35 5 2013 9 ROBOT Vol.35, No.5 Sep., 2013 DOI 10.3724/SP.J.1218.2013.00527 360 510642 bug 360 Pioneer3-AT bug 360 TP249 A 1002-0446(2013)-05-0527-08 Smooth Obstacle-avoidance by 4-wheel Navigational Vehicle Using Non-360 Range Sensors TAN Zhibin ZHAO Zuoxi ZHAO Ruqi LI Jiaojiao YU Long (Key Laboratory of Key Technology on Agricultural Machine and Equipment of South China Agricultural University, College of Engineering, South China Agricultural University, Guangzhou 510642, China) Abstract: An obstacle-avoidance navigation algorithm based on bug algorithm is proposed for the vehicle using non-360 range sensors and having minimum turning radius. Then the vehicle can bypass obstacle edge along a continuous and smooth path and complete the fixed-goal navigation task. This algorithm uses virtual antenna to intuitively represent passable state in the front. The tentacle algorithm is used to analyze turning state, and the minimum turning radius of the vehicle is changed according to the surrounding environment to correct the turning route in real time. The test result on a Pioneer 3-AT wheeled robot show that the robot using the proposed algorithm can reach the fixed goal in an unknown environment without collision, and the trajectory is continuous and smooth. The algorithm solve the problem that bug algorithm cannot be used in this kind of vehicles. Keywords: smooth trajectory; non-360 range; obstacle avoidance; virtual antenna; tentacle 1 Introduction [1] [2] bug [3] [4-10] 360 bug 360 180 bug 360 360 360 bug [11] bug 45 [12] 180 6117508120114404110003 948 2011-G32 zhao zuoxi@hotmail.com 2012-12-06/2013-03-25/2013-08-03
528 2013 9 360 bug [13] bug bug bug bug Pioneer3-AT 2 Design of virtual antennas [13] 1 D r L r W r O 1 180 360 R max D recb L W b D recs L W s W b > W s D recs W s = W r W s W r D recb D recs W b W s L 3 Turning route selection mechanism bug von Hundelshausen [14] [15] [16] 3.1 2 3.3 4.2 3 45 6 7 8910 11 2 12 1 13 D recs R max D recb W s W b L O L r 2 W r Fig.2 Vehicle and tentacles distribution 1 Fig.1 Design of virtual antennas [14]
35 5 360 529 k r k ρ k R min, k = 0,,(n 3)/2 r k =, k = (n 1)/2 (1) ρ k 7 R min, k = (n + 1)/2,,n 1 R min ρ ρ ρ [14] ρ ρ ρ n k 0,1,,n 1 n (n 1)/2 (n 1)/2 (n 1)/2 R min 1/4 n = 13 ρ = 1.3 R min = 500 mm 2 3.2 DIN70000 3 O O 1 O θ O 1 O Y x k = r k cosθ L arc = r k θ πr (2) y k = r k sinθ + r k 0 2 L arc θ α k O O θ Y O 3 Fig.3 Simulated body position on tentacle diagram X 2 3 α k = θ P k = [x k,y k,α k ] T 3.3 P kt = [x kt,y kt,α kt ] T k t 1) 7 0,,6 4 H A T kt k H t I 0 B A 1 2 34 5 a H 6 A C A 23 1 4 4 5 0 t 1 2 3 b 5 6 0 6 (a) (b) (c) Fig.4 4 H Cases of the vehicle moving along different tentacles 2) 0 T 00 T 0a 7(a) T 0a D recb T 0a D recb B D r T 0a I[0] T 0a 3) 2) 1 T 10 T 1t 7(b) D recb D r I[1] T 1t H
530 2013 9 4) 2 T 20 T 2t 4(c) T 2b D r T 2b D r C I[2] 5) I I 6) 4 0 4 Implementation principle of the improved algorithm S T X O d(x,t ) X T d(x,o) X O H L L 4.1 bug bug 2 360 360 bug bug 180 3 5 5 3 6 bug T D scb D scs D scs Fig.5 5 R ng D scb D scs Bar Bar r R max bug Design of the original bug algorithm s virtual antennas x /mm Fig.6 8000 6000 4000 2000 0 4000 6 2000 0 2000 4000 y /mm bug Experimental result of the original bug algorithm D scb D scs T 4.2 bug bug bug i) T ii) S T T 3 1 Tab.1 1 Differences between the new and original algorithms 0 3 5
35 5 360 531 4.2.1 T D recs i) T ii) D recs H 4.2.2 1) i) D recs ii) g m g g = d(x,o)/2 (3) 2) S (1) S T T T φ T i) T ii) D recs H (2) H L i) ii) D recs iii) D recs i) D recb ii) T (3) i) g ii) D recs i) D recs ii) T 4.2.3 bug D recs T i) D recs ii) D recs d(x,t ) < d(x,o) 4.3 7 S T D recs I H D D recb J (6) E T F L T SHDEFLT SH,DE,LT HD EF,FL
532 2013 9 9 180 90 90 S LMS200 H MobileRobots C++ D E ARIA C++ ARIA I J F L T 7 Fig.7 Trajectory simulation of bypassing obstacle T S 8 Fig.8 Unreachable goal situation 4.4 8 5 Test on a mobile robot and its result analysis Pioneer3-AT Pioneer3-AT MobileRobots 9 Pioneer3-AT Fig.9 Pioneer3-AT mobile robot platform [17] 10 m 7 m 10 L r =580 mm W r =493 mm R min =500 mm R max = 5000 mm L = 700 mm W b = 780 mm W s = 600 mm n = 13 ρ = 1.3 Matlab 10 Fig.10 Test environment
35 5 360 533 11 12 bug T S T (0,0) (8000,0) (H 1 H 2 ) (L 1 L 2 ) bug (H 1H 2 ) (L 1L 2 ) 11 12 2 bug 9000 7000 T Tab.1 2 Comparison of obstacle-avoidance results x /mm 5000 3000 L 2 H 2 L 1 /mm (7934, 28) (7960,20) /mm (8300, 50) (8380,100) /mm 8 627 10 218 Fig.11 x /mm Fig.12 1000 1000 5000 9000 7000 5000 3000 1000 11 1000 5000 12 H 1 S 0 y /mm 5000 Test result of goal navigation with obstacle avoidance using the proposed algorithm L 2 H 1 T S H 2 L 1 0 y /mm bug 5000 Test result of goal navigation with obstacle avoidance using the broken-line bug algorithm H 1 L 2 6 Conclusion (1) bug 180 (2) (3) [17] References [1] [J]2002 19(2) 117-121. Wang R B, Li B, Chu J W, et al. A review of the safety guarantee technology of worldwide intelligent vehicle[j]. Journal of Highway and Transportation Research and Development, 2002, 19(2): 117-121. [2] [J]2005 27(4) 319-324. Meng J H, Zhu J H, Sun Z Q. A new path planning algorithm for sensor-based mobile robot in unknown environment[j]. Robot, 2005, 27(4): 319-324. [3] Lumelsky V J, Stepanov A A. Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape[j]. Algorithmica (New York), 1987, 2(4): 403-430. [4] Sankaranarayanan A, Vidyasagar M. A new path planning algorithm for moving a point object amidst unknown obstacles in a plane[c]//ieee International Conference on Robotics and Automation. Piscataway, USA: IEEE, 1990: 1930-1936.
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