Sensor Based-Robotics: An Autonomous Observer Rafael Murrieta-Cid - PowerPoint PPT Presentation

Sensor Based-Robotics: An Autonomous Observer Rafael Murrieta-Cid ITESM – Estado de M´ exico Campus, Mechatronics Center MOVIE 2005 – p.1/42

� � � � � � � � � Researchers involved in this work Professor Seth Hutchinson, U. of Illinois. Professor Jean Claude Latombe, Stanford U. Professor Steven LaValle, U. of Illinois. Dr. Hector Gonzalez, Honda Fundamental Research Labs. Dr. Alejandro Sarmiento, Intel. Sourabh Bhattacharya, Master Student U. of Illinois. Claudia Esteves PhD Student LAAS/CNRS. Teja Muppirala, Master Student U. of Illinois. Benjamin Tovar, PhD Student U. of Illinois. MOVIE 2005 – p.2/42

� Problem Definition Our ideas are centered on the development of mobile robotic systems that perform sophisticated visibility-based tasks. Map Building Optimal Navigation Target Finding and Tracking MOVIE 2005 – p.3/42

� � � Planning Exploration Strategies for SLAM A mobile sensor (the observer) must define a motion strategy to efficiently build a map of an indoor environment. We have developed a randomized motion planner that selects the next best view from a set based on maximizing a utility function. The final result of the exploration is a multi-representational map consisting of polygons, landmarks and a road map. MOVIE 2005 – p.4/42

✒ ✄ ✒ ✪ ✖ ✗ ✓ ✍ ✩ ✏ ✣ ✁ ✤ ✥ ✂ ✦ ✧ ✑ ✏ ✄ ✡ ✮ ✭ ✆ ✬ ✠ ✞ ✄ ✫ ✍ ✆ ☛ ☞ ☛ ✌ ✍ ✎ ★ Planning Exploration Strategies for SLAM e ✝✟✞ ✂☎✄ ✛✢✜ e ✘✚✙ ✓✕✔ Length of the closest free edge s Distance from the robot to the next possible position Distance from the next possible position to the closest free edge Orientation change to reach the next robot’s configuration Cumulative uncertainty Object identification probability Number of landmarks inside a visibility region Number of corners inside a visibility region ✯✱✰ A function that penalizes configurations that like near an obstacle. ✲✴✳ ✵✷✶ ✸✺✹ Minimum distance from a full edge MOVIE 2005 – p.5/42 Table 1:

Planning Exploration Strategies for SLAM MOVIE 2005 – p.6/42

✤ ❃ ✾ ❃ � ✼ ✾ ✻ ✦ ✦ ❄ ✼ ✤ ✻ ✾ ✼ ✦ ✁ ❅ ❆ ✦ ✾ ❀ ✤ ❍ ✻ ❑ ✼ ❍ ● ❆ ✽ ❋ ✻ ✼ ✦ ✁ ❀ ❁ ❂ ✤ ❃ ✤ ❇ Planning Exploration Strategies for SLAM Given two sets of points and , the Hausdorff distance is used to find the matching and update robot localization ✻✿✾ ❈❊❉ ✗❏■ MOVIE 2005 – p.7/42

Planning Exploration Strategies for SLAM (a) (b) (a) Laser data (b) Model matching MOVIE 2005 – p.8/42

Experiments Omnidirectional and infinite range sensor Fxperiments in Real Robot 180 degrees field of view and limited range MOVIE 2005 – p.9/42

� � � Planning Exploration Strategies for SLAM The crux of our method is a sampling-based motion planner algorithm that, given a partial map of the environment, selects where to move the robot next. We balance the desire to see as much of the as-yet-unseen environment as possible, while at the same time having enough overlap and landmark information with the scanned part of the building to guarantee good registration and robot localization. Visibility is used to bias the sampling generation. MOVIE 2005 – p.10/42

� � Planning Exploration Strategies for SLAM Benjamín Tovar, Rafael Murrieta-Cid, Claudia Esteves, Robot Motion Planning for Map Building. in proc IEEE/RSJ International Conference on Intelligent Robots and Systems 2002 . Benjamin Tovar, Rafael Murrieta-Cid, Claudia Esteves, Robot Motion Planning for Model Building Under Perception Constraints. in proc 9th International Symposium on Intelligent Robotic System 2001 . MOVIE 2005 – p.11/42

� � � Optimal Navigation We propose a sensor feedback motion strategy for robot navigation. We developed a data structure and algorithm that captures the topology of the environment and enables a robot to navigate optimally. This data structure is a dynamic tree that encodes enough information to generate optimal paths, although only information of gap critical events is used. MOVIE 2005 – p.12/42

Optimal Navigation The robot view of the environment MOVIE 2005 – p.13/42

Optimal Navigation Reduced visibility graph Visibility tree at point q T(q) Visibility region Measurement of the gap sensor MOVIE 2005 – p.14/42

Optimal Navigation Learning Tg MOVIE 2005 – p.15/42

Optimal Navigation Experiments with real robots Experiments MOVIE 2005 – p.16/42

� � Optimal Navigation We have presented a data structure and algorithm that captures the topology of the environment and enables a robot to navigate optimally. We want to study what other capabilities should be added to the robot to relax the requirements of omnidirectional, unbounded-range sensing. MOVIE 2005 – p.17/42

� � Optimal Navigation Benjamín Tovar, Steven M. LaValle and Rafael Murrieta-Cid, Optimal Navigation and Object Finding without Geometric Maps or Localization. in IEEE proc International Conference on Robotics and Automation 2003 . Benjamín Tovar, Steven M. LaValle and Rafael Murrieta-Cid, Locally-optimal Navigation in Multiply-connected Environments without Geometric Maps. in proc IEEE/RSJ International Conference on Intelligent Robots and Systems 2003 . MOVIE 2005 – p.18/42

� � � Optimal Navigation II Problem Definition: Path Planning for a Differential Robot (Minimal Length Paths). A mobile robot navigates in an obstacle-free workspace while maintaining view of a fixed landmark The robot has sensing constraints namely, limited range and angle of view Our goal is to find the path that is optimal in sense of distance between a given start and a goal position MOVIE 2005 – p.19/42

Optimal Navigation II Y x b φ ψ y b θ X r=r min r=r max MOVIE 2005 – p.20/42

Optimal Navigation II G V T 2 T 2 T 1 P G P C T 1 B P A II P T 1 Q Q II’ T 2 I’ P I D P’’ T2 P’’ III’ P’ T1 P’ IV’ T 1 G T1 P’’ III IV G T2 P’ VI MOVIE 2005 – p.21/42

� � � � Optimal Navigation II Conclusions Formulation of the problem of tracking a static target with sensing constraints Proposed a constructive proof for the controllability of the system Proposed the nature of optimal paths Presented the partition of the workspace based on the nature of the optimal paths MOVIE 2005 – p.22/42

� � � Optimal Navigation II Sourabh Bhattacharya, Rafael Murrieta-Cid and Seth Hutchinson, Path Planning for a Differential Drive Robot: Minimal Length Paths-A Geometric Approach, in proc IEEE/RSJ Conference on Intelligent Robots and Systems 2004 . Proposed Research Directions Determine the optimal paths in the sense of time Investigate the case of a robot with area and mass. MOVIE 2005 – p.23/42

◆ � � � ✻ ✣ ▲ ✁ ▼ Generating Expected-Time Efficient Trajectories for Rapidly Finding an Object Problem Definition Use one or more mobile robots to find an object as quickly as possible on average. robots with omni-directional, infinite range sensors moving in a known environment. Robots start moving from an initial position at along trajectories . MOVIE 2005 – p.24/42

MOVIE 2005 – p.25/42 ❝ ❛ ❧ ❘ ❘ � ❯ ❱ ❲ ❳ ❭ ❚ ❝ ❦ ❤ ♦ ♥ ❤ ❣ ❣ ❛ � ♠ ❛ ❣ ❝ ❣ ❝ ❤ ❣ ❡ ❞ � � ❡ ❡ ❝ ❤ ❙ ❝ ❭ ❣ ❳ ❝ ❞ ❲ ♠ ❦ ❱ ❤ ❯ � ❘ ❝ ❧ ❘ � ❣ Object Finding ♦❢❡ Expected Value vs. Worst Case: ❤❜❛ ❤❥✐ ❤❥✐ ❖◗❚ ❖◗❙ ❞❢❡ ❞❢❡ ❵❜❛ Worst case time ❵❜❛ Worst case time ❖◗❙ ❖◗❚ ❪❴❫ ❪❴❫ ❖◗P ❖◗P ❨❬❩ ❨❬❩ Route 1: Route 2:

♣ r � ✦ � ✓ � ♣ ✻ ✤ ✤ ✓ ✦ q Object Finding Different Versions of the Problem: Sensing at specific locations Polygonal environment If the object PDF is uniform, MOVIE 2005 – p.26/42

❳ ③ ❪ ❝ ✇ ❶ ❤ ✸ P ⑥ ⑤ ❪ ❛ ❵④ ③ ❳ ❲ ❱ ❯ ② ❷ � � Object Finding Different Versions of the Problem: Continuous sensing Polygonal environment Robot senses the environment as it moves ✈①✇ ✈①✇ ✲⑩⑨ s✉t s✉t ❪⑧⑦ MOVIE 2005 – p.27/42

� � � Object Finding Different Versions of the Problem: Simulation of a 3-D environment Robot has finite volume Robot is a redundant mobile manipulator MOVIE 2005 – p.28/42

� � � � Object Finding Two-Layered Approach Partition the environment into regions bounded by critical curves Find an ordering of visiting these regions Showed the discrete problem to be NP-hard by reduction Solve each region independently and concatenate the resulting sub-paths MOVIE 2005 – p.29/42