要:以知覺的數(shù)據(jù)從分配的視覺系統(tǒng)吸取了的 exteroceptive 為基礎(chǔ)的硬未知的物體和同時追蹤的二維歐幾里得幾何的空間的這一個紙住址即時位置的問題和被網(wǎng)絡(luò)的移動機械手的定方位判斷. 對于隊局限的充份和必需的情況被計劃. 以統(tǒng)計的操作員和曲線圖搜索運算法則為基礎(chǔ)的一個局限和物體追蹤方式為與異種的感應(yīng)器一起本土化的一隊機械手被呈現(xiàn). 方式在有被裝備全方向的錄像機和 IEEE 802.11b 無線網(wǎng)路的像汽車一樣移動的機械手的一個實驗的月臺被實現(xiàn). 實驗的結(jié)果使方式有效. 關(guān)鍵詞:合作的局限; 多機械手形成; 分配了感應(yīng)器網(wǎng)絡(luò); 感應(yīng)器數(shù)據(jù)融合物 介紹 以使一隊移動的機械手自治地在一些里面航行需要了形成而且更進一步運行像監(jiān)視和目標獲得這樣的合作工作, 他們一定能夠以形成和一個全球的叁考框架本土化他們自己 [1,2]. 因此, 該如何估計機械手的位置和定方位 (姿勢) 以精確的和有效率的方式是特別興趣. 我們對這一張紙的興趣是在二空間的特別歐幾里得幾何的空間 SE(2) 中本土化一隊異種的機械手和用從異種的感應(yīng)器被獲得的數(shù)據(jù)本土化目標. 明確地, 我們對情況感興趣為哪一個所有的機械手以形成能被本土化包圍. 我們的局限方式接近地被講到被呈現(xiàn)的那些. 在某種意義上機械手用來自它自己的感應(yīng)器的那一個來臨可以使用他們的隊友感應(yīng)器數(shù)據(jù) (或一些被相關(guān)的數(shù)據(jù)) 和數(shù)據(jù). 在那一張紙, 機械手使用分配了測知改善自己的局限或目標局限 [1]. 因為比較多機械手局限問題藉由聯(lián)合被使用最少的機械手交換的數(shù)據(jù)一致最佳化，過濾器的兩者文件的方法學(xué)已經(jīng)呈現(xiàn)解決. 最近的文學(xué)使用曲線圖做模型感應(yīng)器網(wǎng)絡(luò)和合作的控制方案 [5, 6]. 在曲線圖硬理論上的結(jié)果 [7-9] 可能是直接地在 R2 適用于多機械手系統(tǒng) (裁判員. [10, 11]). 然而，相對一點的注意已經(jīng)一起支付到網(wǎng)絡(luò)生觀察, 這對照相機的網(wǎng)絡(luò)是特別地重要的. 這一張紙在以統(tǒng)計的操作員和簡單的曲線圖搜索運算法則為基礎(chǔ)的 SE(2) 為隊局限和物體追蹤呈現(xiàn)不同的方式. 此外, 不同的早先方法, 我們在一個如此的方法中制定問題隊局限的問題和物體追蹤能被相同的運算法則解決. 我們也表示 , 被講到早先作品的最優(yōu)性的利益能如何容易地在我們的方法被吸收. 為例, 這一張紙表示該如何合并一個廣大的過濾器 (EKF) 改善物體追蹤. 在結(jié)束, 使我們的方法有效, 我們出示實驗的結(jié)果以有被裝備全方向的錄像機和一個 IEEE 802.11b 無線網(wǎng)路的一群五個像汽車一樣的自治機械手 (見到 Fig.1) 我們承擔(dān)一個全方向的發(fā)射器和接收器和每個機械手能聽的每個機械手有 每隔一機械手. 因此, 所有的機械手以合作的樣子以形成能交換他們的估計而且本土化他們自己. 注意我們不承擔(dān)任何類型的固有感受器數(shù)據(jù) , 像是機械手的來自任何的不活潑的感應(yīng)器的速度和加速. 2 多機械手的區(qū)域化 形成 為了要考慮一隊機械手是否能被區(qū)域化,如果這數(shù)據(jù)是適當(dāng)?shù)?，融化來自不同的感?yīng)器的可得的數(shù)據(jù)而且查證是必需的. 對于 SE(2) 的一隊 n 機械手, 局限是表示機械手位置和定方位的特色的 3n 坐標的決心. 因此，見到是必需的如果 3n 獨立的測量是可得的. 因為每個測量在 3n 坐標上敘述一個限制, 我們?yōu)樗械南拗瓢l(fā)展了一個功能獨立的測試. 因此, 我們定義等級將會允許的一個限制點陣式 我們查證隊是否能被本土化. 對于每范圍和舉止測量, 在框架 Bi 的在坐標上的限制有被: 一雙生測量, jk φ和 ik φ ,包括機械手 Rj 和 Rk, 造成下列的類型 3個限制. 最后, 生測量, ij φ和φ kj,包括三機械手 Ri ， Rj 和 Rk, 的任何雙造成下列的類型 4個限制. 這些限制能以形式被寫: 在 L1 是一個測量的線組合的地方, 和 h 在一些身體修理的叁考框架中是形狀變數(shù)的一個非線性功能. 只有能用來描述網(wǎng)絡(luò)的限制的四類型. 能被寫的所有的其他相等在上述的限制相等上功能依賴. 藉由區(qū)別四個限制相等, 我們使描述機械手坐標的可允許小變化 (相等地速度) 的表達. 在～之后這一個程序為 M 可能的限制給一個 M × 3n 點陣式作為叁考框架 Bi: 如果 n 機械手的 3n能在一個不活潑的框架被估計，在 SE(2) 的 n 機械手的定義 1 一隊被說是能地方化的. 評論顯然地被講到在系統(tǒng)理論中的可觀察性[14]- 如果一個隊是能地方化的超過任何的時間間隔, 系統(tǒng)完全觀察得出. 然而, 我們將會在一個即時又靜態(tài)的設(shè)定中使用定義 1, 而且如此克制不要使用系統(tǒng)理論上的記號法. 評論 2 我們也能需要要在設(shè)定它是唯一的必需品估計 3n 的地方的親戚被本土化的隊。n 的 3 個坐標。在一個身體叁考框架中的個機械手. 在 SE(2) 的 n 機械手的定理 1 一形成是能地方化的只有當(dāng)如果 N=3 n。2ng.nb.nr ≤ 0 （ 11 ng ， nb 和 nr 是被不活潑的或全球定位感應(yīng)器做的測量數(shù)量,生的） 感應(yīng)器和范圍感應(yīng)器分別地查證是容易的證明被任何全球的定位感應(yīng)器做的每個絕對的位置測量能直接地用來估計二州變數(shù) , 和每舉止和范圍測量將會至少增加在結(jié)構(gòu)或形成的形狀方面的一個限制. 因此, ng ，全球的位置感應(yīng)器， nb, 生感應(yīng)器, 和 nr, 范圍感應(yīng)器最多將會提供 2ng+nb+ nr 獨立的測量. 自從 3n 州變數(shù)之后必須被估計,2ng+nb+ nr 一定至少相等 3n. 用有限制的測知能力提供機械手的形成, 定理 1 提供一種簡單的必需品情況沒有考慮形成幾何學(xué)容易地查證. 注意像劃時代的感應(yīng)器，圓規(guī)和不活潑的測量單位 (IMUs) 這樣的另外感應(yīng)器,能以筆直的方式被與這一個結(jié)構(gòu)合并. 圖. 4 在 SE 為一群三個移動的機械手抽取樣品舉止測知曲線圖(2). 三角法地, 曲線圖 (b) 總是能轉(zhuǎn)換成基于感知外界刺激在計算的網(wǎng)絡(luò)得到的知覺數(shù)據(jù). 4 局限方式 我們的局限方式承擔(dān)每個機械手為溝通和測知讓一個獨特的確認 (身份證) 兩者. 起先 , 我們也承擔(dān)物體角落有清楚的測知身份證. 如果基于式樣分類的簡單啟發(fā)用來解決聯(lián)合機械手測量的問題，這一項假定能被放松. 我們的方式是集中的在某種意義上每個機械手收集來自其他的機械手的測知數(shù)據(jù)而且聯(lián)合使用的這數(shù)據(jù)它自己的，則只有曾經(jīng)叁觀所有節(jié)樹藉由在更深入地去之前在相同的深度拜訪所有的節(jié). 在這里，因為我們沒有在考慮樹, 節(jié)能被拜訪超過一次. 因此, 如果有曲線圖的根和一個特定的節(jié)之間的超過一條路徑, 這些路徑將會被用. 在第一個，一個頂點被拜訪, 它的位置被估計. 從然后在,之上每次一個節(jié)被到達, 它的先前估計姿勢被和使用這條新的路徑的最近被估計的姿勢結(jié)合. 因為一個節(jié)被允許被拜訪超過一次,理論上, 運算法則可以在環(huán)中進入. 環(huán)引起互相依賴的情形哪里, 舉例來說, vi 的姿勢能被計算使用來自 vj 的數(shù)據(jù)和 vj 的姿勢能被計算使用來自 vi 的數(shù)據(jù). 為了避免這一個問題, 最初的曲線圖被轉(zhuǎn)換成一個直接的曲線圖哪里環(huán)被移動. 新的曲線圖以身為被選擇如起源的機械手的根與一棵樹 (然而, 它不是一棵樹) 類似. 環(huán)被藉由在有著相同的深度的機械手之間除去邊緣避免. 因為物體沒有測量, 他們從不不再產(chǎn)生環(huán)和他們的優(yōu)勢正在劃除. 一樣的在二個機械手之間以單向邊緣發(fā)生. 在不同深度的二個機械手之間的雙向性邊緣也可能產(chǎn)生環(huán). 因為運算法則視曲線圖為一棵樹而且從不向根移動，所以這些情形被避免. 例外與總是被獨立地方向跟隨的單向邊緣一起做. 對于情形的圖 5 表演被源自的曲線圖的一個例子和運算法則的四個步驟呈現(xiàn).這實際上是 4 R's 和 R' s 的組合當(dāng)?shù)氐臏y量. 因為 R4 直接地不能夠本土化 R1, 一條經(jīng)過 R3 的間接路徑被需要. 和早先的運算法則的議題之一是一些邊緣 (像是在早先的例子 e 12 和 e 21) 不被用于機械手的和物體的姿勢判斷. 為了避免浪費有用的數(shù)據(jù)，一可以在假定機械手的定方位的二個部份中分開運算法則和位置能分開地被計算. 二個部份是: 1) 使用相同的運算法則的機械手的定方位的判斷; 而且 2) 機械手的判斷和使用線性的目標的位置重量了承擔(dān)在一個第三個機械手 (Rk) 同等的人物框架的二個機械手 (Ri 和 Rj) 的位置線地被講的最少的正方形方法被: ki θ和 ij φ被假定被知道的地方. 博學(xué) ki θ的需要在二個部份中解釋運算法則的區(qū)分. 除計算定方位之外，運算法則的第一部份負責(zé)計算被連接到計算的網(wǎng)絡(luò)的機械手的數(shù)字和, 結(jié)果, 為定義要計算的變數(shù). 一些邊緣仍然在運算法則的這一個部份被浪費, 什么在觀察之下是合理的舉止測量容易比范圍一些好很多. 另外的進步能被實行使用一個動態(tài)的過濾器估計物體的位置. 因為物體是硬的,如果和它的角落有關(guān)聯(lián)的一個模型被用,即使當(dāng)一些角落不能夠被機械手見到，追蹤能被運行. 一簡單的不連續(xù)的樣板物體的尺寸必然地不被知道的地方是 , vxj 和 vyj 是 j O 的速度成份的地方 , dj 和 j θ是 j O 和 j 之間的邊緣的大小和定方位+ O, T 是樣品時間, 和ω是物體有角的速度. 這一個模型考慮物體的速度成份是持續(xù)的. 因為它不總是真實,在過濾器計畫期間, 低的價值一定被指定給表現(xiàn)模型的三條最后線的信心水平的變數(shù). 因為一個非線性模型被用, 一個廣大的過濾器 (EKF) 是必需的. 被用于這一個過濾器的測量將會是 x 和與起源機械手相關(guān)的每個物體角落的 y. 一經(jīng)機械手的姿勢被估計, 在情緒商數(shù)的變形. (18) 而且 (19) 用來計算測量的矢量和它的共分散. EKF 在運算法則中介紹另外的一個步驟. 因為測量的組合現(xiàn)在被過濾器運行，所以因此，與物體角落相關(guān)的曲線圖的頂點一定從局限步驟被移動. 4.2 分配×集中 當(dāng)早先的集中方式的時候工作得很好為一相對地小群體的機械手，網(wǎng)絡(luò)議題, 如此的當(dāng)做交通和延遲, 和當(dāng)我們正在用數(shù)十或數(shù)以百計機械手考慮團體的時候，在一個單身的機械手的缺乏計算資源能造成重要的問題. 在這些情況分配的運算法則的使用變成強制性. 然后, 我們想要一個方法使用相同的運算法則而且減少在藉由使分散那處理的部份被需要的計算和帶寬. 大體上, 可動裝置機械手只需要當(dāng)?shù)氐臄?shù)據(jù)運行一件工作. 因此，如果每個機械手地方性地收集來自它的立即鄰居的數(shù)據(jù)而且聯(lián)合這一筆數(shù)據(jù), 它有它需要大部份的時間的數(shù)據(jù). 在那情況哪里一個機械手能在團體中聽每個機械手的話 (i.e., 在它的溝通范圍里面的所有機械手是), 它可以接受所有的數(shù)據(jù)而且，舉例來說，只在來自它的特定的距離 (測量被深度在曲線圖中) 里面本土化機械手位于. 二者擇一地, 機械手也可能是更選擇的而且選擇只本土化能見到一個給定的物體或位置的機械手. 在這情況, 機械手將會跟隨只有一些令人想要的 在曲線圖上的路徑, 變更運算法則小一點點. 另外的一個方法使分散處理, 當(dāng)特別網(wǎng)絡(luò)被用，而且機械手不能夠直接地和彼此說話的時候，這可能是有用的, 將只本土化附近的機械手 (溝通系列的機械手). 在這一種情形中，當(dāng)給溝通信息和很多的帶寬路由器被保護的時候，機械手不需要工作. 如果全球的數(shù)據(jù)是必需的, 一個機械手能問一或者更多它的鄰居對于數(shù)據(jù) (不生的數(shù)據(jù)) 而且計算使用變形 (18) 的其他機械手 (或一個特定的機械手) 位置和 (19). 因為整個的團體是可得的，當(dāng)即時的估計不是一個限制的時候，這一個程序能回歸地被運行直到全部州數(shù)據(jù). 4.3 全球的局限 迄今，一經(jīng)每個機械手在它自己的叁考框架中計算其余者的位置在一個身體叁考框架的唯一的比較局限被考慮. 當(dāng)在 W 的與一個固定的框架相關(guān)的測量可用來根機械手的時候, 簡單的變形能用來把比較的估計轉(zhuǎn)變成全球的坐標. 5 實驗的結(jié)果 為了示范早先的策略，我們在被裝備全方向的照相機的一隊五個像汽車一樣的移動機械手上呈現(xiàn)局限實驗和 IEEE 802.11(見到無花果樹. 1) 被引導(dǎo)網(wǎng)絡(luò). 促進視覺的處理, 每個機械手和物體的角落與為每個機械手提供一個獨特的感應(yīng)器確認的一種特別的顏色一起作記號. 和一部外部的計算機的一臺被校正的在頭上的照相機用來本土化環(huán)境的隊. 這部外部的計算機也收集在溝 通網(wǎng)絡(luò)里面被廣播的感應(yīng)器數(shù)據(jù). 我們的全方向照相機的限制是他們的決議以物體的距離減少. 如此我們承擔(dān)范圍數(shù)據(jù)受制于為價值通常用對范圍的第四力量的不一致比例項分配了噪音比 1.5 m 小, 當(dāng)舉止閱讀蒙受了通常分配的持續(xù)不一致的噪音的時候. 范圍評價比 1.5 m 之前更大忽略. 圖 6 表演地面-事實的數(shù)據(jù)獲得了使用一臺被校正的在頭上的照相機, 對于實驗在哪一個一個機械手 (R1) 向被另外的一個機械手本土化的一個目標移動 (R2). 這一個機械手需要透過被其他機械手廣播的數(shù)據(jù)本土化目標. 表現(xiàn)的視野的領(lǐng)域被猛擲的圓周表示 R1 能基本上同時地見到一或二個機械手. 圖 6 表演 R1's 運動在 R0' s 叁考框架的判斷 圖. 6 以被 R2 收集而且分享了過計算的網(wǎng)絡(luò)的數(shù)據(jù)為基礎(chǔ)的對于 O1 的 R1 移動. 點表現(xiàn) R1'被 R0 和真實軌道的連續(xù)線估計的 s 位置. 內(nèi)部的猛擲圓周表現(xiàn)機械手大小和向外的一些表現(xiàn)視野的照相機領(lǐng)域. (哪一個與全球的叁考一致構(gòu)成) 和 R1's 真實的軌道. 圖 7 表演追蹤使用 EKF 的一個三角形的盒子的三個機械手的二個迅速射擊. R0's 叁考框架被顯示. 當(dāng) R0 只用來本土化另外二的時候，機械手 R1 和 R2 能夠見到盒子角落. 即使 R0 不能夠見到盒子角落, 它能夠追蹤使用來自它的隊友的數(shù)據(jù)的盒子. 權(quán)利的迅速射擊表示，當(dāng)一個機械手變盲目的時候，角落共分散增加但是 盒子仍然被追蹤. 如果來自一臺外部的校正照相機的數(shù)據(jù)被用，最后一個結(jié)果表示機械手如何全球性地被本土化. 來自外部的照相機的圖 8 表演二個圖像本土化環(huán)境的機械手. 加上一個中間的，在哪 R4 能被照相機見到，在表 1 被顯示，局限造成這二個結(jié)構(gòu)。 由于數(shù)據(jù)從其他的機械手, 不是被外部照相機見到的機械手能仍然被本土化. 6 結(jié)論 這一張紙?zhí)峁┮粋€策略給在 SE 仿制多機械手形成(2). 使用這圖標模型,我們已經(jīng)為在來自分配的照相機測量的 SE(2) 決定移動機械手的完全能地方化形成源自充份的和必需的情況。 這一張紙經(jīng)過統(tǒng)計的操作員和曲線圖搜索運算法則在 SE(2) 的被分配的機械手-感應(yīng)器的網(wǎng)絡(luò)中也為局限和追蹤呈現(xiàn)一個簡單的和有效率的方法。 給州判斷的被提議的方法在分配的網(wǎng)絡(luò)里面對感應(yīng)器或機械手的數(shù)字感到可攀登。 實驗式地，我們的局限運算法則已經(jīng)被實現(xiàn)和廣泛地在排范圍從物體處理到感應(yīng)器配置的一個多機械手工作的大多樣性用。 附錄Ⅱ Robot-Sensor Networks General Robotics Automation Sensing and Perception Laboratory, University of Pennsylvania, Philadelphia, PA, USA Abstract: This paper addresses the problem of real-time position and orientation estimation of networked mobile robots in two-dimensional Euclidean space with simultaneous tracking of a rigid unknown object based on exteroceptive sensory information extracted from distributed vision systems. The sufficient and necessary conditions for team localization are proposed. A localization and object tracking approach based on statistical operators and graph searching algorithms is presented for a team of robots localized with heterogeneous sensors. The approach was implemented in an experimental platform consisting of car-like mobile robots equipped with omnidirectional video cameras and IEEE 802.11b wireless networking. The experimental results validate the approach. Key words: cooperative localization; multi-robot formation; distributed sensor network; sensor data fusion Introduction In order for a team of mobile robots to navigate autonomously in some desired formations and further perform cooperative tasks, such as surveillance and target acquisition, they must be able to localize themselves in the formation as well as in a global reference frame [1,2]. Therefore, how to estimate robots’ positions and orientations (poses) in a precise and efficient way is of particular interest. Our interest in this paper is localizing a team of heterogeneous robots in two dimensional special Euclidean space SE (2) and localizing targets with information obtained from heterogeneous sensors. Specifically, we are interested in conditions for which all robots in the formation can be localized in the environ- ment. Our localization approach is closely related to those presented by Stroupe etal. in the sense that the robots have access to their teammates sensor data (or some related information) and combinethis information with that coming from its own sensors. In that paper, the robots use distributed sensing to improve self localization or target localization [1]. The methodologies of both papers of Kalman filters have presented solutions for the relative multi-robot localization problem by combining information exchanged by the robots using least squares optimization. Recent literatures use graphs to model sensor networks and cooperative control schemes [5, 6]. Results on graph rigidity theory [7-9] can be directly applied to multi-robot systems in R2 (Refs. [10, 11]). However, relatively little attention has been paid to networks with bearing observations, which is particularly important for networks of cameras. This paper presents a different approach for team localization and object tracking in SE(2) based on statistical operators and simple graph searching algorithms. Furthermore, different from the previous approaches, we formulate the problem in such a way that the problem of team localization and object tracking can be solved by the same algorithm. We also show how the advantages related to optimality of previous works can be easily incorporated in our method. As an example, this paper shows how to incorporate an extended Kalman filter (EKF) to improve object tracking. In the end, to validate our method, we show experimental results obtained with our Clod Buster platform consisting of a group of five car-like autonomous robots (see Fig.1) equipped with omnidirectional video cameras and an IEEE 802.11b wireless network.. We assume that each robot has an omnidirectional transmitter and receiver and each robot can listen to every other robot. Thus, all robots in the formation can exchange their estimates and localize themselves in a cooperative manner. Note that we do not assume any kind of proprioceptive information such as the robot’s velocity and acceleration from any inertial sensors. 2 Localizability of Multi-Robot Formations To consider whether a team of robots can be localized, it is necessary to fuse the information available from different sensors and verify if this information is adequate. For a team of n robots in SE(2), localization is the determination of the 3n coordinates that characterize the robot positions and orientations. Thus, it is necessary to see if 3n independent measurements are available. Since every measurement specifies a constraint on the 3n coordinates, we developed a test of functional independence for all constraints. Accordingly, we define a constraint matrix whose rank will allow us to verify whether the team can be localized. For each range and bearing measurement, the constraints on the coordinates in frame Bi are given by: A pair of bearing measurements, jk φ and ik φ , involving robots Rj and Rk, results in the following Type 3 constraint. Finally, any pair of bearing measurements, ij φ and φkj, involving three robots Ri, Rj, and Rk, results in the following Type 4 constraint. All these constraints can be written in the form: where L1 is a linear combination of measurements, and h is a nonlinear function of the shape variables in some body-fixed reference frame. There are only four types of constraints that can be used to describe the network. All other equations that can be written are functionally dependent on the above constraint equations. By differentiating the four constraint equations, we get expressions describing allowable small changes (equivalently velocities) of the robot coordinates. Following this procedure for M possible constraints gives an M×3n matrix for reference frame Bi: Definition 1 A team of n robots in SE(2) is said to be localizable if the 3n coordinats of the n robots can be estimated in an inertial frame. Remark 1 Localizability is obviously related to observability in system theory[14] — if a team is localizable over any time interval, the system is completely observable. However, we will use Definition 1 in an instantaneous, static setting, and thus refrain from using system theoretical notation. Remark 2 We can also require the team to be localized in a relative setting where it is only necessary to estimate 3n.3 coordinates of n.1 robots in a body reference frame. Theorem 1 A formation of n robots in SE(2) is localizable only if N=3n.2ng.nb.nr ≤ 0 （11） where ng, nb, and nr are numbers of measurements made by inertial or global positioning sensors, bearing sensors, and range sensors, respectively. Proof It is easy to verify that each absolute position measurement made by any global positioning sensor can be directly used to estimate two state variables, and each bearing and range measurement will add at least one constraint on the configuration or shape of the formation. Thus, ng, global position sensors, nb, bearing sensors, and nr, range sensors will provide at most 2ng+nb+nr independent measurements. Since 3n state variables have to be estimated, 2ng+nb+nr must be at least equal to 3n. Given a formation of robots with limited sensing capability, Theorem 1 provides a simple necessary condition to easily verify the localizability without considering the formation geometry. Note that additional sensors, such as landmark sensors, compasses, and inertial measurement units (IMUs), can be incorporated into this framework in a straightforward way. Fig. 4 Sample bearing sensing graphs for a group of three mobile robots in SE(2). Trigonometrically, graph (b) can always transform into (a) based on exteroceptive sensory information available in the computational network. 4 Localization Approach Our localization approach assumes that each robot has a unique identification (ID) both for communication and sensing. At first, we also assume that object corners have distinct sensing IDs. This assumption can be relaxed if simple heuristics based on pattern classification are used to solve the problem of associating robots measurements. Our approach is centralized in the sense that each robot collects sensing information from other robots and combines this information using its own computational resources via the computational network. Thus, given the previous background, localization in a network of mobile robots can be addressed by combining a series of operations that involve transformations and combinations. This algorithm visits, only once, all nodes of a tree by visiting all the nodes at the same depth before going deeper. Here, since we are not considering trees, the nodes can be visited more than once. Thus, if there is more than one path between the root of the graph and a specific node, all these paths will be used. At the first time a vertex is visited, its position is estimated. From then on, each time a node is reached, its previously estimated pose is combined with the pose recently estimated using this new path. Because a node is allowed to be visited more than once, theoretically, the algorithm could enter in loops. Loops cause situations of interdependence where, for example, the pose of vi can be computed using information from vj and the pose of vj can be computed using information from vi. To avoid this problem, the original graph is transformed into a direct graph where loops are removed. The new graph is similar to a tree (however, it is not a tree) with the root being the robot chosen as the origin. Loops are avoided by removing edges between robots with the same depth. Because objects do not have measurements, they never create loops and their edges are never deleted. The same occurs with unidirectional edges between two robots. A bidirectional edge between two robots of different depths may also create loops. These situations are avoided because the algorithm treats the graph as a tree and never moves towards the root. Exceptions are made with unidirectional edges that are always followed independently of the direction. Figure 5 shows an example of the derived graph and four steps of the algorithm for the situation presented. This is actually the combination of 4 R' s and 1 R' s local measurements. Because R4 cannot localize R1 directly, an indirect path going through R3 is needed. One of the issues with the previous algorithm is that some of the edges (such as e12 and e21 in the previous example) are not used in the robots’ and object’s pose estimation. To avoid wasting useful information, one could divide the algorithm in two parts assuming the robots’ orientations and positions can be computed separately. The two parts are: 1) estimation of the robot’s orientations using the same algorithm; and 2) estimation of the robots’ and targets’ positions using a linear weighted least squares method that assumes that the positions of two robots (Ri and Rj) in a third robot (Rk) coordinate frame are linearly related by: where ki θ and ij φ are assumed to be known. The necessity of knowing ki θ explains the division of the algorithm in two parts. Besides computing the orientations, the first part of the algorithm is responsible for computing the number of robots connected to the computational network and, consequently, for defining the variables to be computed. Some edges are still wasted in this part of the algorithm, what is reasonable under the observation that bearing measurements tend to be much better than the range ones. Another improvement can be carried out using a dynamic Kalman filter to estimate the object’s position. Because the object is rigid, if a model that relates its corners is used, tracking can be performed even when some corners cannot be seen by the robots. A simple discrete model where the dimensions of the object are not necessarily known is where vxj and vyj are the velocity components of j O , dj and j θ are the size and orientation of the edge between j O and 1 j+ O , T is the sample time, and ω is the object angular velocity. This model considers that the object’s velocity components are constant. Since it is not always true, during the filter project, low values must be assigned to the variables that represent the confidence level of the three last lines of the model. Because a nonlinear model is used, an extended Kalman filter (EKF) is necessary. The measurements used in this filter will be x and y of each object corner relative to the origin robot. Once the robots’ poses are estimated, the transformation in Eqs. (18) and (19) are used to compute the vector of measurements and its covariance. The EKF introduces another step in the algorithm. Thus, the vertices of the graph relative to the object corners must be removed from the localization steps because the measurements’ combinations are now performed by the filter. 4.2 Centralized × Distributed While the previous centralized approach works very well for a relatively small group of robots, network issues, such as traffic and delays, and the lack of computational resources in one single robot can pose significant problems when we are considering groups with tens or hundreds of robots. In these cases the use of distributed algorithms becomes mandatory. Then, we want a way to use the same algorithm and reduce both the computation and bandwidth needed by decentralizing part of the processing. In general, mobile robots only need local information to perform a task. Thus, if each robot collects information from its immediate neighbors and combines this data locally, it has information that it needs most of the time. In the case where a robot can listen to every robot in the group (i.e., all robots are within its communication range), it could receive all the data and, for example, localize only the robots located within a certain distance (measured by the depth in the graph) from it. Alternatively, the robot could also be more selective and