Motion Planning for Multiple Autonomous Vehicles Genetic Algorithm
- Slides: 26
Motion Planning for Multiple Autonomous Vehicles Genetic Algorithm Presentation of the paper: R. Kala, K. Warwick (2014) Heuristic based evolution for the coordination of autonomous vehicles in the absence of speed lanes, Applied Soft Computing, 19: 387– 402. April, 2013 Rahul Kala School of Systems, Engineering, University of Reading rkala. 99 k. org
Key Contributions • The design of a GA which gives results within low computational times for traffic scenarios. • Employment of the developed GA for constant path adaptation to overcome actuation uncertainties. The GA assesses the current scenario and takes the best measures for rapid trajectory generation. • The use of traffic rules as heuristics to coordinate between vehicles. • The use of heuristics for constant adaptation of the plan to favour overtaking, once initiated, but to cancel it whenever infeasible. • The approach is tested for a number of diverse behaviours including obstacle avoidance, blockage, overtaking and vehicle following. Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Why GA? • Optimality • Probabilistic Completeness • Iterative Concerns • Computational Cost • Cooperative Coordination Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Key Concepts • Use Road Coordinate Axis system • Optimize as the vehicle moves: – Tune plan – Overcome uncertainties – Compute feasibility of overtake • Integration with route planning – Next road/segment becomes the goal as the vehicle is about to complete the previous Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Map For each vehicle entered in scenario and not reached goal Overall Algorithm Planning by Dijkstra’s Algorithm if coarser path is not built Finer Planning by Bezier Curves Database of all Vehicle Trajectories Blockage? Yes Genetic Algorithm Optimization No Operational Mode Path Following Overtaking Vehicle Following Steering and Speed Control Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
GA Optimization Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Individual Representation Genotype Y’ X’ Y’ Mapping Control points Source Directional maintenance points Phenotype Goal Trajectory The genotype (optimized by GA) stores all control points of the spline curve Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Genetic Operators Repair Insert Crossover Mutation • Sorts points in X’ axis (vehicle always drives forward) • Deletes points behind the crossed position • Deletes excess control points till trajectory gets better • Add random individuals • For variable length chromosome • Randomly deviate points Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Fitness Function Contributors • Length of trajectory in without safety distance • Length in infeasible region Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Checking Granularity Points of checking Finer at start Trajectory Coarser at the end Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Coordination • Priority based coordination • Only vehicles ahead considered • Cooperation added by traffic heuristics – Overtake – Vehicle Following • Vehicle can request another vehicle to – Slow down – Turn right/left Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Determination of speed Path Optimization: GA Speed Optimization Genetic Algorithm Motion Planning for Multiple Autonomous Vehicles Alternating optimization of path and speed Increase by δ if feasible Decrease by δ if infeasible rkala. 99 k. org
Traffic Heuristics • Two heuristics used: Overtaking and Vehicle Following • Imparts cooperation to an else non-cooperative coordination Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Traffic Heuristics Give initial turns to the other vehicles to best overtake Overtaking Alter speeds of the other vehicles to best overtake Cancel overtake if it seems dangerous Assess Situation Give initial turns to the other vehicles to best overtake Vehicle Following Alter speeds of the other vehicles to best overtake Initiate overtake if it seems possible Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Overtaking Move Left R 1 R 2 R 3 Move Left R 2 R 3 R 1 R 2 R 1 R 3 Motion Planning for Multiple Autonomous Vehicles R 3 R 1 R 3 rkala. 99 k. org
Overtaking Too close, R 3 slows Too close, R 2 slows R 2 R 1 R 3 Not possible, abandon R 2 R 1 R 2 R 3 Motion Planning for Multiple Autonomous Vehicles R 1 R 3 rkala. 99 k. org
Vehicle Following R 1 R 2 Move Left R 2 R 3 Move Left Infeasible, slow down R 1 Feasible, speed up R 2 R 1 R 3 Infeasible, slow down R 1 R 2 R 3 R 2 Move Left R 3 R 1 R 2 R 3 Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Results Vehicle position at the time of blockage Blockage Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
b Results - 2 vehicle Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Results - Overtaking Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Results – Vehicle Following Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Distance Analysis 960 940 920 900 880 860 840 820 800 780 760 6 11 16 Motion Planning for Multiple Autonomous Vehicles 21 Speed 26 31 36 rkala. 99 k. org
Time Analysis 160 140 120 100 80 60 40 20 0 6 11 16 Motion Planning for Multiple Autonomous Vehicles 21 26 Speed 31 36 rkala. 99 k. org
Analysis 900 898 Distance 896 894 892 890 888 886 884 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Number of Individuals Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org
Analysis Minimum Individuals for Feasible Solution 160 Road Coordinate Axis System 140 Cartesian Coordinate Axis System 120 100 80 60 40 20 0 0 1 2 3 4 5 Number of Obstacles Motion Planning for Multiple Autonomous Vehicles 6 7 8 rkala. 99 k. org
• Acknowledgements: • Commonwealth Scholarship Commission in the United Kingdom • British Council Thank You Motion Planning for Multiple Autonomous Vehicles rkala. 99 k. org