Probabilistic Robotics
1. Paper overview
In one sentence: Representing uncertain location as a distribution lets a robot combine sensing with motion and choose informative routes, at the cost of computation and approximation. e-beliefe-filtere-coastal-controle-implicationse-roadmape-chapter-boundary
| At a glance | What to know |
|---|---|
| Research problem | Source description A robot must act despite unpredictable environments, limited or faulty sensors, uncertain actuation, approximate internal models and algorithms constrained by real-time computation. These are distinct sources of uncertainty: even reliable sensing cannot make an incomplete model exact. Section 1.1 contrasts environments where engineers can suppress uncertainty with homes, roads and other open settings where decisions must proceed without certainty. e-uncertainty |
| Core mechanism | Source description The organizing concept is explicit probabilistic belief over alternative states. Localization and coastal navigation connect this representation to perception and control: observations change confidence, motion can reduce it, and a planner can seek future observations. These are introductory explanations of a field, rather than a newly specified model. e-beliefe-filtere-coastal-controle-roadmap |
| A key reported result | Coastal navigation: qualitative route comparison: The upper pair of trajectories visibly diverges; the lower pair remains closer and ends near the marked goal. The text claims a higher chance of true goal arrival for the informative route, without quantifying it. Qualitative agreement of estimated and true trajectories; no numerical localization-error or goal-success metric is reported.. Figure 1.2 shows a two-dimensional map of an actual building with Start, Goal, Measured Path and True Path labels. No dataset split, repeated-trial protocol or execution configuration is supplied. A route through relatively featureless open space versus a longer route seeking a corner and following a wall; no named benchmark baseline or controlled ablation. The illustration motivates planning for information. It does not establish an effect size, uncertainty interval, general success rate or the cost of the additional travel. e-coastal-figuree-coastal-control |
| Reading caution | Author claim The authors identify computational complexity and approximation as central costs. Representing entire densities requires more work than keeping a single guess; exact posteriors in continuous worlds are often intractable. Compact Gaussian approximations can help, but may be too crude. The chapter acknowledges remaining computational challenges without measuring runtime or memory. e-implications |
Core contributions
- Source description
The organizing concept is explicit probabilistic belief over alternative states. Localization and coastal navigation connect this representation to perception and control: observations change confidence, motion can reduce it, and a planner can seek future observations. These are introductory explanations of a field, rather than a newly specified model. e-beliefe-filtere-coastal-controle-roadmap
- Author claim
The authors argue that integrating imperfect models with imperfect measurements improves robustness and relaxes accuracy demands relative to traditional model-based and reactive approaches. These broad comparative statements are author claims; this chapter supplies no controlled performance table establishing their generality. e-implicationse-chapter-boundary
- Source description
The roadmap progresses through mathematical foundations, robot models, localization, mapping, and planning/control. The teaching advice instead introduces localization early and returns to mathematics as needed, with hands-on implementation. The historical account positions probabilistic robotics between reliance on accurate models and reliance on immediate sensory feedback, using statistics to combine their incomplete information. e-roadmape-teachinge-history
Figure 1.1. Global localization preserves several plausible positions until motion and another observation distinguish them. Original Figure 1.1. Original paper, p. 4 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Read panels (a) through (e) downward. Horizontal position x follows the corridor; the black curve labeled $\operatorname{bel}(x)$ is the robot's location belief. Panel (a) represents the uniform initial belief described on PDF page 3. In (b), the red $p(z\mid x)$ curve depicts the likelihood of the door observation z at each location, and the black belief has three peaks. In (c), motion to the right shifts and spreads those peaks. A second door observation in (d) concentrates belief near the middle door. Panel (e) shows further motion and renewed spread. The robot icon supplies the illustrated physical position; the distribution expresses what the robot knows. e-beliefe-markov-figuree-filter
What it supports. Sensing and motion have different effects on the information available for localization. An ambiguous observation preserves several hypotheses; uncertain movement carries them forward while broadening them. The next observation becomes informative in combination with that history. The text identifies the process as Bayes filtering and emphasizes retaining low-probability alternatives when sensing can err.
Where the evidence stops. These are schematic densities with no numerical vertical scale, noise parameters or error statistics. The flat initial curve denotes a uniform belief, not zero probability. The chapter explains the sequence but defers detailed Markov-localization algorithms to Chapters 7 and 8.
2. Motivation
2.1 The problem and the proposed response
A robot must act despite unpredictable environments, limited or faulty sensors, uncertain actuation, approximate internal models and algorithms constrained by real-time computation. These are distinct sources of uncertainty: even reliable sensing cannot make an incomplete model exact. Section 1.1 contrasts environments where engineers can suppress uncertainty with homes, roads and other open settings where decisions must proceed without certainty. e-uncertainty
2.2 What this reading follows
A door observation can be accurate yet fail to tell a robot where it is: several doors may look identical. This introductory chapter uses that ambiguity to explain probabilistic state estimation, then asks how a robot should move when its route affects what it can know. The answer can favor a longer path near recognizable structure. The two original figures connect belief updates to active information gathering. This edition covers the complete supplied Chapter 1 sample, including its discussion of computational costs and its roadmap to later material. The examples are qualitative; detailed algorithms and controlled numerical evaluations are outside the supplied chapter. e-beliefe-filtere-coastal-controle-implicationse-roadmape-chapter-boundary
3. Research context
We place the paper in the collection through its world–action interface. The catalog labels and the reading’s assessment are shown separately.
| Catalog dimension | Recorded classification |
|---|---|
| Major category | Foundational work |
| Architecture | Not applicable |
| Prediction paradigm | Not applicable |
| Quadrant | Not applicable |
3.1 Evidence-based assessment
Supports the recorded classification
This chapter supports treating the book as foundational work in state estimation and planning, with architecture, prediction paradigm and quadrant marked Not applicable. It introduces a family of probabilistic methods rather than a single future/action predictor; it provides no architecture supporting One Model, joint future/action prediction or inverse dynamics. The catalog's broader 3D-representation wording is not independently demonstrated by the corridor and two-dimensional map examples. Classification is assessed only at this chapter's scope. e-filtere-coastal-controle-roadmape-history
This is the collection’s architectural analysis, not a new related-work survey. Benchmark comparisons and their protocols appear in Section 6.
4. Problem formulation
4.1 Inputs and outputs
| Inputs | Outputs |
|---|---|
|
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5. Method in detail
5.1 Keep ambiguity after the first door observation
Start with the distinction between the environment and the robot's knowledge of it. The map is known, but the robot's location is initially unknown. Seeing a door narrows the possibilities without identifying which door was seen. In Figure 1.1(b), all three door locations therefore remain plausible. The small probability assigned elsewhere also has a purpose: the sensor may have made a mistake. Section 1.2 explicitly links preserving these unlikely hypotheses to robustness. A distribution can express both ambiguity among similar places and uncertainty about the observation itself. As a reader's interpretation, choosing one peak immediately would discard information that later observations could need. The example motivates preserving alternatives; it does not supply a quantitative threshold for pruning them or an experiment measuring the cost of early commitment. e-beliefe-markov-figuree-chapter-boundary
5.2 Let motion change belief before using the next measurement
Follow the transition from panel (b) to (c). Moving the robot shifts the location hypotheses, but uncertain actuation spreads them. A commanded displacement therefore does not turn the robot's coordinate into known ground truth. The next door measurement is interpreted with this shifted belief, and panel (d) concentrates most probability near one door. Further travel in (e) broadens the belief again. The text calls this posterior estimation a Bayes filter and describes information gained through measurements or lost through uncertain processes. These are inference-time changes in estimated state. They are distinct from the chapter's separate characterization of learning as long-term estimation. Neither discussion specifies a training objective or a numeric update algorithm. The tutorial sequence explains the information flow; the mathematical implementation belongs to later material. e-beliefe-markov-figuree-filtere-implicationse-roadmap
5.3 Plan for the observations a route will make available
Now use the localization lesson to read Figure 1.2. A route through open space can leave few features for correcting uncertain motion. The robot may believe it has arrived while its true position differs. The alternative route seeks a corner and follows a wall, maintaining useful environmental references. Section 1.2 presents the extra travel as worthwhile because the chance of true goal arrival improves. My interpretation is that route quality must account for the information available during execution, alongside geometric progress. This connection is central to uncertainty-aware control, but the chapter does not define a cost function or prove that every wall-following detour is beneficial. Planning anticipates information; control realizes the selected plan. Computational complexity and imperfect belief approximations remain relevant costs when implementing either part. e-coastal-figuree-coastal-controle-implications
5.4 Training and inference
During training
Section 1.3 characterizes robot learning as long-term estimation. It gives no training dataset, loss, optimization schedule, learned modules or training/inference split. The illustrated localization updates are online state estimation; the sample does not describe a neural training pipeline. Detailed mathematical and robot-model treatments are assigned to later chapters. e-implicationse-filtere-roadmape-chapter-boundary
During inference
During operation, measurements and uncertain motion revise the location distribution. For control, anticipated future uncertainty can change which route is selected. This is a conceptual account of estimation and planning with feedback; Figure 1.2 does not document a complete execution protocol, sensor configuration or timing budget. No explicit update equation is printed in the sample, so none is reconstructed here. e-filtere-coastal-controle-chapter-boundary
5.5 Implementation flow
- Represent unresolved location hypotheses
Global localization begins without knowing the robot's position within a known environment. Figure 1.1(a) uses a uniform location belief. Observing a door in (b) creates three peaks because the doors are indistinguishable. Non-door locations retain positive probability because the observation can be wrong. The black curve labeled bel(x) expresses belief over location x; the red p(z|x) curve depicts the door-observation likelihood across locations, where z is the sensor observation in this example. e-beliefe-markov-figure
- Carry uncertainty through motion
From (b) to (c), the belief shifts in the direction of travel and becomes broader because actuation is uncertain. The robot's changed position does not directly reveal its true coordinates to the estimator. The chapter describes this as loss of information through processes in the world; it supplies no transition-model formula or calibrated noise magnitude. e-beliefe-markov-figuree-filtere-chapter-boundary
- Use another observation to disambiguate
The next door observation in (d) concentrates most belief near one door; subsequent motion in (e) shifts and broadens that dominant hypothesis. The text identifies this posterior-estimation process as a Bayes filter and the figure as Markov localization. The observation is interpreted in light of the belief carried through motion, so an initially ambiguous landmark can become informative through a sequence of actions and measurements. e-markov-figuree-filter
- Choose actions for their information consequences
Coastal navigation anticipates uncertainty along candidate trajectories. In Figure 1.2(a), open space provides few features and the estimated and true paths diverge. In (b), the route seeks a distinctive corner and follows a wall. The text describes this as choosing a longer route to stay localized. Planning selects an information-gathering route; control realizes the plan. The sample does not specify the planner's objective, search procedure or low-level controller. e-coastal-figuree-coastal-controle-chapter-boundary
6. Experiments & results
This introductory chapter explains why a robot should maintain a distribution over possible states and consider how its actions change uncertainty. Global localization shows sensing narrowing competing location hypotheses while motion spreads them; coastal navigation shows why a longer, informative route can be preferable. The chapter also identifies computational and approximation costs. It provides conceptual foundations and qualitative illustrations, with detailed algorithms deferred to later chapters.
The complete supplied Chapter 1 contains exactly two scientific figures: the schematic Markov-localization sequence and the qualitative coastal-navigation path comparison. Both are retained. It contains no quantitative benchmark table, ablation, detailed architecture diagram, training plot or explicit update equation. The roadmap and figure captions defer mathematical foundations, robot models and the illustrated algorithms to later chapters, which are unavailable here. Consequently this edition uses two original figures and provides no fabricated table or ablation. The plots lack the scales, calibrated noise settings and trial statistics needed to infer numerical performance. Identity HTML supports book attribution only and is not a scientific visual. e-chapter-boundarye-markov-figuree-coastal-figuree-roadmap
6.1 Read the original evidence
Figure 1.2. Coastal navigation uses recognizable structure to help preserve localization along the route. Original Figure 1.2; images credited in the source to Nicholas Roy, MIT. Original paper, p. 5 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. In each panel, begin at Start near the lower center and trace both labeled paths toward Goal at the upper left. The graphic calls one curve Measured Path; the accompanying text describes it as the estimated path. Compare its separation from True Path in (a) and (b). The upper route crosses relatively open space and the two endpoints diverge. The lower route approaches a distinctive corner and follows a wall, as explained on PDF page 6, with the paths remaining closer. Use the labels and the paired trajectories to read the comparison; the map provides no metric scale from which to calculate travel distance or position error. e-coastal-figuree-coastal-controle-chapter-boundary
What it supports. A route changes which observations the robot can obtain and therefore how confidently it can localize. The authors use the lower, longer route to illustrate active information gathering and claim a higher chance of actually reaching the goal. The visible path agreement supports the qualitative motivation, without quantifying the improvement or its travel cost.
Where the evidence stops. The chapter identifies an actual-building map and coastal-navigation algorithm outputs, but supplies no sensor configuration, execution protocol, repeated trials or success metric. The pair is not an ablation isolating information value. Detailed algorithm discussion is deferred to Chapter 16.
6.2 Results and evaluation conditions
| Task & protocol | Reported result | Comparison & interpretation |
|---|---|---|
| Coastal navigation: qualitative route comparison Figure 1.2 shows a two-dimensional map of an actual building with Start, Goal, Measured Path and True Path labels. No dataset split, repeated-trial protocol or execution configuration is supplied. | The upper pair of trajectories visibly diverges; the lower pair remains closer and ends near the marked goal. The text claims a higher chance of true goal arrival for the informative route, without quantifying it. Qualitative agreement of estimated and true trajectories; no numerical localization-error or goal-success metric is reported. | A route through relatively featureless open space versus a longer route seeking a corner and following a wall; no named benchmark baseline or controlled ablation. The illustration motivates planning for information. It does not establish an effect size, uncertainty interval, general success rate or the cost of the additional travel. e-coastal-figuree-coastal-control |
6.3 Ablations and diagnostic examples
No ablation or diagnostic visual is included in this edition. The source-specific visual limitations are recorded above.
7. Analysis & limitations
7.1 What the evidence leaves open
The authors identify computational complexity and approximation as central costs. Representing entire densities requires more work than keeping a single guess; exact posteriors in continuous worlds are often intractable. Compact Gaussian approximations can help, but may be too crude. The chapter acknowledges remaining computational challenges without measuring runtime or memory. e-implications
The multimodal corridor belief makes premature commitment a plausible failure mode, but this chapter does not compare representation families experimentally. Likewise, coastal navigation depends on informative environmental structure. Neither illustration establishes robustness to a wrong map, persistent sensor bias, moving obstacles or all possible route geometries. e-beliefe-markov-figuree-coastal-controle-chapter-boundary
7.2 Questions for discussion
- How much low-probability belief should be preserved when sensing or the map can be wrong?
- When does the localization benefit of an informative detour outweigh its extra travel cost?
- Which belief approximations preserve the ambiguity needed for a useful control decision?
8. Reproducibility audit
8.1 Requirements and known gaps
Recreating the localization illustration would require explicit door positions, a state representation, sensor likelihoods and a motion-noise model. The chapter gives the qualitative observation–motion sequence but no numeric settings. A reader-chosen simulation can test the explanation; it cannot claim exact reproduction of the original curves. The caption defers Markov-localization details to Chapters 7 and 8. e-beliefe-markov-figuree-filtere-chapter-boundary
Testing the navigation argument would require a map, matched start/goal conditions, an estimator, sensor and actuator uncertainty, and a declared tradeoff between travel and localization. A proposed comparison should measure actual goal error as well as the robot's estimated arrival and route length. Figure 1.2 supplies neither those settings nor trial statistics; its caption points to Chapter 16 for the algorithm. e-coastal-figuree-coastal-controle-chapter-boundary
8.2 Proposed reproduction checks
The following checks are proposals motivated by the paper. They have not been run as part of this reading.
Check 1: Test whether retaining location alternatives helps recovery
Reader-proposed check, not performed: build a one-dimensional simulated corridor with three indistinguishable doors and explicitly document the chosen geometry, motion noise and door-sensor error rates. Replay identical observation and motion sequences through a full grid belief and a version that retains only its current dominant location hypothesis after each update. Include ambiguous observations and occasional erroneous detections. Compare localization error, recovery frequency and probability assigned to the true location over matched trials. The hypothesis is that preserving alternatives improves recovery after misleading evidence. No improvement under the tested conditions would count against that hypothesis. These settings would be reader choices, not recovered parameters for Figure 1.1. e-beliefe-markov-figuree-filtere-chapter-boundary
Check 2: Separate the information benefit of a detour from extra travel
Reader-proposed check, not performed: compare an open-space route and a corner-and-wall route in a simulated known map, holding start, goal, estimator, controller and noise settings fixed. Measure true terminal goal error, estimated terminal error, the rate of declaring arrival while outside a predeclared goal tolerance, and route length across matched trials. Repeat with boundary observations made uninformative while preserving collision geometry and the same routes. The information-gathering explanation predicts that the informative route's localization advantage should diminish in that control. Persistence would suggest another cause for the difference. This tests the chapter's reasoning; the original map data, settings and trial records are not supplied. e-coastal-figuree-coastal-controle-chapter-boundary
8.3 Reading coverage
Visual audit: All ten PDF pages were rendered at 200 DPI and actually viewed, covering the chapter heading, sections 1.1–1.6, both original figures and captions, and every PDF page supporting retained claims or proposed checks. Both final scientific crops were re-extracted at 300 DPI using the prescribed bounds and actually viewed at native output size. Figure 1.1's panel order, likelihood/belief labels and rightward motion were checked against the text on PDF pages 3 and 6; Figure 1.2's path labels, endpoints and credit were checked against its caption and PDF page 6. The figures and explanatory text support the qualitative readings given here. No quantitative table or ablation appears in this chapter. Later chapters, external media and identity-document images were not visually inspected.
PDF pages inspected for this edition: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. Appendix coverage: not present.
Original figures and tables remain the work of the source’s authors. Extractions preserve their scientific content; any HTML wrapper layout is disclosed with each figure. The surrounding reading notes are our own.
Text reading scope & known omissions
- Chapter 1 Introduction, complete supplied chapter: PDF pages 1–10, printed pages 3–12
- 1.1 Uncertainty in Robotics: PDF pages 1–2
- 1.2 Probabilistic Robotics: PDF pages 2–6, including Figures 1.1 and 1.2
- 1.3 Implications: PDF pages 7–8
- 1.4 Road Map: PDF page 8
- 1.5 Teaching Probabilistic Robotics: PDF page 9
- 1.6 Bibliographical Remarks: PDF pages 9–10
Outside the original text pass
- Only Chapter 1 Introduction, sections 1.1–1.6, printed pages 3–12 (10 PDF pages), is available in the supplied source. Chapters 2–17, appendices, front matter, full bibliography, and index are not supplied; this is not full-book availability or a completed reading.
- Text and figure captions are extracted, but figure images (including Figures 1.1 and 1.2), their plotted geometry, and page layout are not reconstructed in textPath. The retained PDF preserves them. No downstream report may claim visual figure inspection from this text.
- Poppler pdftotext -layout output is retained separately. Only ASCII control characters that arose as graphic debris were removed from the reading text; per-page counts are recorded in the cleaned-pages sidecar. No missing words or numerical claims were inferred.
- The PDF excerpt does not include the book title/copyright pages; title, authors, publication year and ISBN are verified through its official publisher link. Printing-level equivalence is not established.
- External figures/slides/errata linked from the author website were not used as chapter text.
- Only Chapter 1 is supplied. Chapters 2–17, appendices, front matter, full bibliography and index are unavailable in this reading. Complete reading of the ten-page sample is a selected-chapter partial report, not full-book coverage.
- PDF page 1 shows only the heading '1 Introduction' and no author byline. Book identity was established separately by reading both supplied identity texts and all seven original HTML fragments. The Stanford source https://robots.stanford.edu/probabilistic-robotics/ names Probabilistic Robotics in its h1 and Sebastian Thrun, Wolfram Burgard and Dieter Fox under 'About the Authors' (original UTF-8 byte ranges 4251–4282, 5533–5559 and 5560–6214).
- The PRH source https://www.penguinrandomhouse.com/books/655720/probabilistic-robotics-by-sebastian-thrun-wolfram-burgard-and-dieter-fox/9780262201629/ independently names the same book and authors (byte ranges 151203–151234 and 151347–151736). Its Product Details heading and contents (201071–201120 and 201539–202105) identify ISBN 9780262201629, The MIT Press, publication date 2005-08-19 and 672 pages. Those product facts are localized to PRH; the page count does not expand this report's coverage. These identity documents are not additional scientific sources. Their HTML was read as inert text; no identity PNG was viewed.
- The supplied acquisition associates the ISBN-bearing publisher sample with that book product. The PDF lacks title/copyright pages and a printing or revision statement. Sample printing remains unknown; equivalence to a particular printing or later edition cannot be established. No revision comparison or source substitution was performed. Nicholas Roy is credited for Figure 1.2, not as a book author.
- Extracted text includes captions but does not reconstruct figure geometry or page layout. Graphic control debris was removed from the localization figure's text extraction; missing words or values were not inferred. All ten original PDF pages and both final crops were separately visually inspected.
- External figures, slides and errata linked from the author website were not used as chapter text. Cited historical works, the full bibliography and linked software were not inspected. No paper code was executed and no experiments were reproduced.
The visual audit above records the subsequent illustrated pass.
8.4 Traceable evidence
e-chapter-boundaryPDF pages 1–10 (printed pages 3–12), Chapter 1, sections 1.1–1.6; heading on PDF page 1; roadmap on PDF page 8
The supplied artifact is the introductory chapter, headed '1 Introduction', with no author byline. It contains two figures, conceptual discussion, a roadmap, teaching advice and bibliographical remarks; no quantitative experiment table, ablation, explicit update equation, training recipe or appendix appears. Detailed methods are assigned to later chapters.
Go to primary source ↓e-uncertaintyPDF pages 1–2 (printed pages 3–4), section 1.1, paragraphs on environments, sensors, actuation, models and algorithmic approximations
Uncertainty comes from environmental unpredictability, sensor limitations/noise/failure, actuation, model incompleteness and approximations imposed by real-time operation. Its importance varies across engineered and open environments.
Go to primary source ↓e-beliefPDF page 3 (printed page 5), section 1.2, opening paragraph and global-localization example through the motion paragraph
Probability distributions represent ambiguity. A known environment has three indistinguishable doors; uniform initial belief becomes three equally plausible peaks after a door observation, with positive probability retained elsewhere. Motion shifts and spreads the belief.
Go to primary source ↓e-markov-figurePDF page 4 (printed page 6), Figure 1.1, panels (a)–(e), bel(x) and p(z|x) curves, and caption
The original graphic shows a flat initial belief, three observation-induced peaks, their rightward shift and broadening, a dominant peak after another observation, and further motion. Red curves are labeled p(z|x); black curves bel(x). The caption identifies Markov localization and refers to Chapters 7 and 8.
Go to primary source ↓e-filterPDF page 6 (printed page 8), section 1.2, first two paragraphs, continuation of Figure 1.1 explanation
A second door observation concentrates probability near one door and later motion carries the belief onward. The text names Bayes filtering for posterior estimation over robot locations and describes density updates as gaining information through sensing or losing it through uncertain processes.
Go to primary source ↓e-coastal-figurePDF page 5 (printed page 7), Figure 1.2(a)–(b), Start/Goal and Measured Path/True Path labels, and caption
Two map panels contrast visibly diverging paths in open space with closer paths near known obstacles. The caption calls these coastal-navigation algorithm results, points to Chapter 16, and credits the images to Nicholas Roy, MIT. No scale, numeric error, success rate or trial count is provided.
Go to primary source ↓e-coastal-controlPDF page 6 (printed page 8), section 1.2, last two paragraphs on coastal navigation and active information gathering
The map depicts an actual building. A feature-poor route accumulates uncertainty; an alternative seeks a distinctive corner and hugs a wall. The authors describe the longer route as improving the chance of actual goal arrival through active information gathering, separating probabilistic planning from the control that realizes plans.
Go to primary source ↓e-implicationsPDF page 7 and opening continuation on PDF page 8 (printed pages 9–10), section 1.3
The authors claim robustness benefits from combining imperfect models and sensors, describe learning as long-term estimation, and acknowledge computational complexity and the need for approximation. Continuous-state posterior inference can be intractable and Gaussian approximations may be inadequate. Computational challenges remain.
Go to primary source ↓e-roadmapPDF page 8 (printed page 10), section 1.4, chapter-group bullets and closing reading advice
The roadmap assigns mathematical foundations to Chapters 2–4, robot models to 5–6, localization to 7–8, mapping to 9–13, and probabilistic planning/control to 14–17, ending with exploration. These later treatments are pointers, not material present in the sample.
Go to primary source ↓e-teachingPDF page 9 (printed page 11), section 1.5
The authors recommend teaching localization early, revisiting mathematical material as needed, introducing planning/control early and using hands-on implementation. Mentioned website teaching materials are external to the chapter.
Go to primary source ↓e-historyPDF pages 9–10 (printed pages 11–12), section 1.6, model-based, behavior-based, hybrid and probabilistic-paradigm discussion
The historical account contrasts accurate-model assumptions with immediate sensor-driven behavior, notes hybrid architectures, and describes probability as the means of combining incomplete models and incomplete measurements. It cites earlier research and deployments without reproducing their experimental evidence.
Go to primary source ↓8.5 Primary sources
1 Introduction ↗
PDF · 3,585 extracted words
Source fingerprint
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