Thesis

Path Planning And Collision Avoidance for Clustered Central Place Foraging

Central place foraging algorithms for multiple robots are gaining attention due to their performance and efficiency in various applications like planetary surveys, mining, object transportation and manipulation. In foraging tasks, multiple robots search for resources and deposit the collected resources to a particular location called “nest” or “home”. If the resources are deposited at a central single collection point, it becomes a central place foraging task. The performance of central place foraging approaches is reduced due to reactive interrobot collision avoidance. The performance decreases in two cases, first case is when two or more robots collect the resources from the same cluster and go to the central location for deposition and the second case is when the path of one robot going to nest from its search position or vice versa intersects with the path of another robot searching for resources. The approach proposed in this thesis is called Path Planning And Collision Avoidance Algorithm For Clustered Central Place Foraging (PPCA-CCPFA). PPCA-CCPFA concentrates on improving the performance of central place foraging task in terms of reducing the number of inter robot collisions and improving target collection in given time for clustered resource distributions. We compare our approach to the popular Distributed Deterministic Spiral Algorithm (DDSA). The proposed algorithm detects inter robot collision and finds an alternate collision free path for a robot in case 1 and adds a delay time for a robot in case 2. This approach has shown notable increase in the performance of DDSA with a single 8 x 8 resource cluster. This algorithm is tested on a single cluster resource distribution at random locations in the arena for a swarm size of 3 to 15 robots.

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